Awesome-AI-Memory

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👋 Introduction

Large Language Models (LLMs) have rapidly evolved into powerful general-purpose reasoning and generation engines. Nevertheless, despite their continuously advancing capabilities, LLMs remain fundamentally constrained by a critical limitation: the finite length of their context window. This constraint defines the scope of information directly accessible during a single inference process, endowing models with only short-term memory capabilities. Consequently, they struggle to support extended conversations, personalized interactions, continuous learning, and complex multi-stage tasks.

To transcend the inherent limitations of context windows, AI memory and memory systems for LLMs have emerged as a vital and active research and engineering frontier. By introducing external, persistent, and controllable memory structures beyond model parameters, these systems enable large models to store, retrieve, compress, and manage historical information during generation processes. This capability allows models to continuously leverage long-term experiences within limited context windows, achieving cross-session consistency and continuous reasoning abilities.

Awesome-AI-Memory is a comprehensive repository dedicated to AI memory and memory systems for large language models, systematically curating relevant research papers, framework tools, and practical implementations. This repository endeavors to map the rapidly evolving research landscape in LLM memory systems, bridging multiple disciplines including natural language processing, information retrieval, intelligent agent systems, and cognitive science.

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🎯 Goal of Repository

Our mission is to establish a centralized, continuously evolving knowledge base that serves as a valuable reference for researchers and practitioners, ultimately accelerating the development of intelligent systems capable of long-term memory retention, sustained reasoning, and adaptive evolution over time.

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📏 Project Scope

This repository focuses on memory mechanisms and system designs that extend or augment the context window capabilities of large language models, rather than merely addressing model pre-training or general knowledge learning. The content encompasses both theoretical research and engineering practices.

🌀 Included Content (In Scope)

• Memory and memory system designs for large language models
• External explicit memory beyond model parameters
• Short-term memory, long-term memory, episodic memory, and semantic memory
• Retrieval-Augmented Generation (RAG) as a memory access mechanism
• Memory management strategies (writing, updating, forgetting, compression)
• Memory systems in intelligent agents (Agents)
• Shared and collaborative memory in multi-agent systems
• Memory models inspired by cognitive science and biological memory
• Evaluation methods, benchmarks, and datasets related to LLM memory
• Open-source frameworks and tools for memory-enhanced LLMs

🌀 Excluded Content (Out of Scope)

• General model pre-training or scaling research without direct memory relevance
• Purely parameterized knowledge learning without memory interaction
• Traditional databases or information retrieval systems unrelated to LLMs
• Generic memory systems outside the LLM context (unless demonstrating direct transfer value)

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🔔 Recent hot research and news

• 2026-07-06 - 🎉 Updated 25 papers, including 4 on Datasets & Benchmark, and 23 on Framework & Methods
• 2026-06-14 - 🎉 Updated 24 papers, including 2 on Survey, 4 on Systems & Models, 2 on Datasets & Benchmark, and 16 on Framework & Methods
• 2026-06-06 - 🎉 Updated 45 papers, including 1 on Survey, 6 on Systems & Models, 12 on Datasets & Benchmark, and 26 on Framework & Methods
• 2026-05-10 - 🎉 Updated 16 papers, including 3 on systems and models, 1 on benchmarks, and 12 on methods; also added 1 new project under systems and open sources
• 2026-05-06 - 🎉 Updated 16 papers, including 2 on systems and models, 2 on benchmarks, and 12 on methods
• 2026-04-27 - 🎉 Updated 15 papers, including 2 on survey, 3 on systems and models, and 10 on methods
• 2026-04-17 - 🎉 Updated 46 papers, including 1 on survey, 5 on systems and models, 3 on benchmarks, and 37 on methods
• 2026-04-07 - 🎉 Updated 16 papers, including 15 on methods, and 1 on benchmarks
• 2026-03-15 - 🎉 Updated 14 papers, including 14 on methods
• 2026-03-08 - 🎉 Updated 15 papers, including 3 on survey, 2 on systems and models, 5 on benchmarks, and 5 on methods
• 2026-03-02 - 🎉 Add a new code agent to this repo
• 2026-02-27 - 🎉 Updated 20 papers, including 1 on survey, 2 on systems and models, 2 on benchmarks, and 15 on methods
• 2026-02-26 - 🎉 Updated 14 papers, including 14 on methods
• 2026-02-14 - 🎉 Updated 15 papers, including 1 on survey, 12 on methods, 1 on benchmarks, and 1 on systems and models
• 2026-02-09 - 🎉 Updated 15 papers
• 2026-02-01 - 🎉 Updated 16 papers, including 9 on methods, 4 on benchmarks, and 3 on systems and models
• 2025-12-24 – 🎉 Release Repository V(1.0)
• 2025-12-10 – 🎉 Initial Repo

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🗺️ Table of Contents

• Awesome-AI-Memory
  • 👋 Introduction
  • 🎯 Goal of Repository
  • 📏 Project Scope
  • 🔔 Recent hot research and news
  • 🧠 Core Concepts
  • 📚 Paper List
  • 🧰 Resources
    • 📊 Benchmarks and Tasks
    • 💻 Systems and Open Sources
    • 🎥 Multi-media resource
    • 🧠 Adam Framework
  • 🤝 Make a Contribution
  • 💬 Community & Support
  • 🌟 Star Trends

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🧠 Core Concepts

• LLM Memory: A fusion of implicit knowledge encoded within parameters (acquired during training) and explicit storage outside parameters (retrieved at runtime), enabling models to transcend token limitations and possess human-like abilities to "remember the past, understand the present, and predict the future."

• Memory System: The complete technical stack implementing memory functionality for large language models, comprising four core components:

  • Memory Storage Layer: Vector databases (e.g., Chroma, Weaviate), graph databases, or hybrid storage solutions
  • Memory Processing Layer: Embedding models, summarization generators, and memory segmenters
  • Memory Retrieval Layer: Multi-stage retrievers, reranking modules, and context injectors
  • Memory Control Layer: Memory prioritization managers, forgetting controllers, and consistency coordinators

• Memory Operations: Atomic memory operations executed through tool calling in memory systems:

  • Writing: Converting dialogue content into vectors for storage, often combined with summarization to reduce noise
  • Retrieval: Generating queries based on current context to obtain Top-K relevant memories
  • Updating: Finding relevant memories via vector similarity and replacing or enhancing them
  • Deletion: Removing specific memories based on user instructions or automatic policies (e.g., privacy expiration)
  • Compression: Merging multiple related memories into summaries to free storage space

• Memory Management: The methodology for managing memories within memory systems, including:

  • Memory Lifecycle: End-to-end management from creation, active usage, infrequent access, to archiving/deletion
  • Conflict Resolution: Arbitration mechanisms for contradictory information (e.g., timestamp priority, source credibility weighting)
  • Resource Budgeting: Allocating memory quotas to different users/tasks to prevent resource abuse
  • Security Governance: Automatic detection and de-identification of PII (Personally Identifiable Information)

• Memory Classification: A multi-dimensional classification system unique to memory systems:

  • By Access Frequency: Working memory (current tasks), frequent memory (personal preferences), archived memory (historical records)
  • By Structured Degree: Structured memory (database records), semi-structured memory (dialogue summaries), unstructured memory (raw conversations)
  • By Sharing Scope: Personal memory (single user), team memory (collaborative spaces), public memory (shared knowledge bases)
  • By Temporal Validity: Permanent memory (core facts), temporary memory (conversation context), time-sensitive memory (e.g., "user is in a bad mood today")

• Memory Mechanisms: Core technical components enabling memory system functionality:

  • Retrieval-Augmented Generation (RAG): Enhancing generation by retrieving relevant information from knowledge bases
  • Memory Reflection Loop: Models periodically "review" conversation history to generate high-level summaries
  • Memory Routing: Automatically selecting retrieval sources based on query type (personal memory/public knowledge base)

• Explicit Memory: Memory stored as raw text outside the model, implemented through vector databases with hybrid indexing strategies:

  • Dense Vector Indexing: Handling semantic similarity queries
  • Sparse Keyword Indexing: Processing exact match queries
  • Multi-vector Indexing: Segmenting long documents into multiple parts, each independently indexed

• Parametric Memory: Knowledge and capabilities stored within the fixed weights of a language model's architecture, characterized by:

  • Serving as the model's core long-term semantic memory carrier
  • Being activatable without external retrieval or explicit contextual support
  • Providing the foundational capability for zero-shot reasoning, general responses, and language generation

• Long-Term Memory: Key information designed for persistent storage, typically implemented as external knowledge bases with capabilities including:

  • Automatic Summarization: Distilling multi-turn dialogues into structured memory
  • Context Binding: Recording memory context to prevent erroneous generalization
  • Multimodal Storage: Simultaneously preserving text, images, audio, and other multimodal memories

• Short-Term Memory: Active information within the LLM's context window, constrained by attention mechanisms. Key techniques include:

  • KV Cache Management: Reusing key-value caches to reduce redundant computation
  • Context Compression: Using summaries instead of detailed history (e.g., "the previous 5 dialogue rounds discussed project budget")
  • Sliding Window Attention: Focusing only on the most recent N tokens while preserving special markers
  • Memory Summary Injection: Dynamically inserting summaries of long-term memory into short-term context

• Episodic Memory: Memory type recording specific user interaction history, fundamental to personalized AI:

  • User Identity Recognition: Identifying the same user across sessions
  • Interaction Trajectory Recording: Preserving user decision paths and feedback
  • Emotional State Tracking: Recording patterns of user mood changes
  • Preference Evolution Modeling: Capturing long-term changes in user interests

• Memory Forgetting: Deliberately designed forgetting mechanisms in large models, including:

  • Selective Forgetting (Machine Unlearning): Removing the influence of specific information from training data, such as covering specific knowledge with forgetting layers
  • Privacy-Driven Forgetting: Automatically identifying and deleting PII information, or setting automatic expiration
  • Memory Decay: Automatically lowering the priority of infrequently accessed memories based on usage frequency
  • Conflict-Driven Forgetting: Strategically updating or discarding old memories when new evidence conflicts with them

• Memory Retrieval: The complex process of precisely locating relevant information from massive memory repositories:

  • Semantic Pre-filtering: Vector similarity matching to obtain Top-100 candidates
  • Contextual Reranking: Reordering results based on current query context
  • Temporal Filtering: Prioritizing the most recent relevant information

• Memory Compression: A collection of techniques maximizing memory utility under limited resources:

  • Content-level Compression: Extracting core information while discarding redundant details
  • Representation-level Compression: Vector quantization (e.g., PQ coding), dimensionality reduction
  • Organization-level Compression: Clustering similar memories, building hierarchical memory structures
  • Knowledge Distillation: Transferring key patterns from external memory into parametric memory

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📚 Paper List

Papers below are ordered by publication date:

Survey

Date	Paper & Summary	Tags	Links
2026-06-25	Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents
	• This survey frames privacy in LLM agents from a data-centric perspective, focusing on the data an agent queries, stores, remembers, exchanges, and acts upon rather than only on attack categories. • It organizes risks across databases, document collections, APIs, cross-session memory, intermediate workflow state, and multi-agent communication, showing how sensitive information can leak before the final response is produced. • The paper highlights that agent privacy requires lifecycle-level controls over memory writes, retrieval, delegation, logging, and tool use, making persistent state and data governance central to safe agent deployment.
2026-06-23	Are We Ready For An Agent-Native Memory System?
	• This paper studies agent memory from a data-management perspective, arguing that memory for LLM agents should be evaluated as a system supporting persistent storage, retrieval, update, consolidation, and lifecycle governance rather than as a monolithic retrieval add-on. • It decomposes an agent-native memory system into four modules: representation and storage, extraction, retrieval and routing, and maintenance. Under this framework, the authors evaluate 12 representative memory systems and two baselines across five benchmark workloads spanning 11 datasets. • The experiments show that no single memory architecture dominates across all settings; effectiveness depends on the alignment between memory structure and workload bottlenecks. Fine-grained ablations quantify effects on representation fidelity, retrieval precision, update correctness, and long-horizon stability, while cost analysis shows that localized maintenance can be more efficient than global reorganization.
2026-06-10	Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application
	• This survey systematically frames LLM agent environments across a full lifecycle, including environment modeling, synthesis, evaluation, and application. • It introduces symbolic and neural paradigms for environment synthesis and analyzes environment design through structural properties and capability evolution. • The work highlights co-evolution between agents and environments (memory, workflows, trajectories, and exploration) and points toward future directions such as environment-as-a-service and multi-agent ecosystems.
2026-06-09	Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation
	• This paper analyzes security risks of LLM agents from a lifecycle perspective and formalizes key interactions including information flow, delegated authority, and persistent state. • Based on a review of 247 studies, it organizes attack surfaces, defense mechanisms, and evaluation methods, showing that current defenses remain weak and incomplete. • It emphasizes the need for clearly defined trust boundaries, principled permission control, and realistic long-horizon security evaluation frameworks aligned with real-world environments.
2026-06-04	Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads
	• Characterizes agent memory as a stateful long-horizon systems workload rather than only a retrieval add-on. • Introduces a system-oriented taxonomy and a phase-aware profiler for construction, retrieval, and generation cost. • Analyzes ten representative systems and distills practical recommendations for scalable, freshness-aware memory deployment.
2026-05-23	MemEye: A Visual-Centric Evaluation Framework for Multimodal Agent Memory
	• Proposes MemEye, a visual-centric benchmark evaluating multimodal agents' long-term memory beyond text-dominated shortcuts. • Designs a 2D evaluation matrix (X-axis: Scene/Region/Instance/Pixel granularity; Y-axis: Atomic/Relational/Evolutionary reasoning depth). • Introduces three-stage validation gates ensuring questions cannot be solved via textual context, captions, or beyond VLM capabilities.
2026-06-01	EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning
	• Proposes EverMemOS, a three-stage memory lifecycle (Episodic Trace Formation → Semantic Consolidation → Reconstructive Recollection) that organizes fragmented conversations into structured, evolving memory units (MemCell/MemScene). • Introduces MemCell as atomic memory unit containing Episode + Atomic Facts + Foresight + Metadata, and MemScene for scene-level consolidation enabling long-term user profile evolution. • Designs a reconstructive retrieval paradigm with MemScene-guided retrieval, foresight validity filtering, and sufficiency verification to achieve "necessary and sufficient" context for long-horizon reasoning.
2026-05-19	An Agent-Oriented Pluggable Experience-RAG Skill for Experience-Driven Retrieval Strategy Orchestration
	• Identifies three engineering problems in pipeline-based retrieval: non-reusable strategy logic, non-observable routing decisions, and non-evolvable workflow coupling. • Proposes Experience-RAG Skill with six modules (interface, scene analyzer, experience memory, strategy router, retriever pool, result packer) to encapsulate retrieval strategy selection as a pluggable agent skill. • Shows rule-based routing outperforms learned routing (0.8924 vs 0.8778/0.8627) under limited experience memory; experience memory records (scene_features, score_vector, best_margin) enable future upgrade to learning-based routing.
2026-04-17	Human Cognition in Machines: A Unified Perspective of World Models
	• Surveys memory, perception, language, reasoning, imagination, motivation, and metacognition in world models from a unified cognitive-architecture perspective. • Introduces Epistemic World Models for structured knowledge agents in scientific discovery, with taxonomy and future directions across video, embodied, and cognitive world models.
2026-04-17	Agentic Frameworks for Reasoning Tasks: An Empirical Study
	• Evaluates 22 common agentic frameworks on BBH, GSM8K, and ARC, comparing accuracy, latency, cost, and cross-benchmark consistency. • Finds that performance differences mainly come from orchestration quality, especially memory control, context growth, and failure-retry mechanisms.
2026-04-09	Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
	• Surveys LLM agent externalization from the perspective of memory, skills, protocols, and harness engineering. • Argues that memory externalizes cross-time state, skills externalize procedural capability, protocols externalize interaction structure, and harnesses orchestrate these components together. • Maps the evolution from model parameters to context and then to external infrastructure, while discussing the key trade-offs and open challenges across these layers.
2026-03-05	Beyond the Context Window: A Cost-Performance Analysis of Fact-Based Memory vs. Long-Context LLMs for Persistent Agents
	• Addresses the ongoing industry debate over the performance and cost trade-offs between long-context models and external memory systems for building persistent agents. • Conducts a systematic cross-benchmark evaluation of long-context approaches and fact-based external memory solutions across three major memory benchmarks, assessing both accuracy and cumulative API inference cost. • Long-context models demonstrate advantages in factual recall, but their costs increase with each interaction turn; at a 100k context length, memory systems surpass them in cost efficiency after approximately 10 turns, providing a quantitative basis for real-world engineering decisions.
2026-03-02	Modular Memory is the Key to Continual Learning Agents
	• Traditional foundation models rely on weight updates for continual learning, which can easily lead to catastrophic forgetting and makes large-scale experience accumulation difficult. • Proposes a roadmap for a modular memory architecture that integrates in-context learning with learning encoded in model weights. • This architecture leverages in-context learning for rapid adaptation and weight updates for capability consolidation, offering theoretical guidance for building truly lifelong learning agents.
2026-03-02	Emerging Human-like Strategies for Semantic Memory Foraging in Large Language Models
	• Investigates whether large language models possess human-like, efficient strategic access mechanisms when processing vast amounts of semantic memory. • Uses mechanistic interpretability techniques to analyze semantic fluency tasks, rigorously examining convergent and divergent memory search patterns within the model. • Confirms the presence of human-like strategic memory search behavior across different layers of LLMs, laying an interpretability foundation for research on cognitive alignment and enhanced human-AI collaboration.
2026-02-26	Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions
	• This paper explores the foundations, evaluation, and future directions of personalized LLM-driven agents, providing a capability-oriented systematic review of their underlying principles, assessment methodologies, and prospective developments. • It constructs a taxonomy centered on four interdependent core components: user profile modeling, memory, planning, and action execution. • The paper offers a comprehensive analysis of how user signals are represented, propagated, and utilized, and discusses application scenarios and design trade-offs ranging from general-purpose assistance to specialized domains.
2026-01-14	Rethinking Memory Mechanisms of Foundation Agents in the Second Half: A Survey
	• This paper proposes a unified taxonomy for foundation agent memory across three dimensions: memory substrate, cognitive mechanism, and memory subject. • It analyzes memory operation mechanisms in single-agent and multi-agent systems while categorizing memory learning policies into prompting, fine-tuning, and reinforcement learning paradigms. • It provides a comprehensive review of evaluation metrics and benchmarks across various applications, outlining critical future directions such as memory efficiency, life-long personalization, and multimodal integration.
2025-12-15	Memory in the Age of AI Agents: A Survey
	• Provides a comprehensive and up-to-date landscape of agent memory, explicitly distinguishing it from related concepts like LLM memory, RAG, and context engineering. • Introduces a unified taxonomy examining memory through three lenses: Forms (token-level, parametric, latent), Functions (factual, experiential, working), and Dynamics (formation, evolution, retrieval). • Discusses emerging research frontiers such as automation-oriented memory design, reinforcement learning integration, and trustworthiness, while compiling representative benchmarks and frameworks.
2025-09-18	A Survey of Machine Unlearning
	• Provides an in-depth exploration of the concept and background of machine unlearning, highlighting its importance in modern machine learning. • Machine unlearning aims to enable learning algorithms to effectively remove the influence of specific data without requiring full model retraining. • The paper analyzes the necessity, challenges, and design requirements of machine unlearning, reviews current research progress, and emphasizes the field’s complexity and diversity in terms of algorithmic effectiveness, fairness, and privacy protection.
2025-09-02	A Survey on the Memory Mechanism of Large Language Model based Agents
	• Explores the memory mechanisms of LLM-based agents, emphasizing the crucial role of memory in agent self-evolution and complex interactions. • Systematically summarizes and categorizes existing memory module designs and evaluation methods, while analyzing their roles and limitations across different application scenarios. • Such agents are able to improve decision-making and task execution.
2025-05-31	A Survey of Machine Unlearning in Large Language Models: Methods, Challenges and Future Directions
	• The paper investigates machine unlearning in large language models (LLMs), aiming to effectively remove the influence of undesirable data (e.g., sensitive or illegal information) without full retraining, while preserving overall model utility. • It defines the objectives and paradigms of LLM unlearning and establishes a comprehensive taxonomy. • The paper reviews existing approaches, evaluates their strengths and limitations, and discusses opportunities for future research.
2025-05-27	Rethinking Memory in AI Taxonomy, Operations, Topics, and Future Directions
	• Explores multidimensional research on memory in artificial intelligence (AI), with a particular focus on memory operations and management in large language models (LLMs). • Categorizes various types of memory representations and operations—including integration, updating, indexing, forgetting, retrieval, and compression—and provides a systematic analysis of the importance of memory in AI and how it is implemented. • Through an extensive review of the literature, the paper identifies four key research themes: long-term memory, parametric memory, long-context memory, and multi-source memory integration.
2025-04-24	Cognitive Memory in Large Language Models
	• Provides a comprehensive examination of memory mechanisms in large language models (LLMs), with a particular focus on different types of memory and their roles within the models. • While LLMs excel at information retrieval and interaction summarization, their long-term memory remains unstable. • Integrating memory into AI systems is crucial for delivering context-rich responses, reducing hallucinations, improving data processing efficiency, and enabling the self-evolution of AI systems.
2025-04-23	From Human Memory to AI Memory A Survey on Memory Mechanisms in the Era of LLMs
	• Explores the relationship between human memory and the memory mechanisms of LLM-based artificial intelligence (AI) systems. • The main contributions include a systematic definition of memory in LLM-driven AI systems and its conceptual linkage to human memory. • The paper proposes a three-dimensional memory taxonomy based on object, form, and time, and summarizes key open issues in current research on personal memory and system memory.
2025-04-02	Digital Forgetting in Large Language Models: A Survey of Unlearning Methods
	• The paper explores digital forgetting in large language models (LLMs) and corresponding unlearning methods, with a focus on addressing issues related to privacy, copyright, and social ethics. • It analyzes different types of model architectures and training processes, as well as practical approaches to digital forgetting, including data retraining, machine unlearning, and prompt engineering. • By introducing the concept of “forgetting guarantees,” the paper emphasizes effective mechanisms for both exact and approximate forgetting.
2025-01-12	Human-inspired Perspectives: A Survey on AI Long-term Memory
	• This paper systematically examines the interplay between human long-term memory mechanisms and AI long-term memory, and proposes an adaptive long-term memory cognitive architecture (SALM). • It introduces the structure of human memory, including sensory memory, working memory, and different types of long-term memory (episodic, semantic, and procedural memory). • The paper analyzes the classification of AI long-term memory—parametric and non-parametric memory—as well as their storage and retrieval mechanisms.

Framework & Methods

Date	Paper & Summary	Tags	Links
2026-07-02	DRIFTLENS: Measuring Memory-Induced Reasoning Drift in Personalized Language Models
	• DRIFTLENS is a ground-truth-free framework that maps each expressed reasoning step to a value-ontology symbol and measures divergence between a question's no-memory trajectory and its trajectory under injected user-attribute memory, revealing that personalization memory silently reshapes how a model reasons ("symbolic drift"), not just its answer. • Measures per-instance reasoning stability of personalized LLMs under memory perturbations using a value ontology and two drift metrics (DTW and SRI) on a benchmark of unverifiable, persona-indifferent questions, and evaluates GRPO- and DPO-based post-training as mitigation. • Across four LLMs and 10 user-attribute categories, irrelevant persona memory induces medium-to-large reasoning drift (Cohen's d ≈ 0.35–0.98); GRPO and DPO both reduce drift but neither dominates (e.g., GRPO lowers DTW to 0.186 vs. 0.309 on Gemma2-2B; DPO reaches 0.204 on Qwen3-4B).
2026-07-02	InduceKV: Fixed-Footprint Continual Adaptation of Multimodal LLMs via Inducing KV Memories
	• Reframes continual multimodal-LLM adaptation as budgeted online inducing-set selection: task increments are stored as attention-compatible external KV memories (a frozen retrieval key plus compact layerwise KV payloads) injected into self-attention, keeping the backbone frozen under a strict fixed memory budget. • Fixed-footprint continual adaptation of MLLMs (task-incremental tuning, continual VQA, domain-incremental, lifelong tuning); extracts attention-ready memory entries and builds a compact inducing set via bilevel optimization (inner retrieval calibration; outer weight selection). • Consistently beats PEFT, MoE, replay, and prompt-retrieval baselines under matched budgets; improves over HiDe-LLaVA by 0.88 Avg/1.12 Last on UCIT and 1.35/1.43 on COIN, and raises continual-VQA AP 51.34→52.64 over CL-MoE while outperforming QUAD on VQACL.
2026-07-02	A-TMA: Decoupling State-Aware Memory Failures in Long-Term Agent Memory
	• Identifies "ghost memory" — a state-coordination failure where old, current, and transition facts coexist and mislead answers — and proposes A-TMA (Adaptive Truth Maintenance Auditing), a state-aware overlay that decouples memory into three diagnosable levels (bank maintenance, retrieval, answer-time resolution). • Long-term agent memory under changing user facts; A-TMA keeps superseded/transition records with typed links (a lightweight Sentry gate plus a Qwen2.5-3B Judge), builds state-aligned evidence packets, and conditions QA on explicit labels, alongside a new conflict-heavy benchmark LTP (LoCoMo Temporal Plus). • On LTP, Graphiti/Zep +A-TMA improves conflict accuracy by 0.240 absolute (0.480→0.720) and InsideOut+A-TMA lifts Acc from 0.117 to 0.662; on LoCoMo, Graphiti/Zep +A-TMA raises temporal F1 from 0.0295 to 0.1705.
2026-07-02	Learning User-Aware Recall: Personalized Retrieval in Long-Term Conversational Memory
	• Profile-guided Personalized Retrieval Optimization (PPRO) makes long-term conversational memory retrieval both user-aware and optimizable by injecting a derived user-profile embedding as an explicit personalized prior into the retrieval ranking score. • Personalized long-term conversational QA; PPRO builds episodic and semantic memory banks plus a user profile offline, performs profile-guided dual-path retrieval, and trains a query rewriter with GRPO using evidence-retrieval and answer quality as rewards while keeping memory banks and answer model frozen. • On LoCoMo, PPRO gives the best overall F1 across three backbones, beating prior-best SimpleMem by 7–19 points (e.g., GPT-4o overall F1 48.16 vs. 40.87); on LongMemEval-S it reaches 81.5 overall accuracy vs. 75.9 for the best baseline.
2026-07-02	ISM: Self-Improving Strategy Memory for Continual Mathematical Reasoning
	• Intelligent Schema Memory (ISM) is a self-evolving external memory that lets a frozen LLM improve at math reasoning under hard episodic resets by maintaining a compact, bounded bank of strategy schemas with dual representation (stable content + online-adapting feature hook), where every update is gated by symbolic verification. • Continual mathematical reasoning under a streaming episode protocol with frozen parameters; ISM uses two-stage retrieval (operator filter + soft scoring), symmetric success/failure learning, and seven self-improvement mechanisms plus conditional schema synthesis. • 80.67% on MATH-Hard and 61.67% on OlympiadBench over a 300-episode stream, beating the strongest baseline by +2.00 points on each while storing 64% and 86% fewer schemas (up to 23× fewer entries), with positive backward transfer (+0.03) on OlympiadBench.
2026-07-01	Multi-Head Recurrent Memory Agents
	• Decomposes recurrent-memory performance into capture vs. retention, diagnoses retention as the dominant bottleneck (caused by monolithic memory blocks), and proposes Multi-Head Recurrent Memory (MHM) — a training-free framework partitioning memory into independent heads with a stage-wise select-then-update strategy that structurally shields unselected heads from overwriting. • Reliable long-context reasoning over 100K–1M tokens; MHM-LRU is a lightweight instantiation that selects the least-recently-updated head each step, guaranteeing uniform head utilization with zero extra token overhead and no retraining. • On RULER-HQA at 896K tokens, MHM-LRU lifts retention from less than 30% to 73.96% and accuracy to 49.74% (vs. 21.62% MemAgent, 0.00% native LLM); on BABILong at 1M tokens it reaches 41.41% vs. 25.26% for MemAgent, staying stable where baselines collapse.
2026-07-01	AUTOMEM: Automated Learning of Memory as a Cognitive Skill
	• Reframes memory management as an independently trainable "metamemory" skill by promoting file-system operations (read/write/search/append/create) to first-class memory actions alongside task actions, then automates its improvement along scaffold structure and model proficiency via meta-LLM-driven outer loops. • Long-horizon procedurally generated games (Crafter, MiniHack, NetHack); Loop 1 (a meta-LLM revises the agent scaffold/file schema) and Loop 2 (a meta-LLM curates good memory decisions to LoRA-finetune a dedicated "memory specialist" while the gameplay model stays frozen). • Optimizing memory alone yields ~2×–4× gains on a Qwen2.5-32B base — Crafter 25.0→51.36%, MiniHack 7.5→30.0%, NetHack 0.42→1.85% — bringing the 32B model to the level of frontier systems like Claude Opus 4.5 and Gemini 3.1 Pro Thinking.
2026-07-01	Imprint: Online Memory Compression for Long-Horizon Egocentric QA
	• Imprint reframes long-horizon egocentric memory as an online memory compression problem (rather than hierarchical text summarization), representing observations as structured Interaction Records and consolidating them using cognitively-inspired signals of recurrence, recency, and distinctiveness. • Long-horizon egocentric QA; parses captions into (person, action, object, timestamps) records via Qwen2.5-7B-Instruct, groups them into event prototypes, scores importance, and consolidates online into a compact retrieval-oriented memory. • On the EgoLifeQA 7-day benchmark, improves QA accuracy 31.0%→35.8% and grounded accuracy 10.8%→64.8% (6× more evidence-grounded answers than EgoRAG), while reducing memory footprint 2.3× (109 MB vs. 254 MB) and retrieval latency 11.8× (1.7s vs. 20.1s/query).
2026-06-30	From Signals to Structure: How Memory Architecture Drives Language Emergence in LLM Agents
	• Demonstrates that in Lewis signaling games with frozen LLM agents, memory architecture matters more than channel capacity for language emergence — a persistent private notebook lets agents externalize learned conventions and avoid the high-capacity collapse seen in stateless agents. • A two-agent referential signaling game (sender/receiver coordinating a code from scratch) run with gpt-5.4-mini; compares five memory architectures (memory only, env board, scratchpad, codebook, codebook meta) across channel capacities from 4 to 125. • The scratchpad notebook achieves the most reliable coordination (0.867 ± 0.023 at capacity=25) while stateless "memory only" peaks at cap=25 then collapses (collision 1.0 at cap=64); the information-bottleneck point (cap=8) is a bimodal fragility point, not a compositional optimum.
2026-06-30	The Past Is Prologue: A Plug-in Controller for Selective Updates in Sequentially Evolving LLM Memory
	• Janus is a method-agnostic plug-in memory controller that treats each candidate memory update as an accept/reject deployment decision, combining a Memory Momentum Trigger (when to compare old vs. new memory) with a compact hybrid evaluation set of coverage, boundary, and fresh tasks (what to compare on). • Sequentially evolving LLM memory for task-solving agents; Janus wraps existing memory updaters (e.g., DC-RS, ExpeL) without changing their update rules, using directional deviation of the memory-update trajectory to trigger bounded-cost old-vs-new validation. • Across six datasets, two LLMs (Qwen3-8B, DeepSeek-V4-Flash), and two updaters, Janus improves average accuracy by +2.7 to +4.6 points (e.g., DC-RS 79.5→83.2 and ExpeL 78.3→81.5 on Qwen3-8B).
2026-06-29	Forensic Trajectory Signatures for Agent Memory Poisoning Detection
	• Discovers a mechanistically-forced behavioral invariant ("recall fact before send email") that persistent memory-poisoning attacks imprint on an LLM agent's tool-call trajectory, enabling detection from operation-only tool logs without access to memory contents, model weights, or activations. • Detects memory-channel (delayed-trigger) poisoning by extracting 19 trajectory features from trigger-session tool logs and training LR/RF/GBM classifiers, evaluated via 5-fold CV, BCa bootstrap, and leave-one-model-out hold-out on 2,520 runs across 9 models (7B–120B). • The single invariant rule alone reaches AUC=0.9563; the full Random Forest reaches AUC=0.9904 (Recall 0.984), with AUC=1.000 on 6/9 cross-model hold-outs and zero-retraining transfer to GPT-4.1/GPT-4o; a prefix-only variant hits AUC=0.934 for inline blocking.
2026-06-29	Neural Procedural Memory: Empowering LLM Agents with Implicit Activation Steering
	• NPM is a training-free framework that represents agent procedural memory as implicit activation-steering vectors in the residual stream rather than explicit textual instructions, distilled from dual-granularity contrastive experiences to overcome the text-action disconnect of RAG-injected guidelines. • Procedural memory for LLM agents; pre-computes steering vectors from contrastive success/failure trajectories, then retrieves and dynamically synthesizes a task-specific vector injected at inference time to modulate reasoning and action selection without parameter updates or context expansion. • On four benchmarks (ALFWorld, WebShop, ScienceWorld, BabyAI), NPM matches/exceeds explicit textual baselines (e.g., MiniCPM3-4B avg 22.60→28.87; Qwen3-8B 30.63→36.32) and the hybrid NPM+Workflows setting is best overall (Qwen3-8B avg 41.89, ALFWorld 66.42%).
2026-06-29	Mandol: An Agglomerative Agent Memory System for Long-Term Conversations
	• Mandol consolidates fragmented vector/graph memory into a unified memory-native architecture combining a hierarchical memory model, an agglomerative SemanticMap/SemanticGraph structure that natively fuses key-value/vector/graph storage, and a quantitative retrieval mechanism that runs without invoking LLMs. • Long-term cross-session conversational memory; replaces RAG-style recall-then-rank with query-adaptive routing, MAD-based denoising/conflict resolution, and MMR token-constrained context generation over an in-memory unified store (with DuckDB persistence). • Best overall accuracy on LoCoMo (92.21%) and LongMemEval (88.40%), with ~5.4× mean retrieval and ~4.8× mean insertion speedup under 10 QPS load, cutting tokens 17.4–20.0% vs. EverMemOS.
2026-06-28	Manufactured Confidence: How Memory Consolidation Turns Hearsay into Confident Facts
	• Diagnoses "manufactured confidence" — memory-consolidation products (mem0, LangMem) rewrite hedged, casual remarks into confident, dated standalone "facts" that agents then obey, showing agents key on the confidence of phrasing rather than the source, needing no attacker. • Uses judge-free access-control and budget-approval agents across five models/four providers to isolate the failure, running the same poisoning protocol against mem0, LangMem, and a verbatim-storage control, and testing framings, source attribution, uncertainty tags, and a hedge-preserving extraction prompt. • mem0 and LangMem launder hedged injections into confident facts at 100% (verbatim control 0%); confident framings grant unauthorized access ~0.81 while hedges collapse to ~0.00; a redundant directory restores 0.00 wrong-grant, and hedge-preserving extraction cuts wrong-grant from 0.45 to 0.10.
2026-06-28	Selective Memory Retention for Long-Horizon LLM Agents
	• TraceRetain is a lightweight capacity-bounded memory-retention framework for frozen LLM agents that scores memory entries by interpretable features (success, age, access frequency, redundancy, specificity, similarity, downstream utility) and evicts the lowest-scoring ones. • Formulates external-memory management as a capacity-constrained retention problem on ALFWorld (gpt-5-mini, ReAct-style), comparing TraceRetain-Linear/CEM against cache heuristics (FIFO/LRU/LFU/Random/Ebbinghaus) and unbounded memory under a 75%-distractor noisy-write stress. • Methods saturate on clean ALFWorld (47–49/50 vs. 39/50 no-memory); under noisy writes, unbounded and FIFO Precision@5 collapse while TraceRetain-CEM stays stable (16.9%→16.6%) and preserves 97/100 task success, with bounded K=50 matching unbounded K=100.
2026-06-27	Memory as an Attack Surface in LLM Agents: A Study on Multiple-Choice Question Answering
	• Frames the external memory of LLM agents as an attack surface, showing that misleading or corrupted memories inserted through ordinary natural-language interactions can silently flip an agent's answer even when the current query is clean. • Builds a planner-guided LLM QA agent with external memory for four-option MCQ, then applies two attacks — false-information memory injection and interaction-based answer-choice steering — across ML, cybersecurity, and networking on GPT-5.4/GPT-4o mini, Gemma2-9B, and Phi3-14B. • Clean baselines average 91.85% (closed) vs. 77.10% (open); false-memory injection causes 82/1064 answer changes (7.80% ASR) with Phi3-14B most vulnerable (34.48% shift in cybersecurity); feedback reinforcement biases answers more than example exposure.
2026-06-25	Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agents
	• Introduces "supersession" (keeping the current value of a changed fact) as a distinct, trainable failure mode, and releases Supersede — the first RL environment whose reward directly targets temporal fact-currency rather than a proxy, reframing the FAMA metric as a dense training signal. • Handling superseded facts in long multi-session dialogue under a bounded, self-maintained memory; diagnoses the gap on the LongMemEval knowledge-update subset and trains it down via GRPO fine-tuning of Qwen2.5-3B with a programmatic supersession-aware reward. • Bounded memory drops knowledge-update accuracy 92%→77% on frontier gpt-5.4 (p=0.0033); accuracy falls further as conversations grow 24× (68%→28%); GRPO training nearly doubles held-out supersession accuracy on real unseen conversations (9.0%→16.7%).
2026-06-25	Temporal Validity in Retrieval Memory: Eliminating Stale-Fact Errors for AI Agents over Evolving Knowledge
	• MemStrata maintains temporal validity via a deterministic (subject, relation, object) supersession rule in a bi-temporal ledger — retiring stale facts with no similarity threshold and no LLM call on the read path — backed by a proof that cosine similarity cannot separate contradictions from duplicates (AUROC 0.59). • Keeps agent memory current under evolving knowledge (code renames, config/dependency/API changes); stores facts like RAG for full static recall but supersedes contradicted values, evaluated fully deterministically on a local 7B model across 6 benchmarks. • Ties RAG on static knowledge yet reaches 0.95–1.00 accuracy on evolving knowledge vs. RAG's 0.20–0.47 (2–5× gain), drives stale-fact-error rate from 15–40% to ~0%, and runs ~8× faster than LLM-reranking baselines (~2.1s vs. ~16–18s).
2026-06-24	Memory Makes the Difference: Evaluating How Different Memory Roles Shape Conversational Agents
	• Introduces the first fine-grained taxonomy of conversational memory by functional role (answer, clarifying, enriching, distracting, irrelevant) plus a user-centric, context-aware LLM-as-judge evaluation framework covering accuracy, relevance, and informativeness. • Controlled comparative experiments on conversational RAG over two long-term multi-session datasets (LongMemEval-m, Long-MT-Bench+) with three frontier LLMs and three retrievers, varying context size and memory-type composition. • Clarifying memory reliably improves factual accuracy; distracting memory substantially harms accuracy/relevance; irrelevant memory reduces topic relevance; performance rises then declines as context grows (information overload), and answer memory remains essential.
2026-06-23	Reasoning as Attractor Dynamics: Latent Memory Retrieval via Gibbs-Weighted Energy Minimization
	• Reframes LLM reasoning as retrieval from a Dense Associative Memory — correct chains as flat-minima attractor basins, hallucinations as sharp minima — and introduces a Gibbs-Weighted Basin Selection operator that reweights sampled paths by inverse-square spectral entropy (W ∝ E⁻²). • Math reasoning (GSM8K); sample K high-temperature trajectories, compute each path's trajectory energy as length-normalized NLL, then reweight via a post-hoc Gibbs measure to relax into the dominant attractor basin. • On GSM8K with Phi-3.5-mini (3.8B), Gibbs-Weighted Retrieval (K=12) reaches 90.07% vs. Standard Sampling/majority-vote 84.69% and Greedy Decoding 78.4% — a +5.38% gain over self-consistency.
2026-06-23	ReM-MoA: Reasoning Memory Sustains Mixture-of-Agents Scaling
	• A memory-augmented Mixture-of-Agents framework built on a Ranked Reasoning Memory that persistently stores/ranks cross-layer reasoning traces via a comparative Reviewer Agent, paired with Curated Diversified Memory Routing to preserve both reasoning quality and exploration diversity. • Scalable multi-agent LLM reasoning; at each layer a Reviewer Agent comparatively scores traces with rationales and later agents receive distinct high/low/contrastive trace subsets, with an optional frontier-model (GPT-5.5) LoRA distillation pipeline to upgrade the Reviewer. • Across five reasoning benchmarks (MATH, MMLU-redux, Formal Logic, CRUX, HellaSwag) it consistently beats prior MoA variants and the gap widens with depth — e.g., MATH at L=9: ReM-MoA* 84.0% vs. AttentionMoA 76.9% vs. Standard MoA 61.0%.
2026-06-19	When Does Overlap Help? OSU-Mem and a Cell-Conditional Analysis of Trajectory Memory for LLM Agents
	• OSU-Mem organizes agent trajectory memory into overlapping semantic units with budgeted coarse-to-fine retrieval, and shows via cell-conditional analysis that overlap helps only when evidence steps share tool calls or entities (T+E+) and hurts when they share neither (T−E−). • Budgeted retrieval from long-horizon LLM-agent trajectories under a strict token budget; builds OSUs from entity/tool/subgoal/similarity views, then query-adaptive centroid-scored expansion, evaluated on a synthetic benchmark, τ-bench, and ToolBench with a 2×2 tool/entity cross-tabulation. • +39.9% Recall and +61.5% Hit@2 over the strongest baseline at B=256 on the synthetic benchmark; wins on T+E+ but loses on T−E− in τ-bench; on ToolBench overlap beats disjoint construction with a monotonic dose-response.
2026-05-30	Memory Shot for Long-Term Dialogue
	• MemShot renders raw dialogue spans directly into structured visual "memory shots" (images preserving speaker turns, timestamps, and turn boundaries) and leverages an MLLM's internal visual reasoning, avoiding fragile, heavyweight text-based memory construction. • Long-term dialogue memory-augmented QA; segments dialogue into contiguous spans, renders each into a hierarchical header+chat visual unit, retrieves top-k units (Qwen3-VL-Embedding-8B), and answers with Qwen3-VL-Instruct MLLMs (2B/8B/32B). • Competitive-to-superior on LoCoMo (79.61 overall Acc @32B) and LongMemEval (74.80 overall Acc @32B) while delivering ~70× faster memory construction (≈9.56s), beating visual-memory baseline MemOCR by over 10%.
2026-06-25	MIRROR: Novelty-Constrained Memory-Guided MCTS Red-Teaming for Agentic RAG
	• This paper studies red-teaming for multimodal agentic RAG systems whose attack surface spans retrieved text, images, direct user queries, and orchestrator-level tool manipulation. • MIRROR combines an episodic memory bank of successful attack traces with Monte Carlo tree search; retrieved memories provide search priors, while a deterministic novelty gate blocks copying from known or retrieved attacks. • Across four attack surfaces, the framework validates candidates through deterministic replay or structured tool-call parsing, aiming to make memory-guided adversarial search both more effective and less dependent on recycled templates.
2026-06-23	Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning
	• This paper studies experience-driven self-evolution for LLM agents and identifies a Self-Confirmation Trap: single-agent loops may treat wrong-but-self-consistent trajectories as successful experience, causing erroneous memories to be retrieved and reused later. • It proposes EDV, an Execute-Distill-Verify framework in which multiple heterogeneous agents first explore the same task space, a third-party distillation agent compares candidate trajectories to produce experience candidates, and an execution group verifies them through consensus before memory insertion. • By decoupling execution, experience distillation, and verification, EDV turns isolated self-reflection into collaborative experience construction and filters noisy or erroneous content before it enters shared or private memory. Experiments on tau2-bench, Mind2Web, and MMTB show consistent improvements over strong baselines, highlighting the importance of reliable memory construction for agent self-evolution.
2026-06-18	Grouped Query Experts: Mixture-of-Experts on GQA Self-Attention
	• This paper proposes Grouped Query Experts (GQE), a mixture-of-experts layer on top of grouped-query attention that targets the high cost of dense self-attention at long context lengths. • Within each GQA group, a router selects k query-head experts per token while leaving all key-value heads dense and unchanged. This preserves the KV-cache advantages of GQA while reducing active query-head computation according to token difficulty or information content. • On a fixed 30B token budget at the 250M parameter scale, GQE matches the downstream accuracy of an all-active GQA baseline while activating only half of the query heads per token, suggesting a path to more efficient long-context processing.
2026-06-18	Multi-Agent Transactive Memory
	• This paper introduces Multi-Agent Transactive Memory (MATM), which organizes action–observation trajectories generated by heterogeneous agents into group-level shared memory. Producer agents contribute execution experience, while consumer agents retrieve prior trajectories, enabling the reuse of procedural knowledge that would otherwise be discarded after a single task. • MATM employs a state-conditioned key–value index, using the current task and recent interaction history as retrieval keys and subsequent trajectory segments as values. It further applies a learning-to-rank model that integrates producer reliability, consumer characteristics, retrieval scores, and trajectory attributes to perform personalized reranking. • Experiments in ALFWorld and WebArena demonstrate that retrieving shared trajectories improves task performance and reduces interaction steps without requiring direct inter-agent coordination or joint training, with benefits extending across agents of varying capability levels. The work thereby extends individual experiential memory into a collective knowledge infrastructure for open agent ecosystems.
2026-06-17	What Must Generalist Agents Remember?
	• This paper formally investigates the information that generalist agents must retain to sustain near-optimal behavior across multiple environments and objectives. It defines states with identical observations but conflicting optimal actions as observational bottlenecks that reveal the necessity of memory. • The separation theorem establishes that when different domains require mutually incompatible optimal actions at such a bottleneck, any unified near-optimal policy must induce distinct memory distributions. Consequently, a memoryless policy that relies solely on the current observation cannot simultaneously maintain a high success rate and cross-domain robustness. • The paper further proves that if memory is sufficient to estimate the value functions of a set of relevant objectives, local transition dynamics can be approximately reconstructed from it. Memory therefore serves simultaneously as a mechanism for domain disambiguation, environment-model reconstruction, and a substrate for planning. These findings establish theoretical necessity but do not directly prescribe a specific engineering architecture for memory.
2026-06-17	User as Engram: Internalizing Per-User Memory as Local Parametric Edits
	• This paper decomposes personalized memory into user-specific content and reasoning skills shared across users. It proposes storing user facts through localized row edits in the hash-keyed memory table of the Engram model, while a single shared adapter provides fact interpretation and indirect reasoning capabilities. • Unlike per-user LoRA modules, which exert dense effects on global weights, localized Engram edits operate only at precisely triggered positions and leave all other positions unchanged. Facts belonging to different users are written into non-overlapping hash slots, enabling additive and lossless multi-user composition within a shared table. • The paper reports that the proposed design matches per-user LoRA in direct recall, improves indirect-reasoning accuracy by an average factor of 5.6, and reduces memory consumption by approximately 33,000 times. After roughly 100 facts, it also outperforms retrieval pipelines using larger models. Its applicability, however, depends on the underlying model possessing an editable Engram memory architecture.
2026-06-16	Closing the Feedback Loop: From Experience Extraction to Insight Governance in Verbal Reinforcement Learning
	• This paper identifies a retention–forgetting dilemma faced by parameter-free verbal reinforcement learning in non-stationary environments: retaining obsolete rules indefinitely induces negative transfer, whereas permanently deleting prior knowledge causes catastrophic forgetting when similar conditions recur. • The authors propose a three-layer memory architecture comprising rules, evidence, and skills, connected through a feedback-driven curation loop. Rules distill experience, evidence records the cross-episode reliability of rules, and skills govern rule selection, conflict resolution, and abstention when necessary. • A financial forecasting case study shows that the same accumulated experience can produce performance below a zero-shot baseline when governance is absent, whereas incorporating the curation loop improves both predictive accuracy and risk-adjusted returns. This finding suggests that the central bottleneck in continual agent learning is not merely experience extraction, but also the governance of knowledge lifecycles and application permissions.
2026-06-16	Memory as a Wasting Asset: Pricing Flash Endurance for Embodied Agents, and the Limits of Doing So
	• This paper models the finite program/erase endurance of robotic flash storage as non-renewable depreciating capital. It introduces an endurance shadow price η and uses it to construct a wear-augmented per-byte index for optimizing memory placement across RAM, onboard non-volatile storage, and cloud infrastructure. • Theoretical analysis shows that threshold-based placement policies can achieve cost optimality under different value–write correlation levels χ. Only when χ is positive can the optimal policy become non-monotonic, moving frequently written, high-value memories away from local flash storage. Real-world logs show that χ is positive for cyclic long-horizon operations, near zero for short-horizon tasks, and negative for non-cyclic teleoperation. • Endurance constraints are generally inactive for high-end TLC devices rated for approximately 3,000 P/E cycles, but may become binding for QLC or eMMC devices rated for approximately 1,000 P/E cycles. The paper also explicitly notes that wear-aware management primarily improves device longevity and cost efficiency; it has not yet been shown to increase task value or task success rates.
2026-06-15	Posterior Twins: Distributional Behavioral Simulation for Enterprise Decisions
	• This paper introduces Posterior Twins, extending the output of enterprise digital twins from a single most likely behavior to a posterior distribution over behaviors conditioned on a specific decision context. This formulation captures states such as adoption, churn, hesitation, and risk migration across different population segments. • The system uses governed historical memory as its evidence base and integrates behavioral-model routing, scenario orchestration, distribution aggregation, and auditing mechanisms. Evaluation employs both modal accuracy and Wasserstein-1 distance to distinguish point-prediction correctness from overall distributional fidelity. • On 226 held-out samples, TL-Twin Alpha achieves the lowest Wasserstein-1 distance in the reported results, at 1.16, while Gamma and Delta exhibit more balanced operating points. The study demonstrates the need for distributional evaluation in enterprise simulation, although its conclusions remain constrained by the scale of a single benchmark and the particular system configurations examined.
2026-06-15	Trust-Aware Multi-Agent Traceability: Confidence-Calibrated Knowledge Graphs for Consistent Software Artifact Management
	• This paper addresses error propagation in multi-agent software-engineering pipelines by employing a shared knowledge graph as both centralized semantic memory and a coordination interface. This enables downstream agents to evaluate and inherit upstream artifacts according to calibrated confidence estimates. • The method comprises two-stage traceability-link prediction combining embedding-based retrieval with LLM-based multi-criteria analysis, a trace-seed mechanism for comparing confidence at generation and verification time, and protocols for threshold gating, confidence-disagreement detection, and conflict resolution. • An automotive software-engineering case study and ablation experiments indicate that confidence calibration is essential to the effective operation of the coordination protocol. However, because the evidence is derived primarily from a domain-specific case, the work is better understood as an architectural validation of trustworthy multi-agent artifact governance than as a universally applicable conclusion across all collaborative settings.
2026-06-15	HiMPO: Hindsight-Informed Memory Policy Optimization for Less-Entangled Credit in Long-Horizon Agents
	• This paper focuses on entangled causal credit assignment in memory-writing operations for long-horizon agents. A final failure may originate from tool invocation, noisy observations, or subsequent reasoning, yet trajectory-level rewards can mistakenly penalize an earlier memory update that was itself correct. • HiMPO first compares the task-relevant information recoverable from the new and previous memories under the same pre-write state to estimate the local utility of a memory update. It then calibrates this utility using a bounded hindsight-relevance filter and applies the resulting memory-specific advantage exclusively to memory tokens. • Across open-domain evaluations and compressed-memory question-answering tasks, the method outperforms multiple memory and reinforcement-learning baselines. Controlled interventions further demonstrate that HiMPO reduces responsibility leakage caused by tool errors and improves the accuracy and interpretability of credit attribution for memory writes.
2026-06-15	User as Code: Executable Memory for Personalized Agents
	• This paper proposes the “User as Code” paradigm, implementing the user model of a personalized agent as a continuously evolving software project. Typed Python objects store user state, while ordinary functions encode constraints, aggregation logic, and response rules, allowing memory representation and memory reasoning to share the same executable medium. • The system employs a two-stage pipeline comprising an append-only fact log and periodic code checkpoints. This preserves the original history while organizing it into verifiable structured state, thereby supporting contradiction resolution, cross-record aggregation, and safety rules that are proactively triggered by state changes. • UaC achieves a conventional factual recall rate of 78.8% on LOCOMO and approximately 99% on historical aggregation questions, substantially exceeding the 6%–43% performance of retrieval-based memory. Its central contribution extends beyond improved recall: it transforms user memory from a passive query database into a proactively serving system capable of deterministic execution.
2026-06-15	TokenPilot: Cache-Efficient Context Management for LLM Agents
	• This paper observes that although conventional context pruning and dynamic memory eviction reduce input-token counts, they also alter sequence boundaries and prompt layouts, resulting in prefix mismatches and KV-cache invalidation. Context sparsity and cache continuity therefore exhibit a systematic tension. • TokenPilot adopts a dual-granularity management strategy. At the global level, Ingestion-Aware Compaction compresses environmental noise as information enters the context and stabilizes the prompt prefix. At the local level, Lifecycle-Aware Eviction performs conservative batched offloading according to the remaining task utility of individual context segments. • In both independent and continuous operation modes on PinchBench and Claw-Eval, the paper reports cost reductions of 56%–61% and 61%–87%, respectively, while maintaining task performance comparable to existing systems. These findings establish cache-compatible stable layout as an independent design objective for cost optimization in long-horizon agents.
2026-06-14	FragFuse: Bypassing Access Control of Large Language Model Agents via Memory-Based Query Fragmentation and Fusion
	• This paper reveals that long-term memory introduces a cross-turn temporal channel into agent access control. A prohibited request that would ordinarily trigger refusal can be decomposed into superficially benign fragments, written separately into memory, and subsequently recombined during retrieval. • FragFuse comprises three stages: refusal-sensitive fragment identification, memory injection using marker-based carriers, and fusion-oriented retrieval attacks. It further uses surrogate models to optimize fusion instructions and marker design, enabling automated attack generation under a black-box threat model. • The paper reports an average access-control bypass rate of 86.3% and an end-to-end harmful-task success rate of 41.1%. Prompt-injection detectors and perplexity-based detectors provide limited defensive effectiveness. These results demonstrate that security review must encompass the complete lifecycle of memory writing, storage, retrieval, and fusion rather than inspecting only the current user query.
2026-06-14	DYNA: Dynamic Episodic Memory Networks for Augmenting Large Language Models with Temporal Knowledge Graphs in Continuous Learning
	• This paper introduces DYNA, which uses a frozen large language model as its reasoning core and constructs a continuously updatable external episodic memory by representing events as nodes and temporal relations such as “before,” “after,” and “co-occurs with” as timestamped directed edges. • During querying, the system uses random walks, node centrality, and graph-structural information to identify relevant events, then combines the retrieved results with the model’s internal knowledge to generate answers. This avoids the training costs and parameter-level knowledge interference associated with continual fine-tuning. • Across three categories of temporal-memory tasks, the paper reports that DYNA reduces catastrophic forgetting by approximately 7% relative to fine-tuning and improves temporal-ordering accuracy by approximately 5% relative to standard RAG. The positive correlation between graph clustering coefficients and retrieval performance further indicates that memory topology itself is an important determinant of performance.
2026-06-12	AgentSpec: Understanding Embodied Agent Scaffolds Through Controlled Composition
	• This paper introduces AgentSpec, formalizing embodied agents as typed compositions of perception, memory, reasoning, reflection, action, and optional learning modules. Standardized interfaces support the independent replacement, ablation, and controlled recombination of these components. • The study systematically compares different reasoning, memory, reflection, and reinforcement-learning components across DeliveryBench, ALFRED, MiniGrid, and RoboTHOR. The results show that agent performance is not determined by the strength of any single module, but is significantly influenced by module compatibility, task environment, and component interactions. • The experiments specifically demonstrate that structured, multi-granularity memory provides more stable support for long-horizon state tracking, although its benefits depend on alignment between the memory representation and the downstream reasoning strategy. The work thus transforms agent scaffolding from an empirically configured engineering artifact into an analyzable compositional design space.
2026-06-11	EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments
	• Introduces EvoArena, a benchmark suite for simulating dynamic environment changes to evaluate memory robustness in LLM agents. • Proposes EvoMem, a patch-based memory paradigm that explicitly tracks structured memory evolution over time. • Results show that current agents perform poorly under evolving environments, while EvoMem significantly improves performance.
2026-06-11	MemRefine: LLM-Guided Compression for Long-Term Agent Memory
	• Proposes MemRefine, an LLM-driven memory compression framework under fixed storage budgets. • Uses semantic and factual consistency evaluation to iteratively delete, merge, or retain memory entries. • Maintains or exceeds baseline performance under strict memory constraints.
2026-06-11	Getting Better at Working With You: Compiling User Corrections into Runtime Enforcement for Coding Agents
	• Introduces TRACE, which compiles user corrections into runtime constraints for LLM agents. • Converts feedback into executable rules for continuous behavior correction. • Significantly reduces preference violations in interactive tasks.
2026-06-11	G-Long: Graph-Enhanced Memory Management for Efficient Long-Term Dialogue Agents
	• Proposes G-Long, a graph-based memory system using structured triples for long-term dialogue management. • Employs lightweight LMs for structured extraction and attention-aware importance scoring. • Achieves efficient and high-quality memory retrieval with reduced computational cost.
2026-06-11	Multi-Turn Reasoning When Context Arrives in Pieces: Scalable Sharding and Memory-Augmented RL
	• Addresses multi-turn reasoning failures caused by fragmented context input in dialogue systems. • Introduces a compact rolling memory mechanism replacing full history tracking. • Improves zero-shot reasoning and generalization across long-context tasks.
2026-06-10	Arbor: Tree Search as a Cognition Layer for Autonomous Agents
	• Introduces Arbor, a multi-agent framework using tree search as a shared cognitive layer. • Maintains an explicit evolving search tree as shared working memory for coordination. • Improves scalability, reproducibility, and reasoning efficiency in large state spaces.
2026-06-10	Substrate Asymmetry in User-Side Memory: A Diagnostic Framework
	• Decomposes user-side memory into orthogonal axes rather than treating personalization as a single capability. • Identifies three key dimensions: behavioral consistency, factual presence, and factual absence. • Reveals asymmetries across models and highlights alignment and routing trade-offs.
2026-06-10	Organize then Retrieve: Hierarchical Memory Navigation for Efficient Agents
	• Proposes HORMA, a hierarchical memory organization framework for efficient navigation and retrieval. • Structures experience into layered representations to reduce loss and latency in unstructured retrieval. • Improves performance and efficiency in long-horizon dialogue tasks.
2026-06-05	Position: Hippocampal Explicit Memory Is the Cornerstone for AGI
	• Argues that explicit hippocampal-like memory is fundamental for achieving AGI-level capabilities. • Claims implicit statistical learning alone is insufficient for planning, reasoning, and meta-cognition. • Outlines computational requirements for implementing explicit memory systems in AI.
2026-06-09	Trace Only What You Need: Structure-Aware On-Demand Hypergraph Memory for Long-Document Question Answering
	• Introduces DocTrace, a multi-agent RAG framework with structure-aware hypergraph memory. • Builds document structure trees and on-demand shared memory graphs for reasoning reuse. • Achieves strong improvements over structured RAG baselines on long-document QA tasks.
2026-06-09	REAL: A Reasoning-Enhanced Graph Framework for Long-Term Memory Management of LLMs
	• Proposes REAL, a time- and confidence-aware directed graph for long-term memory management. • Supports non-destructive updates with multi-version factual memory retention. • Achieves +22.72% average improvement in long-term memory tasks.
2026-06-09	Infini Memory: Maintainable Topic Documents for Long-Term LLM Agent Memory
	• Proposes Infini Memory, which organizes agent memory into maintainable topic-centric documents. • Uses buffer-and-consolidation updates to continuously refine semantic memory over time. • Enables iterative retrieval via tool-based memory reading rather than single-pass lookup.
2026-06-09	ActiveMem: Distributed Active Memory for Long-Horizon LLM Reasoning
	• Introduces ActiveMem, decoupling planning and memory systems for long-horizon reasoning tasks. • Uses lightweight planners with distributed memory modules running in parallel. • Improves efficiency while achieving state-of-the-art performance on complex benchmarks.
2026-06-08	Memory Beyond Recall: A Dual-Process Cognitive Memory System for Self-Evolving LLM Agents
	• Proposes DCPM, a dual-process memory system separating explicit and implicit cognitive memory layers. • Uses day-time writing and night-time consolidation mechanisms for long-term evolution. • Demonstrates strong performance in cross-session reasoning and personalization tasks.
2026-06-10	MemToolAgent: Leveraging Memory for Tool Using Agents Based on Environment and User Feedback
	• Proposes MemToolAgent, which enhances tool-using agents via structured memory extraction and retrieval. • Converts past interactions into reusable memory entries for continuous improvement. • Improves personalization and task accuracy through memory-augmented reasoning.
2026-06-05	AdMem: Advanced Memory for Task-solving Agents
	• Proposes AdMem, a unified memory framework integrating semantic, episodic, and procedural memory. • Uses multi-agent coordination for memory generation, reward annotation, and adaptive retrieval. • Improves robustness and success rate in long-horizon task-solving scenarios.
2026-06-04	AdaMEM: Test-Time Adaptive Memory for Language Agents
	• Addresses the difficulty of using past experience when language agents face dynamic test-time conditions. • Combines offline long-term trajectory memory with online short-term policy memory to guide decisions. • Shows consistent gains over static memory baselines, highlighting test-time adaptation as a practical memory direction.
2026-06-04	Ask Only When Needed: Proactive Retrieval from Memory and Skills for Experience-Driven Lifelong Agents
	• Studies when a lifelong agent should consult prior experience rather than always retrieving memory. • Organizes experience into factual, episodic, and skill memory, and learns retrieval as an explicit policy action. • Improves task success while reducing unnecessary interactions, showing the value of proactive memory control.
2026-06-04	Beyond Semantic Organization: Memory as Execution State Management for Long-Horizon Agents
	• Argues that semantic similarity alone fragments decision trajectories in long-horizon agent tasks. • Proposes MAGE, a hierarchical execution-state tree with grow, compress, maintain, and revise operations. • Improves task success and reduces token use by preserving valid state paths and isolating erroneous branches.
2026-06-04	Beyond Similarity: Trustworthy Memory Search for Personal AI Agents
	• Identifies semantic-similarity memory search as a trust boundary for personal AI agents. • Introduces MemGate, a lightweight query-conditioned gate that decides whether retrieved memories should enter context. • Reduces cross-domain leakage, sycophancy, tool drift, and memory-induced jailbreak risks while preserving utility.
2026-06-04	EMBER: Efficient Memory via Budgeted Evidence Retention for Long-Horizon Agents
	• Focuses on which evidence should survive when long-horizon agents have fixed retained-memory budgets. • Learns to store source-backed evidence capsules with retrieval keys and update metadata during ingestion. • Improves retained-evidence recall and answer quality, showing that memory quality depends on budgeted evidence survival.
2026-06-04	Membrane: A Self-Evolving Contrastive Safety Memory for LLM Agent Defense
	• Targets evolving jailbreak attacks where static safety classifiers and naive memory guardrails are insufficient. • Builds contrastive safety memory cells pairing harmful queries with superficially similar benign requests. • Improves agent-level defense while reducing over-refusal and maintaining robustness under memory poisoning.
2026-06-04	Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents
	• Challenges the static retrieve-then-reason pipeline used by many memory-augmented agents. • Represents memory as a Cue-Tag-Content graph and lets the agent actively reconstruct relevant paths during reasoning. • Outperforms strong baselines on long-memory benchmarks while reducing token and runtime cost.
2026-06-04	TOKI: A Bitemporal Operator Algebra for Contradiction Resolution in LLM-Agent Persistent Memory
	• Treats contradiction resolution in persistent agent memory as a write-time consistency problem. • Defines bitemporal operators with explicit isolation assumptions, provenance annotations, and audit rows. • Clarifies the correctness contract needed by production memory systems when beliefs evolve or conflict.
2026-06-03	ABBEL: Learning Natural-Language Belief States for Memory-Efficient Interaction
	• Addresses the cost of keeping full interaction histories in long sequential decision tasks. • Learns recursively updated natural-language belief states and directly supervises their information content. • Reduces summary errors and memory footprint, narrowing the gap with full-context agents.
2026-06-03	PersonaTree: Structured Lifecycle Memory for Person Understanding in LLM Agents
	• Focuses on building durable person understanding from long-term interactions. • Organizes evidence, claims, confidence, and query-conditioned paths into a structured persona tree. • Improves personalization by returning only the evidence depth needed for each query.
2026-06-03	RAMPART: Registry-based Agentic Memory with Priority-Aware Runtime Transformation
	• Addresses runtime context assembly for LLM agents with explicit memory policies. • Defines a registry-based memory model with primitives such as promote, gate, write, evict, and rollback. • Shows that grouping and priority management of memory blocks can improve task success across models.
2026-06-03	Scaling Self-Evolving Agents via Parametric Memory
	• Addresses the limitation that retrieval memories influence prompts but rarely change future agent behavior. • Compresses historical experience into explicit memory and uses lightweight online updates as parametric memory. • Establishes a scaling direction for agents that continuously improve after deployment.
2026-06-03	Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents
	• Argues that event order is central to long-horizon memory but often lost in similarity-based organization. • Introduces SegTreeMem, an online segment-tree structure that preserves temporal order while forming hierarchical memory. • Improves long-memory answering and shows performance depends on keeping order during memory construction.
2026-06-03	Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs
	• Studies how graph topology affects leakage of private information across multi-agent LLM systems. • Introduces MAMA with Engram seeding and Resonance extraction phases over controlled synthetic PII documents. • Finds denser connectivity, shorter distance, and higher target centrality increase leakage risk.
2026-06-02	DMF: A Deterministic Memory Framework for Conversational AI Agents
	• Targets nondeterminism and token cost in LLM-summary-based conversational memory pipelines. • Uses classical NLP, vector geometry, mathematical scoring, survival scores, and decay rules. • Achieves competitive accuracy while reducing token usage and improving reproducibility.
2026-06-02	InfoMem: Training Long-Context Memory Agents with Answer-Conditioned Information Gain
	• Addresses sparse rewards in training chunk-wise memory agents for long-context tasks. • Uses answer-conditioned information gain to evaluate whether final memory supports the ground-truth answer. • Provides a more targeted training signal for deciding what information should be retained.
2026-06-02	MemTrain: Self-Supervised Context Memory Training
	• Targets the lack of high-quality labeled data for training memory agents. • Uses self-supervised masked reconstruction and intermediate memory recall objectives on unlabeled corpora. • Improves memory-intensive reasoning on long-text and search-based QA tasks.
2026-06-02	RGMem: Renormalization Group-inspired Memory Evolution for Language Agents
	• Targets long-term user-state modeling under evolving and potentially conflicting conversational evidence. • Uses a renormalization-group-inspired multi-scale process with hierarchical coarse-graining and thresholded updates. • Improves cross-session continuity and adaptation to changing user preferences over flat retrieval or static summaries.
2026-06-02	SaliMory: Orchestrating Cognitive Memory for Conversational Agents
	• Addresses memory-related failures in conversational agents by supervising memory operations more explicitly. • Uses hierarchical staged rewards and contrastive refinement to train filtering, consolidation, and recall behavior. • Improves end-to-end accuracy and personalization while reducing memory-operation errors.
2026-06-02	Training-Free Lexical-Dense Fusion for Conversational-Memory Retrieval
	• Studies retrieval over multi-session conversational history where lexical and semantic cues have complementary strengths. • Combines BM25 with dense late-interaction scoring without additional training. • Improves controllable, reproducible retrieval for multi-hop, temporal, and adversarial memory questions.
2026-06-01	DELTAMEM: Incremental Experience Memory for LLM Agents via Residual Trees
	• Targets redundancy and retrieval conflict when agents accumulate many task experiences. • Organizes goal-conditioned experience and scene-level knowledge into residual trees with self-organization. • Enables compact experience reconstruction and improves performance across interactive environments.
2026-06-01	Memory Retrieval for Changing Preferences
	• Addresses personalization when user preferences change and older memories may conflict with newer evidence. • Formulates retrieval as selecting historical turns that provide evidence about the latent preference state. • Outperforms embedding-only retrieval in preference-dense long-context dialogue tasks.
2026-05-31	Don't Ask the LLM to Track Freshness: A Deterministic Recipe for Memory Conflict Resolution
	• Shows that LLM-based judgment is unreliable for resolving freshness conflicts in evolving memory. • Replaces direct LLM tracking with candidate extraction and version-aware deterministic aggregation. • Improves conflict resolution and clarifies that aggregation, not storage, is the main bottleneck.
2026-05-31	Honest Lying: Understanding Memory Confabulation in Reflexive Agents
	• Studies how reflexive agents may store false self-explanations as persistent memory. • Introduces diagnostic signals for reflection dependence and replaces open-ended self-diagnosis with trajectory-level failure extraction. • Reduces confabulated memory use across environments and improves the reliability of reflective agents.
2026-05-31	Joint Agent Memory and Exploration Learning via Novelty Signals
	• Explores the mutual dependence between memory and exploration in open-ended agent environments. • Uses novelty-driven interaction to jointly train memory and exploration policies. • Improves generalization in unseen environments while reducing token consumption.
2026-05-30	MemPro: Agentic Memory Systems as Evolvable Programs
	• Challenges fixed memory construction-retrieval pipelines that stop improving after deployment. • Treats the entire memory system as an evolvable program with version trees and failure-driven edits. • Shows memory pipelines can continuously improve across benchmarks through iterative diagnosis and debugging.
2026-05-07	Belief Memory: Agent Memory Under Partial Observability
	• Proposes BeliefMem, a memory framework that shifts the memory paradigm from storing deterministic conclusions to maintaining an attribute-level belief representation to combat self-reinforcing errors in partially observable environments. • Maintains multiple candidate conclusions with probabilities updated via Noisy-OR evidence merge, and features a belief-aware retrieval mechanism to preserve uncertainty for agent decision-making. • Demonstrates superior average performance on the LoCoMo and ALFWorld benchmarks over existing deterministic memory methods, showing strong memory correction capabilities and data efficiency.
2026-05-07	MemReranker: Reasoning-Aware Reranking for Agent Memory Retrieval
	• Proposes MemReranker (0.6B/4B), a family of reasoning-aware reranking models for agent memory systems that moves beyond simple semantic matching by leveraging a multi-stage LLM knowledge distillation pipeline. • Combines Elo/Bradley-Terry calibrated scoring, BCE pointwise distillation, and InfoNCE contrastive fine-tuning to achieve well-calibrated relevance scores and hard-sample discrimination. • Demonstrates state-of-the-art performance on memory retrieval benchmarks (LOCOMO, LongMemEval), matching the ranking quality of larger closed-source models (e.g., GPT-4o-mini) with significantly lower inference latency.
2026-05-07	Event-Causal RAG: A Retrieval-Augmented Generation Framework for Long Video Reasoning in Complex Scenarios
	• Proposes Event-Causal RAG (EC-RAG), a lightweight framework for infinite long-video reasoning that asynchronously segments video streams into semantically complete events and abstracts them into State-Event-State (SES) graph memory, replacing traditional fixed-length clip memory. • Designs a Dual-Store Memory system integrating a vector database for semantic matching and a graph database for causal-topological retrieval, achieving low-overhead storage and efficient spatiotemporal memory merging in streaming environments. • Introduces a bidirectional graph retrieval strategy to efficiently identify relevant event causal chains, significantly improving causal reasoning accuracy and preventing out-of-memory issues without requiring expensive long-sequence fine-tuning of backbone video foundation models.
2026-05-06	Tree-based Credit Assignment for Multi-Agent Memory System
	• Proposes TreeMem, a tree-based reinforcement learning framework for multi-agent memory systems that derives agent-specific credit directly from final downstream rewards without task-specific annotations. • Expands each memory agent's (builder, summarizer, retrieval) outputs into multiple subsequent branches, estimating the contributions of intermediate actions via Monte Carlo averaging. • Converts coarse final rewards into granular optimization signals, enabling heterogeneous memory agents to specialize effectively and outperforming strong baselines on long-horizon benchmarks.
2026-05-05	MemFlow: Intent-Driven Memory Orchestration for Small Language Model Agents
	• Proposes MemFlow, a training-free memory orchestration framework for Small Language Models (SLMs) that replaces open-ended reasoning loops with intent-driven memory routing to handle long-horizon memory tasks. • Features a specialized multi-agent pipeline (Router, Memory, Answer, and Validator) with dynamic context packing to ensure deterministic evidence preparation and grounded responses under strict token budgets.
2026-05-05	Governed Collaborative Memory as Artificial Selection in LLM-Based Multi-Agent Systems
	• Proposes governed collaborative memory as an artificial selection regime for LLM-based multi-agent systems, determining which candidate memories persist as durable, shared institutional state. • Introduces a layered architecture separating agent-local, shared institutional, archive, and project-continuity memory, emphasizing memory evaluation for provenance fidelity, selection traceability, and role preservation.
2026-05-05	Learning to Forget -- Hierarchical Episodic Memory for Lifelong Robot Deployment
	• Proposes H²-EMV, a hierarchical episodic memory framework for lifelong robot deployment that uses LLM-based relevance judgment and selective forgetting to manage memory scale. • Integrates user feedback to update natural-language forgetting rules, achieving personalized memory management while maintaining QA accuracy and reducing query overhead.
2026-05-05	MEMSAD: Gradient-Coupled Anomaly Detection for Memory Poisoning in Retrieval-Augmented Agents
	• Formalizes multiple attack scenarios for persistent external memory poisoning in retrieval-augmented agents and proposes MEMSAD, a semantic anomaly detection defense. • Proves via gradient-coupling theorem that anomaly score gradients align with retrieval objective gradients, enabling certifiable detection radii and optimal calibration sample complexity.
2026-05-05	ScrapMem: A Bio-inspired Framework for On-device Personalized Agent Memory via Optical Forgetting
	• Introduces ScrapMem, a bio-inspired on-device agent memory framework that uses optical forgetting to progressively reduce old memory resolution for storage efficiency. • Constructs an Episodic Memory Graph with causal-temporal event links to maintain semantic consistency, achieving superior retrieval performance on ATM-Bench with significantly lower storage cost.
2026-05-04	A Semantic Autonomy Framework for VLM-Integrated Indoor Mobile Robots: Hybrid Deterministic Reasoning and Cross-Robot Adaptive Memory
	• Proposes the Semantic Autonomy Stack (SAS), a six-layer framework for VLM-integrated indoor robots, featuring a hybrid reasoning mechanism that resolves routine instructions deterministically to bypass VLM inference latency. • Introduces a five-category semantic memory framework that compiles learned preferences into a shared digest, enabling cross-session learning and cross-robot knowledge transfer without retraining.
2026-05-04	The Dynamic Gist-Based Memory Model (DGMM): A Memory-Centric Architecture for Artificial Intelligence
	• Proposes the Dynamic Gist-Based Memory Model (DGMM), a memory-centric architectural framework that represents experience as an explicit, persistent, and graph-structured episodic-semantic memory. • Formally characterizes memory operations into four distinct regimes (ingestion, consolidation, recall, and analysis), decoupling memory storage from downstream interpretation. • Defines architectural invariants such as episodic persistence and locality of cue-conditioned surprise, enabling stable memory structures to support evolving interpretation over time without retraining.
2026-05-04	MAGE: Safeguarding LLM Agents against Long-Horizon Threats via Shadow Memory
	• Proposes MAGE, inspired by shadow stack in system security, maintaining an independent safety memory that distills and preserves critical security context across long task trajectories. • Evaluates pending actions against the safety memory before execution, enabling earlier detection of long-horizon attacks with minimal impact on agent utility.
2026-05-04	Symmetry-Protected Lyapunov Neutral Modes in Equivariant Recurrent Networks
	• Proves that group-orbit tangent directions in equivariant recurrent networks produce symmetry-protected zero Lyapunov exponents, forming long-lived neutral memory modes. • Demonstrates through S¹, T^q, SO(n), U(m) systems and equivariant RNN experiments that strict equivariance enhances long-range memory retention, generalization, and stability.
2026-05-03	Planner Matters! An Efficient and Unbalanced Multi-agent Collaboration Framework for Long-horizon Planning
	• Proposes an unbalanced multi-agent collaboration framework with planner, actor, and memory manager roles, showing that planning contributes most to task performance. • Introduces planner-only RL optimization with trajectory-level rewards, validated on web navigation, system control, and tool-use benchmarks for efficient long-horizon automation.
2026-05-02	MemORAI: Memory Organization and Retrieval via Adaptive Graph Intelligence for LLM Conversational Agents
	• Proposes MemORAI, a memory organization and retrieval framework that utilizes selective memory filtering with dual-layer compression to retain user-persona-relevant content and preserve global context. • Constructs a provenance-enriched multi-relational knowledge graph to track factual origins at the turn level, enabling fine-grained and transparent memory auditing. • Introduces Dynamic Weighted PageRank for query-adaptive subgraph retrieval, applying query-conditioned edge weighting to significantly improve context-sensitive retrieval precision and personalized response generation.
2026-05-01	From Unstructured Recall to Schema-Grounded Memory: Reliable AI Memory via Iterative, Schema-Aware Extraction
	• Argues AI memory should be schema-constrained rather than retrieval-based recall, proposing an iterative schema-aware write pipeline with object detection, field detection, and value extraction. • Adds verification, retry, and state control to the ingestion process, outperforming baselines on structured extraction and end-to-end memory tasks requiring stable facts and state updates.
2026-05-01	Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory
	• Proposes MemCoE, a cognition-inspired two-stage optimization framework: stage one induces global memory criteria via contrastive feedback, stage two uses multi-round RL to learn criteria-compliant memory evolution strategies. • Validates on three personalized memory benchmarks, demonstrating improvements in preference memory, robustness, transferability, and efficiency.
2026-04-30	Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses
	• Proposes AHE, an observability-driven closed-loop mechanism that automatically evolves coding-agent harnesses across component, experience, and decision layers. • Compresses large trajectory volumes into usable evidence via self-prediction and outcome verification, significantly improving coding-agent performance with cross-model transferability.
2026-04-30	EviMem: Evidence-Gap-Driven Iterative Retrieval for Long-Term Conversational Memory
	• Introduces EviMem with IRIS closed-loop iterative retrieval that detects evidence gaps via sufficiency assessment and rewrites queries directionally for scattered multi-turn evidence. • Proposes LaceMem hierarchical memory architecture for coarse-to-fine evidence diagnosis, significantly improving temporal and multi-hop accuracy on LoCoMo while reducing retrieval latency.
2026-04-30	MemRouter: Memory-as-Embedding Routing for Long-Term Conversational Agents
	• Proposes MemRouter, replacing per-turn LLM-based memory management with embedding routing to decide which turns should be written to external memory, training only ~12M parameters. • Outperforms LLM-style memory managers on LoCoMo while significantly reducing memory management latency, with retrieval pipeline, prompts, and QA backbone held constant.
2026-04-29	Detecting Clinical Discrepancies in Health Coaching Agents: A Dual-Stream Memory and Reconciliation Architecture
	• Proposes a dual-stream memory architecture for health coaching LLM agents that separates patient self-reports from structured EHR/FHIR records with a reconciliation engine. • Quantifies error cascades from memory extraction and identifies discrepancy types and severity, emphasizing the necessity of memory verification in clinical longitudinal agents.
2026-04-19	Seeing Isn't Believing: Mitigating Belief Inertia via Active Intervention in Embodied Agents
	• Studies belief inertia in embodied agents, where agents ignore new observations and remain anchored to prior beliefs. • Proposes Estimate-Verify-Update, which predicts, verifies, and evidence-updates textual belief states to actively manage beliefs and improve success rates across embodied benchmarks.
2026-04-22	Memanto: Typed Semantic Memory with Information-Theoretic Retrieval for Long-Horizon Agents
	• Proposes Memanto with 13-class typed semantic memory schema, achieving 89.8% on LongMemEval and 87.1% on LoCoMo without knowledge graphs. • Introduces Moorcheh Information-Theoretic Search for index-free, deterministic retrieval under 90ms with zero write latency. • Addresses constraint drift through built-in conflict resolution and temporal versioning with supersede mechanism.
2026-04-19	Memory Intelligence Agent
	• Proposes MIA, a Memory Intelligence Agent framework featuring a Manager-Planner-Executor architecture that addresses storage and retrieval bottlenecks by compressing historical search trajectories into structured non-parametric memory. • Introduces a two-stage alternating reinforcement learning paradigm to align high-level planning with low-level execution, alongside a continual test-time learning mechanism for on-the-fly parametric memory updates. • Incorporates reflection and unsupervised evaluation mechanisms, achieving state-of-the-art performance in deep research tasks and demonstrating strong self-evolution capabilities across diverse benchmarks.
2026-04-26	ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems
	• Introduces ZenBrain, a seven-layer long-term memory architecture for autonomous agents that combines neuroscience-inspired mechanisms — Hebbian learning, FSRS spaced repetition, sleep-cycle consolidation, and Bayesian confidence propagation — released as an open-source, zero-dependency TypeScript library (Apache-2.0). • On LongMemEval-500 it attains 91.3% of oracle accuracy at 1/106 of the token budget, with 12/12 Bonferroni-corrected head-to-head wins over Letta, Mem0, and A-Mem.
2026-04-18	HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents
	• Introduces HeLa-Mem, modeling long-term LLM-agent memory as a dynamic graph inspired by association, consolidation, and spreading activation. • Combines an episodic memory graph with semantic knowledge distilled through Hebbian Distillation, improving LoCoMo performance while saving context tokens.
2026-04-18	Freshness-Aware Prioritized Experience Replay for LLM/VLM Reinforcement Learning
	• Proposes Freshness-Aware Prioritized Experience Replay, adding exponential age decay to priority sampling. • Mitigates stale priorities as LLM/VLM policies change quickly, outperforming standard on-policy methods and ordinary PER on multi-step reasoning, agent, and math tasks.
2026-04-18	MEMRES: A Memory-Augmented Resolver with Confidence Cascade for Agentic Python Dependency Resolution
	• Introduces MEMRES, a memory-augmented agentic Python dependency resolver that uses a multi-level confidence cascade and treats the LLM as a last resort. • Combines self-evolving memory, an error-pattern knowledge base, semantic import analysis, and Python 2 heuristics to improve dependency resolution success.
2026-04-18	Visual Inception: Compromising Long-term Planning in Agentic Recommenders via Multimodal Memory Poisoning
	• Introduces Visual Inception, an attack that embeds triggers in user-uploaded images to poison long-term memory in agentic recommender systems and hijack future planning chains. • Proposes CognitiveGuard, a dual-process defense combining perceptual sanitization and counterfactual consistency checks to detect abnormal memory-driven planning and reduce risk.
2026-04-18	On Safety Risks in Experience-Driven Self-Evolving Agents
	• Studies safety risks in experience-driven self-evolving LLM agents across web and embodied environments, showing that even benign-task experience can reinforce action-taking over refusal. • In mixed benign/malicious tasks, refusal-related experience mitigates safety degradation but causes over-refusal, exposing a safety-utility trade-off in evolving experience memory.
2026-04-18	OASIS: On-Demand Hierarchical Event Memory for Streaming Video Reasoning
	• Introduces OASIS, an on-demand hierarchical event memory framework for streaming video reasoning that organizes long histories into event hierarchies. • Performs short-context reasoning first and triggers semantic retrieval under uncertainty, using high-level intent to guide memory access while reducing noise and token cost.
2026-04-17	AdaExplore: Failure-Driven Adaptation and Diversity-Preserving Search for Efficient Kernel Generation
	• Introduces AdaExplore, which distills repeated execution failures into reusable validity-rule memories for future Triton kernel generation. • Uses tree-structured candidate organization, local repair, and structural regeneration for diversity-preserving search, improving both correctness and performance optimization.
2026-04-17	StageMem: Lifecycle-Managed Memory for Language Models
	• Introduces StageMem, treating language-model memory as a stateful lifecycle process rather than static storage. • Separates memory into transient, working, and persistent layers, using confidence and strength to support low-cost writing, promotion, updating, and eviction while reducing deep-memory pollution.
2026-04-17	Federation over Text: Insight Sharing for Multi-Agent Reasoning
	• Proposes Federation over Text, where agents upload reasoning traces after independent work and a central server semantically aggregates them. • Builds a shared insight repository across tasks and domains, improving downstream accuracy and reducing reasoning cost without gradient updates or supervision.
2026-04-13	The Past Is Not Past: Memory-Enhanced Dynamic Reward Shaping
	• Proposes MEDS, a memory-enhanced dynamic reward shaping framework that incorporates historical behavioral signals into reward design to mitigate error collapse during LLM reinforcement learning. • Constructs an error memory by reusing layer-wise logits as lightweight representations of reasoning trajectories, applying HDBSCAN clustering to dynamically penalize frequently recurring failure patterns. • Demonstrates consistent improvements in both reasoning performance and exploration diversity across multiple math reasoning benchmarks and base models.
2026-04-11	MemCoT: Test-Time Scaling through Memory-Driven Chain-of-Thought
	• Proposes MemCoT, a test-time memory scaling framework that recasts long-context reasoning as iterative, stateful information search. • Uses multi-view long-term memory perception to surface evidence and a task-conditioned short-term memory module to track search history and refine subsequent queries. • Achieves state-of-the-art results across several long-memory reasoning benchmarks.
2026-04-11	SinkTrack: Attention Sink based Context Anchoring for Large Language Models
	• Introduces SinkTrack, a training-free context anchoring method that exploits the model's attention sink at <BOS> to preserve critical context. • Injects key information into the <BOS> representation so the model can retain initial context throughout generation instead of gradually forgetting it. • Delivers consistent gains on text and multimodal tasks while reducing hallucination and context forgetting.
2026-04-11	Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems
	• Proposes CoARS, a self-distilled reinforcement learning framework for co-evolving agentic recommender systems. • Replaces reliance on external text memory with interaction rewards and self-distilled credit assignment that convert multi-turn supervision into parameter updates. • Improves recommendation quality and user alignment over memory-only baselines.
2026-04-11	CodeComp: Structural KV Cache Compression for Agentic Coding
	• Introduces CodeComp, a structural KV-cache compression method for agentic coding tasks. • Combines static program analysis with code property graphs to preserve structurally critical tokens that attention-only pruning often removes. • Outperforms attention-based compression under the same memory budget and integrates into the SGLang pipeline without changing the model.
2026-04-11	ClawVM: Harness-Managed Virtual Memory for Stateful Tool-Using LLM Agents
	• Proposes ClawVM, a harness-managed virtual memory layer for stateful tool-using LLM agents. • Organizes agent state as typed pages with multi-resolution representations and validates writeback at lifecycle boundaries to avoid stale or destructive state updates. • Significantly reduces controllable failures while adding very little policy-engine overhead.
2026-04-11	CodaRAG: Connecting the Dots with Associativity Inspired by Complementary Learning
	• Presents CodaRAG, an associative retrieval framework inspired by complementary learning for RAG systems. • Consolidates fragmented knowledge, navigates semantic and functional links to recover evidence chains, and suppresses distractors during retrieval. • Improves both retrieval recall and generation accuracy over prior methods.
2026-04-11	Astrolabe: A Content-Addressable Hypergraph for Semantic Knowledge Management
	• Introduces Astrolabe, a content-addressable hypergraph for semantic knowledge management. • Uses content hashes and ordered reference lists to store structured knowledge, while a plugin system interprets records and decomposes structures by width or depth. • Demonstrates the extensibility of the design through plugins that bridge informal and formal mathematics.
2026-04-10	MemReader: From Passive to Active Extraction for Long-Term Agent Memory
	• Proposes the MemReader family for long-term agent memory extraction, covering both passive structured extraction and active write decisions. • MemReader-4B uses GRPO and a ReAct-style process to decide whether information should be written, deferred, retrieved, or discarded. • Outperforms prior methods on knowledge updating, temporal reasoning, and hallucination reduction, and has been integrated into MemOS.
2026-04-10	HyperMem: Hypergraph Memory for Long-Term Conversations
	• Introduces HyperMem, a hypergraph-based memory architecture for long-term conversation. • Models higher-order relations through hyperedges, organizes memory into topic, event, and fact layers, and couples lexical-semantic indexing with coarse-to-fine retrieval. • Improves recall and consistency on long-conversation tasks and achieves the best results on LoCoMo.
2026-04-10	StreamMeCo: Long-Term Agent Memory Compression for Efficient Streaming Video Understanding
	• Proposes StreamMeCo for long-term memory compression in streaming video understanding. • Compresses memory graphs through edge-aware pruning and isolated-node sampling, and adds time-decayed retrieval to offset compression loss. • Delivers large memory savings and faster retrieval while maintaining or improving accuracy on multiple benchmarks.
2026-04-10	Towards Lifelong Aerial Autonomy: Geometric Memory Management for Continual Visual Place Recognition in Dynamic Environments
	• Introduces a geometric memory management framework for continual visual place recognition in dynamic environments. • Decomposes knowledge into static satellite anchors and a dynamic replay buffer, then allocates limited storage with spatially constrained sampling. • Improves long-term retention and spatial generalization, and establishes a 21-task benchmark for evaluation.
2026-04-10	SPASM: Stable Persona-driven Agent Simulation for Multi-turn Dialogue Generation
	• Proposes SPASM, a stable persona-driven framework for multi-turn dialogue generation. • Uses persona creation, client-responder simulation, and an ECP representation that stores dialogue history in a viewpoint-invariant form before projecting it back to each agent. • Reduces persona drift, role confusion, and echoing across long conversations and releases a large-scale dialogue dataset.
2026-04-10	SkillMOO: Multi-Objective Optimization of Agent Skills for Software Engineering
	• Introduces SkillMOO, a multi-objective optimization framework for software engineering agent skills. • Combines LLM-generated edits with NSGA-II selection to evolve skill bundles based on task failures and cost-performance trade-offs. • Improves pass rates while lowering cost, and shows that replacing or pruning instructions can outperform simply accumulating them.
2026-04-10	RecaLLM: Addressing the Lost-in-Thought Phenomenon with Explicit In-Context Retrieval
	• Proposes RecaLLM, which interleaves explicit in-context retrieval with reasoning to address the lost-in-thought problem. • Uses constrained decoding to copy evidence spans and trains on both lexical and semantic retrieval tasks. • Outperforms baselines on long-context benchmarks such as RULER and HELMET and scales to 128K contexts with relatively modest training data.
2026-04-10	Constraint-Aware Corrective Memory for Language-Based Drug Discovery Agents
	• Introduces a constraint-aware corrective memory framework for language-based drug discovery agents. • Audits protocols and diagnoses constraint violations, then writes compact corrective feedback into static, dynamic, and correction memory channels. • Significantly improves task success rates by keeping planning context compact and actionable.
2026-04-10	ADAM: A Systematic Data Extraction Attack on Agent Memory via Adaptive Querying
	• Presents ADAM, a systematic data extraction attack on agent memory via adaptive querying. • Estimates the memory distribution of the victim and uses entropy-guided queries to maximize leakage. • Achieves much higher attack success than prior methods, reaching up to 100% in some settings and highlighting major privacy risks in agent memory.
2026-04-10	EE-MCP: Self-Evolving MCP-GUI Agents via Automated Environment Generation and Experience Learning
	• Proposes EE-MCP, a self-evolving framework for MCP-GUI agents. • Builds a closed loop of environment generation, trajectory collection, task synthesis, and quality filtering, then stores distilled rules in an experience bank for inference-time improvement. • Shows that experience-enhanced agents outperform pure distillation in GUI-intensive settings without requiring model fine-tuning.
2026-04-10	MEMENTO: Teaching LLMs to Manage Their Own Context
	• Introduces MEMENTO, which teaches LLMs to manage their own context by summarizing long reasoning traces into dense intermediate mementos. • Splits reasoning into blocks, generates compact summaries for each block, and continues reasoning from the summaries instead of the full trace. • Reduces context length, KV cache usage, and compute cost while preserving reasoning quality, and releases the OpenMementos dataset.
2026-04-10	Formal Architecture Descriptors as Navigation Primitives for AI Coding Agents
	• Studies formal architecture descriptors as navigation primitives for AI coding agents. • Shows that structured architecture context can reduce aimless repository exploration and improve behavioral consistency across agent runs. • Compares JSON, YAML, and S-expression formats, and introduces intent.lisp and the Forge toolchain.
2026-04-09	Task-Adaptive Retrieval over Agentic Multi-Modal Web Histories via Learned Graph Memory
	• Introduces ACGM, a learned graph memory retriever for task-adaptive retrieval over long multimodal web histories. • Optimizes sparse relevance graphs with policy gradients and models different temporal decay patterns across visual and textual observations. • Outperforms multiple baselines on WebShop, VisualWebArena, and Mind2Web.
2026-04-09	TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation
	• Proposes TSUBASA for long-horizon personalization with evolving memory and self-learning. • Improves memory writing through dynamic memory evolution and strengthens memory reading through context-distilled self-learning that internalizes user experience. • Outperforms systems such as Mem0 on long-horizon personalization benchmarks while achieving a better quality-efficiency trade-off.
2026-04-09	Dynamic Attentional Context Scoping: Agent-Triggered Focus Sessions for Isolated Per-Agent Steering in Multi-Agent LLM Orchestration
	• Introduces DACS, a dynamic attentional context scoping mechanism for multi-agent LLM orchestration. • Keeps only lightweight state summaries in registry mode and switches into a focus mode that injects full context for the selected agent while compressing others. • Improves steering accuracy and reduces interference from irrelevant agents.
2026-04-09	LogAct: Enabling Agentic Reliability via Shared Logs
	• Proposes LogAct, a shared-log execution framework that turns agents into state machines over an explicit log. • Makes planned actions visible before execution, enabling intervention, recovery, semantic replay, and LLM-based introspection from historical traces. • Improves reliability, fault recovery, and debugging for agentic systems.
2026-04-09	PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory
	• Introduces the DD-MM-PAS paradigm and the Pask system for intent-aware proactive agents with long-term memory. • Combines IntentFlow for latent-need detection, a hybrid workspace-user-global memory design, and a full perception-memory-action infrastructure. • Achieves deeper user-intent recognition under low-latency constraints and releases LatentNeeds-Bench.
2026-04-09	Beyond Stochastic Exploration: What Makes Training Data Valuable for Agentic Search
	• Proposes HiExp, a framework that extracts reusable experience knowledge from raw agentic search trajectories. • Uses comparative analysis and hierarchical clustering to organize trajectories into layered experience representations and trains agents with experience alignment. • Turns stochastic exploration into more strategic search and improves stability, task performance, and cross-task generalization.
2026-04-09	"Theater of Mind" for LLMs: A Cognitive Architecture Based on Global Workspace Theory
	• Introduces Global Workspace Agents, a cognitive architecture for LLM agents inspired by global workspace theory. • Combines a central broadcast hub with heterogeneous agents, plus entropy-driven intrinsic motivation and a two-tier memory branching strategy. • Improves semantic diversity and long-term cognitive continuity in multi-agent execution.
2026-04-09	ACF: A Collaborative Framework for Agent Covert Communication under Cognitive Asymmetry
	• Proposes ACF, a collaborative framework for covert communication among memory-augmented agents under cognitive asymmetry. • Decouples covert signaling from semantic reasoning and uses prefix-independent decoding with shared steganographic configurations to avoid symmetry assumptions. • Maintains semantic consistency and covert communication quality even under severe asymmetry, with theoretical error and capacity guarantees.
2026-04-09	Distributed Multi-Layer Editing for Rule-Level Knowledge in Large Language Models
	• Studies rule-level knowledge editing in LLMs and shows that rule knowledge is distributed across layers rather than concentrated locally. • Extends the RuleEdit benchmark and uses causal tracing to distinguish how formulas, descriptions, and instances are represented in different layers. • Proposes DMLE, which improves rule transfer and understanding while remaining competitive on standard editing metrics.
2026-04-09	SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
	• Proposes SkillClaw for collective skill evolution in multi-user LLM agent environments. • Continuously aggregates user trajectories, detects repeated behaviors and failure modes, and converts them into skill revisions or new skills for a shared repository. • Enables cross-user experience transfer and improves performance in real agent settings.
2026-04-09	Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing
	• Introduces SAVeR, a self-auditing framework for faithful reasoning in LLM agents. • Generates diverse candidate beliefs, audits them adversarially to locate violations, and applies minimal constrained edits before the agent commits to action. • Reduces behavior drift caused by unfaithful reasoning while preserving task performance.
2026-04-09	SkillForge: Forging Domain-Specific, Self-Evolving Agent Skills in Cloud Technical Support
	• Proposes SkillForge, a framework for building and self-evolving domain-specific agent skills in cloud technical support. • Combines domain-context skill creation with a closed loop of failure analysis, skill diagnosis, and skill rewriting driven by deployment feedback. • Improves initial skill quality and continues to raise performance over repeated deployment cycles.
2026-04-09	Efficient RL Training for LLMs with Experience Replay
	• Revisits experience replay for LLM post-training and questions the assumption that only fresh on-policy data is useful. • Analyzes the trade-off between staleness, diversity, and generation cost in replay buffer design. • Shows that well-designed replay buffers can cut inference cost substantially without hurting and sometimes even improving performance.
2026-04-09	Artifacts as Memory Beyond the Agent Boundary
	• Formalizes the idea that the environment itself can serve as external memory for an agent. • Introduces artifacts as observations that reduce the information needed to represent history and connects the idea to reinforcement learning theory. • Shows that observing spatial paths can lower the internal memory needed to learn strong policies and motivates future environment-backed memory designs.
2026-04-09	MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation
	• Proposes MT-OSC, a framework that condenses multi-turn chat history in the background to preserve salient information. • Uses a few-shot condenser and a lightweight decision module to retain key facts while compressing irrelevant content. • Cuts token usage by up to 72% while maintaining or improving multi-turn dialogue accuracy and latency.
2026-04-09	M^\star: Every Task Deserves Its Own Memory Harness
	• Proposes M^\star, which represents agent memory as executable Python memory programs rather than fixed schemas. • Evolves task-specific memory data structures, storage logic, and interaction workflows through reflective code evolution guided by population search and failure analysis. • Outperforms fixed memory baselines across dialogue, embodied planning, and expert reasoning benchmarks while discovering markedly different memory designs per task.
2026-04-08	From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI
	• Proposes LOM-action, an ontology-governed graph simulation framework for auditable enterprise AI decisions. • Triggers scenario conditions from business events, performs deterministic graph transformations in isolated sandboxes, and bases decisions only on the resulting scenario-valid graph. • Improves accuracy and tool-chain F1 over baselines while generating fully traceable audit logs.
2026-04-08	LAST: Leveraging Tools as Hints to Enhance Spatial Reasoning for Multimodal Large Language Models
	• Proposes LAST, a tool-as-hints framework for improving multimodal spatial reasoning. • Packages heterogeneous tool invocations into reusable spatial skills through LAST-Box and returns multimodal prompts that can be consumed directly by the model. • Uses a three-stage curriculum to teach adaptive tool use and significantly improves performance on four spatial reasoning datasets.
2026-04-04	LightThinker++: From Reasoning Compression to Memory Management
	• The paper introduces LightThinker, a method that achieves efficiency through representation-level thought compression by condensing lengthy reasoning chains into compact hidden states via gist tokens. • It proposes LightThinker++, an Explicit Adaptive Memory Management framework that utilizes behavioral primitives such as commit, expand, and fold to dynamically regulate context resolution and mitigate information loss in complex scenarios. • The work establishes a specialized trajectory synthesis pipeline to train purposeful memory scheduling, enabling significant reductions in peak token usage and performance gains in both standard reasoning and long-horizon agentic tasks.
2026-04-18	Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents
	• Unifies agent memory, skill discovery, and rule learning as different levels of experience compression, defining a compression function C_L mapping traces to knowledge at levels L0-L3 (raw trace → episodic memory → procedural skill → declarative rule). • Reveals that the memory and skill communities are deeply fragmented with only <1% cross-citation rate among 22 core papers (1136 citations), and proposes a full-spectrum agent learning system that adaptively selects compression granularity across levels. • Optimization targets include reducing context consumption, lowering retrieval latency, and improving cross-task/model/scenario migration ability; the workflow follows a cyclic pattern where new problems go to memory, repeated patterns compress to skills, and cross-scenario principles become rules.
2026-04-03	Poison Once, Exploit Forever: Environment-Injected Memory Poisoning Attacks on Web Agents
	• The paper introduces Environment-injected Trajectory-based Agent Memory Poisoning (eTAMP), the first attack to achieve cross-session and cross-site compromise of web agents through environmental observations without requiring direct memory access. • It discovers Frustration Exploitation, a phenomenon where environmental stress and task failures significantly amplify an agent's susceptibility to malicious instructions by up to eight times. • The work introduces Chaos Monkey, a methodology inspired by chaos engineering, to systematically evaluate the robustness of LLM-based web agents under realistic deployment conditions such as network latency and input errors.
2026-04-03	Aligning Progress and Feasibility: A Neuro-Symbolic Dual Memory Framework for Long-Horizon LLM Agents
	• Identifying long-horizon agent failures as arising from two coupled but distinct objectives: global progress alignment and local feasibility alignment. • Proposing the Neuro-Symbolic Dual Memory Framework that instantiates a neural Progress Memory for stage-aware semantic guidance and a symbolic Feasibility Memory for executable action verification. • Demonstrating superior performance across diverse benchmarks including ALFWorld, WebShop, and TextCraft while significantly reducing invalid action rates and trajectory lengths.
2026-04-02	ByteRover: Agent-Native Memory Through LLM-Curated Hierarchical Context
	• Proposes an agent-native memory architecture where the LLM itself curates, structures, and retrieves knowledge, eliminating the architectural separation and subsequent semantic drift between reasoning agents and external storage pipelines. • Introduces the Context Tree, a hierarchical file-based knowledge graph managed by an Adaptive Knowledge Lifecycle (AKL) that employs importance scoring, maturity tiers, and recency decay to evolve knowledge over time. • Designs a 5-tier progressive retrieval strategy that minimizes latency by resolving queries through multi-level caching and indexing before escalating to agentic reasoning for novel questions.
2026-04-02	MemFactory: Unified Inference & Training Framework for Agent Memory
	• MemFactory presents the first comprehensive framework that unifies the training, evaluation, and inference pipelines for memory-augmented AI agents. • The framework features a highly modular architecture that decouples the memory lifecycle into atomic, plug-and-play components such as Extractors, Updaters, and Retrievers. • It natively incorporates Group Relative Policy Optimization (GRPO) to facilitate efficient fine-tuning of internal memory management policies driven by multi-dimensional environmental rewards.
2026-04-02	MEMRERANK: Preference Memory for Personalized Product Reranking
	• Introduction of a new benchmark for personalized product reranking that incorporates user purchase histories, candidate sets, and human-annotated relevance labels. • Development of the MEMRERANK framework that distills long purchase histories into structured, query-independent within-category and cross-category preference memory. • Implementation of a reinforcement learning post-training objective to optimize the preference memory extractor for downstream reranking utility.
2026-04-02	OMNI-SIMPLEMEM: Autoresearch-Guided Discovery of Lifelong Multimodal Agent Memory
	• OMNI-SIMPLEMEM is proposed as a unified multimodal memory framework for lifelong AI agents that utilizes selective ingestion , progressive pyramid retrieval , and structured knowledge graph augmentation. • The system architecture and configurations were autonomously discovered and optimized through AUTORESEARCHCLAW , an autonomous research pipeline capable of code modification, bug diagnosis, and architectural redesign fundamentally beyond the reach of traditional AutoML. • The framework achieves state-of-the-art results on LoCoMo and Mem-Gallery benchmarks , demonstrating that autonomously identified bug fixes and architectural changes contribute more significantly to performance gains than cumulative hyperparameter tuning.
2026-04-02	SelRoute: Query-Type-Aware Routing for Long-Term Conversational Memory Retrieval
	• Introduction of SelRoute, a selective routing framework that assigns queries to specialized retrieval pipelines based on their specific query types to optimize long-term conversational memory retrieval. • Identification of the enrichment-embedding asymmetry, demonstrating that storage-time vocabulary expansion improves lexical search performance while simultaneously degrading embedding search quality. • Achievement of state-of-the-art results on LongMemEval_M using a CPU-only architecture that requires no LLM inference at query time and generalizes across multiple benchmarks.
2026-03-31	Multi-Layered Memory Architectures for LLM Agents: An Experimental Evaluation of Long-Term Context Retention
	• Proposes a Multi-Layer Memory Framework (MLMF) that decomposes dialogue history into working, episodic, and semantic layers to separate short-term interaction from long-term abstraction. • Introduces an adaptive retrieval gating mechanism and a retention stability objective to regulate semantic drift and preserve persona consistency across extended sessions. • Validates the framework through experiments on long-horizon benchmarks, achieving superior retention stability and multi-hop reasoning while reducing context usage and false memory rates.
2026-03-31	OBLIVION: Self-Adaptive Agentic Memory Control through Decay-Driven Activation
	• OBLIVION introduces a read/write decoupled memory control paradigm that separates the decision of when to retrieve information from the selection of what to reinforce. • The framework implements a hierarchical memory structure consisting of L1 procedural, L2 semantic, and L3 episodic layers integrated with Ebbinghaus-inspired decay-driven activation to manage accessibility without explicit deletion. • Empirical evaluations on static and dynamic benchmarks demonstrate that self-adaptive memory control effectively balances learning and forgetting while significantly reducing interference and computational costs in long-horizon interactions.
2026-03-30	GEMS: Agent-Native Multimodal Generation with Memory and Skills
	• GEMS is proposed as an agent-native multimodal generation framework that utilizes a structured multi-agent loop for iterative refinement and closed-loop optimization. • A persistent Agent Memory mechanism is introduced to manage historical context through hierarchical compression, preserving factual artifacts while distilling high-level strategic experiences. • An extensible Agent Skill module is developed to provide domain-specific expertise via an on-demand loading mechanism, effectively addressing specialized downstream applications.
2026-03-30	Understand and Accelerate Memory Processing Pipeline for Disaggregated LLM Inference
	• Diverse long-context large language model (LLM) inference optimizations are unified into a common four-step memory processing pipeline, which is identified as a primary performance bottleneck through systematic profiling. • This research characterizes the quantitative and qualitative computational heterogeneity across pipeline stages, distinguishing between compute-bound regular operations and memory-bound irregular tasks. • A GPU-FPGA heterogeneous system is developed to accelerate inference by offloading irregular and memory-bounded operations to FPGAs while retaining compute-intensive tasks on GPUs, achieving significant speedups and energy savings.
2026-03-28	Codebase-Memory: Tree-Sitter-Based Knowledge Graphs for LLM Code Exploration via MCP
	• CODEBASE-MEMORY provides a persistent, Tree-Sitter-based knowledge graph architecture supporting 66 languages through a multi-phase parallel build pipeline and incremental synchronization. • The system introduces an MCP-based tool interface exposing 14 structural query tools, such as call-path tracing and impact analysis, with sub-millisecond query latency. • Empirical evaluation across 31 repositories demonstrates that the approach achieves competitive answer quality while reducing token consumption by ten times and tool calls by 2.1 times compared to traditional file-exploration agents.
2026-03-27	Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems
	• A joint scaling perspective is introduced for LLM multi-agent systems that connects team size and lifelong learning as an interacting scaling space rather than isolated dimensions. • LLMA-Mem is proposed as a lifelong memory framework that integrates episodic, procedural, and transactive memory modules to enable cross-task transfer and coordination modeling under flexible memory topologies. • Systematic empirical evaluations reveal a non-monotonic scaling landscape where effective memory design enables smaller teams to achieve superior long-horizon performance and token efficiency compared to larger collectives.
2026-03-27	MemBoost: A Memory-Boosted Framework for Cost-Aware LLM Inference
	• Proposes MemBoost, a memory-boosted LLM serving framework that integrates an Associative Memory Engine, a Meta Controller, and a Large-LLM Oracle to optimize the cost-quality trade-off. • Introduces a retrieve-or-escalate decision loop combined with a continuous write-back mechanism to enable efficient semantic answer reuse and memory growth. • Demonstrates through experiments on the MMLU-Pro dataset that the framework significantly reduces inference costs and latency while achieving accuracy comparable to or exceeding the oracle model.
2026-03-20	PersonaVLM — Long-Term Personalized Multimodal LLMs
	• Proposes PersonaVLM, an innovative personalized multimodal agent framework that transforms a general-purpose MLLM (e.g., Qwen2.5-VL) into a personalized assistant via proactive memory management and self-evolving personality alignment. • Implements a multi-type memory architecture (core, semantic, episodic, procedural) for complex multi-turn reasoning, with proactive remembering that automatically extracts and summarizes multimodal interaction fragments into a persistent personalized database. • Introduces Momentum-based Personality Evolving (PEM) mechanism for consistent response generation, and releases Persona-MME benchmark (2,000+ cases across 14 fine-grained tasks, CVPR 2026) along with full model weights, 80k+ training samples, and evaluation code.
2026-03-19	MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution
	• Proposes MEMMA, a plug-and-play multi-agent framework that coordinates the memory cycle along both the forward and backward paths to address strategic blindness and delayed feedback. • Introduces a Meta-Thinker for strategic reasoning during construction and iterative retrieval, and designs an in-situ self-evolving memory mechanism to convert downstream probe failures into direct memory repairs.
2026-03-11	Governing Evolving Memory in LLM Agents: Risks, Mechanisms, and the Stability and Safety Governed Memory (SSGM) Framework
	• The memory systems of large-model agents are shifting from static retrieval to dynamic autonomous updating, which enhances agent adaptability but also raises serious stability and security concerns. These include semantic drift, program drift that solidifies erroneous workflows, and memory poisoning caused by malicious external injections. • It proposes the Stable and Secure Governance Memory (SSGM) framework. The core design principle of this framework is to completely decouple an agent’s “generative cognitive strategies” from the underlying memory storage medium. Between the two, it introduces an actively intercepting governance middleware, so that memory updates are no longer blindly written directly, but must instead pass through multiple gateway checks. • Pre-merge validation performs logical consistency checks before writes, rejecting updates that contradict core facts to prevent hallucinations from being solidified. Temporal and permission filtering combines decay functions at read time to filter out outdated or invalid data and uses access control to prevent cross-user privacy leakage. Reversible periodic alignment adopts a dual-track storage structure of “mutable activity graph + immutable situational log,” whereby the system regularly aligns current memory with the immutable log and rolls back errors, thereby imposing a strict mathematical upper bound on long-term semantic drift.
2026-03-11	Taming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threats
	• The paper systematically analyzes the security threats faced by autonomous large language model agents such as OpenClaw across five lifecycle stages: initialization, input, reasoning, decision-making, and execution. • A detailed case study on OpenClaw demonstrates the destructiveness of these threats. For example, an attacker can turn transient malicious inputs into long-term behavioral control through “memory poisoning”; and during the decision-making and execution stages, ambiguous instructions may trigger “intent drift,” causing the agent to escalate a simple safety-check task into destructive firewall modifications and high-risk command execution. • Mitigation strategies include: plugin verification and signing in the initialization phase; semantic firewall isolation in the input phase; dynamic memory integrity checks and state rollback in the reasoning phase; intent consistency verification in the decision-making phase; and kernel-level sandboxing and least-privilege control in the execution phase.
2026-03-12	ReMem-VLA: Empowering Vision-Language-Action Model with Memory via Dual-Level Recurrent Queries
	• ReMem-VLA introduces two sets of learnable recurrent memory queries: frame-level queries, which are updated frame by frame to capture short-term memory, and chunk-level queries, which are updated over longer temporal spans to stably maintain long-term memory. • A visual prediction head is added, introducing past observation prediction as an auxiliary training objective, which forces the model to recall visual details by reconstructing historical RGB frames. To address the batching challenge of recurrent training on variable-length sequences, a slot-based streaming training paradigm is proposed, which preserves temporal continuity while preventing state leakage across episodes. • To overcome the bottleneck of traditional truncated backpropagation through time on long-sequence optimization, the model creatively adopts a gradient-free recurrent update path that combines a frozen VLM with a fixed exponential moving average, allowing the queries to focus solely on learning “what task-relevant information to extract” rather than “how to propagate it.”
2026-03-11	Think While Watching: Online Streaming Segment-Level Memory for Multi-Turn Video Reasoning in Multimodal Large Language Models
	• Existing streaming multimodal large models typically adopt a serial “perception–generation alternation” paradigm, where text decoding blocks the continuous intake of video, and as long videos progress, the model is prone to forgetting key information from earlier segments. To address this, this paper proposes a novel streaming inference framework that “thinks while watching.” • The framework divides a video into multiple segments and, during system operation, dynamically generates and maintains persistent segment-level memory notes online, which support multi-turn question answering via implicit retrieval. • Constructs a dedicated three-stage streaming chain-of-thought (CoT) dataset—covering single-turn adaptation, multi-turn interaction, and long-range capability training—and pairs it with segment-level streaming causal masks to ensure strict temporal causality.
2026-03-09	MEMO: Memory-Augmented Model Context Optimization for Robust Multi-Turn Multi-Agent LLM Games
	• In long-horizon multi-agent games, early mistakes are easily amplified, and fixed prompts can lead to rigid strategies and highly variable evaluation results. To address this, this paper proposes MEMO, a self-play framework that requires no updates to model weights. • MEMO cleverly decouples and combines the mechanisms of “retention” and “exploration.” It builds a persistent memory bank that uses CRUD (create, read, update, delete) operations to extract structured strategic insights from self-play trajectories and injects them as prior knowledge for subsequent reasoning; meanwhile, it employs tournament-style prompt evolution based on TrueSkill ratings and a prioritized experience replay mechanism to efficiently explore strategies and revisit critical decision states. • In five text-based game benchmarks, MEMO demonstrates remarkable learning efficiency: with just 2,000 self-play episodes, it boosts GPT-4o-mini’s average win rate from 25.1% to 49.5%, while simultaneously causing a substantial reduction in the variance of its performance.
2026-03-12	Collaborative Multi-Agent Optimization for Personalized Memory System
	• Existing personalized large-model memory systems are typically composed of multiple agents, but most prior methods optimize them independently and locally, neglecting cross-agent collaboration, which means that locally optimal performance cannot guarantee the best global question-answering performance of the overall system. • To address the optimization challenges caused by asynchronous execution of heterogeneous agents, the paper proposes the CoMAM framework, which models the execution pipelines of agents—such as fine-grained extraction, coarse-grained profiling, and memory retrieval—as a sequential Markov decision process. • To align local task improvements with global system performance, CoMAM quantifies each agent’s contribution by computing the group-level ranking consistency between its local rewards and the global system reward.
2026-03-13	Structured Distillation for Personalized Agent Memory: 11× Token Reduction with Retrieval Preservation
	• It proposes extracting each conversational interaction into a structured composite object that includes core content, specific context, topic classification, and related files. This approach follows a “surviving vocabulary” principle, avoiding arbitrary rewriting of technical terms, and successfully compresses the average number of tokens per interaction from 371 to 38, achieving an 11× compression efficiency. • In experiments covering 107 retrieval configurations, it was found that the best pure distilled-text setup can retain 96% of the retrieval quality (MRR) of the original verbatim text. Furthermore, retrieval performance is highly mechanism-dependent: vector search shows virtually no noticeable degradation even under 11× compression, whereas keyword search (BM25) degrades significantly. • The agent carries only the compressed distilled text within the context as a “routing index” for efficient retrieval, while the original full conversation text is stored locally and is only brought up for display when the user needs to inspect it in detail.
2026-03-09	TA-Mem: Tool-Augmented Autonomous Memory Retrieval for LLM in Long-Term Conversational QA
	• Structured episodic memory extraction: TA-Mem introduces an episodic memory extraction agent that uses one-shot prompting to adaptively chunk long contexts based on semantic topic shifts, extracting them into structured memory notes containing summaries, keywords, entities, and events. • Tool-driven autonomous memory exploration: It constructs a multi-indexed memory database supporting diverse query methods (e.g., string matching and vector similarity). A tool-augmented retrieval agent can autonomously select appropriate tools and run iterative agentic loops to explore the memory space. • Enhanced reasoning and token efficiency: By leveraging precise tool calls and a per-session memory caching mechanism to filter out redundant context, the framework significantly outperforms existing baselines on complex long-range inference tasks (such as the LoCoMo benchmark) while maintaining high token efficiency.
2026-03-09	EVOKING USER MEMORY: PERSONALIZING LLM VIA RECOLLECTION-FAMILIARITY ADAPTIVE RETRIEVAL
	• It points out that current memory retrieval in personalized large models either feeds in the entire history, causing context overload, or relies only on a single similarity search, leading to shallow understanding. To address this, the research team draws inspiration from the dual-process theory of human memory (“recollection–familiarity”) and proposes an adaptive memory retrieval framework called RF-Mem. • RF-Mem measures “familiarity” by probing the average similarity scores and entropy of the retrieval. When familiarity is high and uncertainty is low, the system takes a fast “familiarity route,” directly returning the single top-K result; when familiarity is low and uncertainty is high, it instead activates a deeper “recollection route.” • When the “recollection route” is triggered, the system clusters the candidate memories and uses an α-mix strategy to update the original query by blending it with the cluster centroids.
2026-03-10	A Control-Theoretic Foundation for Agentic Systems
	• It proposes a control-theoretic framework that embeds AI agent systems into a feedback control loop for analysis. Rather than treating AI memory, learning, tool invocation, interaction signals, and goal specification as isolated, fragmented functions, this framework unifies and integrates them into a single closed-loop dynamical architecture. • It creatively defines a five-level hierarchy of agents based on the extent of “decision authority” that AI holds within the control architecture. • Applying this framework to both nonlinear and linear systems, the paper points out that as the agent hierarchy increases, the system will inevitably introduce more complex dynamical mechanisms, such as time-varying adaptation, endogenous switching, decision-induced delays, and structural reconfiguration of the control pipeline.
2026-03-11	When OpenClaw Meets Hospital: Toward an Agentic Operating System for Dynamic Clinical Workflows
	• Constrain the agents to an isolated environment where they are only allowed to read and write specific files and invoke a pre-vetted “medical skill library,” cutting off arbitrary code execution and network access at the operating-system level to ensure data security and compliance. • Instead of traditional vector retrieval, clinical documents are organized into a tree structure augmented with manifests. Relying on natural language understanding to read these manifests, the agents perform “progressive disclosure” navigation, thereby acquiring long-term medical record context in a precise and interpretable manner. • Instead of communicating directly, multiple agents collaborate implicitly by append-only writes to shared clinical documents and event subscriptions. This allows the agents to perform on-the-fly task orchestration and flexibly handle complex, long-tail clinical needs that traditional systems cannot address.
2026-03-10	Trajectory-Informed Memory Generation for Self-Improving Agent Systems
	• To address the agents’ “amnesia” problem, it systematically and automatically extracts reusable practical experience from their historical execution traces, including clean successes, inefficient successes, failures, and error recoveries. • It proposes a complete learning feedback loop that includes trajectory intelligence extraction, decision attribution analysis, contextual learning generation, and adaptive memory retrieval. • It achieves comprehensive improvements in agent task performance on the AppWorld benchmark, with particularly pronounced gains on long-horizon tasks that require complex planning.
2026-03-09	AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents
	• It structures the agent’s cognition into “internal cognition” and “external cognition.” This cognition, treated as an updatable state of the agent, is continuously self-corrected and evolved based on the actual outcomes of task interactions, thereby providing a more accurate and less biased knowledge foundation for decision-making. • It adopts a dynamic “select–execute–update” loop for problem solving. The action space is uniformly divided into “emic actions” (self-driven) and “etic actions” (collaborative), enabling the agent to adaptively plan its next step entirely based on the current context and real-time cognition. • To tackle context redundancy and token consumption in long-sequence reasoning, the system designs an Elastic Memory Orchestrator (EMO) that dynamically compresses historical trajectories, filters redundant information, and extracts reusable episodic memories.
2026-03-09	Multi-Agent Memory from a Computer Architecture Perspective: Visions and Challenges Ahead
	• It compares the multi-agent memory system to a classic computer system, distinguishing between two basic architectural prototypes: shared memory and distributed memory. • It proposes an architecture-inspired three-layer memory hierarchy (I/O layer, cache layer, and memory layer), and identifies two critical missing protocols: an agent cache sharing protocol, and an agent memory access protocol that regulates read/write permissions and granularity. • In the future, the most pressing challenge in building multi-agent systems is ensuring memory consistency. This requires the system to properly handle read-time conflicts, the visibility and ordering of update operations when multiple agents concurrently read and write shared memory, and to establish clear versioning and conflict resolution rules in order to maintain coherence of the global context.
2026-03-04	Adaptive Memory Admission Control for LLM Agents
	• The absence of memory admission control allows long-term memory stores to be rapidly polluted by low-quality, redundant, or ineffective noise features, ultimately undermining multi-turn reasoning. • Introduces Adaptive Memory Admission Control (A-MAC), a structured admission mechanism that uses lightweight estimation of five interpretable factors—such as future utility, confidence, and novelty—to rigorously intercept write operations before they occur. • Establishes admission control as a core engineering principle in memory system design, cleansing the memory store at the source and delivering simultaneous latency and performance gains on the LoCoMo benchmark.
2026-03-02	MemSifter: Offloading LLM Memory Retrieval via Outcome-Driven Proxy Reasoning
	• When executing long-horizon tasks, repeatedly invoking the primary model for complex memory retrieval is prohibitively expensive, making it difficult to balance computational efficiency and accuracy. • Proposes the MemSifter framework, which leverages an outcome-driven reinforcement learning paradigm to offload retrieval-intensive computation directly to a lightweight proxy model. • Achieves low-cost decoupling of the retrieval architecture, attaining state-of-the-art retrieval hit rates with minimal inference overhead, and offering a highly scalable industrial solution for long-term memory.
2026-03-02	GAM-RAG: Gain-Adaptive Memory for Evolving Retrieval in Retrieval-Augmented Generation
	• Traditional RAG systems rely on static indexing structures, which introduce substantial traversal redundancy and computational waste when handling frequently recurring queries. • Inspired by cognitive science, the paper proposes the training-free GAM-RAG framework, which uses Kalman gain rules to dynamically strengthen memory states for repeated retrieval based on query feedback. • Successfully balances index stability and adaptability, effectively avoiding unproductive retrieval while substantially reducing inference compute overhead without sacrificing accuracy.
2026-03-02	Diagnosing Retrieval vs. Utilization Bottlenecks in LLM Agent Memory
	• Clearly identifies a prevailing optimization misconception in current memory systems: the true impact of the writing, retrieval, and utilization stages on overall performance must be quantitatively diagnosed. • Develops a diagnostic framework that performs rigorous cross-ablation studies of mainstream writing and retrieval strategies, isolating and disentangling the contribution of each stage. • Demonstrates that retrieval is the dominant performance bottleneck by a wide margin (with an impact weight of up to 20%), and that raw chunk-based storage remains highly effective, guiding the industry to prioritize retrieval enhancement over complex compression schemes.
2026-02-28	MemPO: Self-Memory Policy Optimization for Long-Horizon Agents
	• Long-horizon agents passively depend on external RAG retrieval and lack autonomous mechanisms for assessing the value of historical information and managing what should be retained. • Proposes the Self-Memory Policy Optimization algorithm (MemPO), which introduces an improved credit assignment mechanism to grant the model the ability to proactively summarize and filter high-value memories. • By precisely removing redundant information, the method significantly reduces token consumption while comprehensively outperforming prior approaches in both F1 score and overall task performance.
2026-02-26	ParamMem: Augmenting Language Agents with Parametric Reflective Memory
	• Introduces ParamMem, a parameterized memory module that addresses output repetition caused by self-reflection through diversified reflection generation. • Employs temperature-controlled sampling and cross-sample memory techniques to build the high-performance ParamAgent framework. • Significantly outperforms existing baselines on tasks such as code generation and mathematical reasoning, validating the positive impact of reflection diversity on task success rates.
2026-02-26	Tell Me What To Learn: Generalizing Neural Memory to be Controllable in Natural Language
	• Innovatively proposes a general neural memory system whose updates can be controlled via natural language instructions. • Addresses the limitation of traditional models that prevent users from intervening in memory content, enabling agents to selectively learn from heterogeneous sources. • This approach shows great application potential in scenarios such as healthcare and customer service, where memory accuracy and controllability are critically important.
2026-02-26	Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimization
	• Proposes the EMPO² hybrid policy optimization framework, aimed at overcoming exploration bottlenecks in reinforcement learning training for LLM agents. • Combines memory-guided exploration with policy updates, ensuring strong robustness in both memory-enabled and memory-free scenarios. • Achieves a 128.6% performance leap on the ScienceWorld task and demonstrates excellent adaptability on out-of-distribution tasks.
2026-02-26	AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications
	• Introduces AMA-Bench, a long-term memory evaluation framework specifically designed for autonomous agent–environment interactions rather than dialogue-only settings. • Simultaneously proposes the AMA-Agent system, which leverages causal graphs and tool-augmented retrieval to optimize memory retrieval quality. • The study reveals shortcomings of existing memory systems in continuous environment interaction tasks and provides effective pathways for improvement.
2026-02-25	Towards Autonomous Memory Agents
	• Proposes the autonomous memory agent U-Mem, which shifts memory behavior from passive storage to active knowledge acquisition, verification, and curation. • Introduces a cost-aware knowledge extraction cascade, incorporating multi-level validation from self-signals to expert feedback, combined with semantically aware sampling strategies. • Successfully mitigates cold-start bias and outperforms previous memory baselines on challenging knowledge-intensive tasks such as HotpotQA.
2026-02-25	Structurally Aligned Subtask-Level Memory for Software Engineering Agents
	• To address the challenges faced by software engineering agents when handling tasks with similar descriptions but differing logic, this work proposes structure-aligned, subtask-level memory. • Through fine-grained task decomposition and memory alignment, the mechanism significantly enhances agents’ long-horizon reasoning capabilities. • Its superiority in solving complex, logically entangled tasks is demonstrated across multiple software engineering benchmark evaluations.
2026-02-24	ARCHITECTING AGENTOS: FROM TOKEN-LEVEL CONTEXT TO EMERGENT SYSTEM-LEVEL INTELLIGENCE
	• This paper explores a framework—AgentOS—that enables large language models (LLMs) to evolve into dynamic autonomous cognitive systems. • By redefining LLMs as a “reasoning kernel,” AgentOS introduces the concept of deep context management to address information dilution in long-context tasks and temporal drift in multi-agent collaboration found in current applications. • The paper provides a detailed exposition of AgentOS’s core components, such as the Cognitive Synchronization Pulse (CSP), semantic slicing theory, and perception alignment mechanisms, and explains how they facilitate the emergence of collective intelligence in multi-agent systems.
2026-02-24	Pancake: Hierarchical Memory System for Multi-Agent LLM Serving
	• Proposes Pancake, a multi-level agent memory system designed to address multi-agent memory fragmentation in large-scale LLM services. • Unifies multi-level index caching, coordinated index management, and collaborative GPU–CPU acceleration techniques. • Compatible with mainstream frameworks such as LangChain, Pancake achieves more than a 4.29× throughput improvement under real-world workloads.
2026-02-23	Agents of Chaos
	• The paper reports on red-teaming tests of autonomous language model agents, highlighting the security and privacy vulnerabilities that agents can introduce in real-world environments. • The study examines 11 cases, revealing various issues and failure modes of agents in handling sensitive information, following instructions, and managing resources. • The text discusses the complexity of responsibility attribution and the shortcomings of existing technical and legal frameworks in ensuring the safety of autonomous systems.
2026-02-23	Structured Prompt Language: Declarative Context Management for LLMs
	• Introduces SPL, an SQL-inspired declarative language that treats the LLM context window as a constrained resource for efficient management. • Natively integrates RAG and persistent memory, providing an automatic query optimizer and transparent EXPLAIN-style debugging capabilities. • Experiments confirm that SPL significantly reduces prompt redundancy and lowers computational costs in tasks such as multilingual translation and logical chunking.
2026-02-23	Agentic AI as a Cybersecurity Attack Surface: Threats, Exploits, and Defenses in Runtime Supply Chains
	• Systematically analyzes the security risks in the reasoning dependencies of LLM agents, categorizing them as data and tool supply chain attacks. • Introduces the concept of a “viral agent loop,” warning that agents may become carriers of self-propagating generative worms. • Advocates for the adoption of a zero-trust runtime architecture, constraining tool execution through cryptographically verified provenance and treating context as an untrusted data stream.
2026-02-22	Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations
	• The paper analyzes agentic memory systems—particularly the state persistence capabilities of large language models (LLMs) during long-term interactions—revealing that the empirical foundations of their design and evaluation remain fragile. • The article categorizes memory structures, including lightweight semantic memory, entity-centric and personalized memory, episodic memory, and reflective memory. Through empirical analysis, it shows that current systems fall short of expectations and identifies a mismatch between evaluation metrics and semantic utility. • The paper also explores Memory-Augmented Generation (MAG) in the context of long-context processing, examining different memory architectures, operations, and management strategies, and emphasizes the need for optimized memory management and improved evaluation methodologies.
2026-02-20	From Lossy to Verified: A Provenance-Aware Tiered Memory for Agents
	• TierMem is a hierarchical memory system designed to improve the efficiency and accuracy of information retrieval, addressing information compression challenges arising from long-term interaction histories. • By constructing a fast summary layer and an immutable raw log layer, the system effectively manages information storage and retrieval, accelerating query response times while reducing costs. • TierMem’s design incorporates intelligent routing optimization, error analysis, and hierarchical recall strategies to balance accuracy and efficiency.
2026-02-20	Memory-Based Advantage Shaping for LLM-Guided Reinforcement Learning
	• This study proposes an innovative “memory-based advantage shaping” approach aimed at improving the sample efficiency of reinforcement learning (RL) guided by large language models (LLMs) in sparse or delayed reward environments. • By constructing a memory graph and introducing a utility function, the method effectively integrates external guidance to optimize the learning process of RL models. • Experimental results demonstrate high sample efficiency and strong performance across multiple benchmark environments.
2026-02-20	MIRA: MEMORY-INTEGRATED REINFORCEMENT LEARNING AGENT WITH LIMITED LLM GUIDANCE
	• MIRA (Memory-Integrated Reinforcement Learning Agent) is a novel reinforcement learning agent that integrates guidance from large language models (LLMs) by constructing a structured memory graph, improving learning efficiency and exploration capability in sparse-reward environments. • The core of MIRA lies in combining offline and online LLM guidance, filtering high-confidence outputs, computing utility signals, and optimizing the advantage function to ensure autonomous learning while reducing reliance on real-time LLM queries. • Experimental results show that MIRA outperforms traditional methods in both sample efficiency and convergence across multiple environments, demonstrating its potential in complex tasks.
2026-02-20	REMem: Reasoning with Episodic Memory in Language Agent
	• To address the lack of effective recall of interaction history in existing language agents, the episodic memory reasoning framework REMem is proposed. • Its core consists of two stages—offline indexing and online reasoning—enabling agents to mimic human-like reasoning within spatiotemporal contexts. • Experimental results show that this framework demonstrates significant advantages on specialized episodic memory benchmark tests.
2026-02-17	Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory
	• Mnemis is a novel memory framework designed to enhance the organization and retrieval capabilities of large language models (LLMs). • The framework combines two retrieval mechanisms: System 1 similarity search and System 2 global selection. By constructing a base graph and a hierarchical graph, it can handle complex queries more efficiently. • Mnemis performs exceptionally well on long-term memory benchmarks, achieving high scores such as 93.9 and 91.6, and demonstrates significant advantages in entity recognition and information retrieval.
2026-02-17	ZOMBIE AGENTS: PERSISTENT CONTROL OF SELF-EVOLVING LLM AGENTS VIA SELF-REINFORCING IN-JECTIONS
	• The paper introduces the “Zombie Agent” attack model, focusing on self-evolving large language model (LLM) agents. • The model demonstrates how malicious payloads can be injected into an agent’s long-term memory through a black-box two-stage attack framework (infection and trigger), leading to persistent compromise. • The study shows that existing prompt-filtering defenses are insufficient to protect such self-evolving agents and highlights the particular vulnerability of memory architectures.
2026-02-17	ER-MIA: Black-Box Adversarial Memory Injection Attacks on Long-Term Memory-Augmented Large Language Models
	• This paper explores a framework called ER-MIA, which investigates black-box adversarial memory injection attacks (AMIAs) targeting long-term memory–augmented large language models (LLMs). • The ER-MIA framework reveals attack mechanisms that inject malicious text through normal interactions, potentially leading the model to produce incorrect reasoning. • The study designs multiple automated adversarial memory generation strategies and conducts empirical evaluations, demonstrating that current long-term memory systems exhibit significant vulnerabilities when facing such attacks.
2026-02-17	Improving MLLMs in Embodied Exploration and Question Answering with Human-Inspired Memory Modeling
	• Proposes a non-parametric memory framework that explicitly distinguishes episodic memory from semantic memory, enhancing the exploration capabilities of embodied agents. • Employs a program-style rule extraction mechanism to transform environmental experiences into structured semantic memory that can generalize across scenarios. • Significantly improves observation reuse and question-answering efficiency of multimodal large models in non-stationary environments.
2026-02-16	HyperRAG: Reasoning N-ary Facts over Hypergraphs for Retrieval Augmented Generation
	• HyperRAG is an innovative retrieval-augmented generation (RAG) framework that replaces traditional binary knowledge graphs with n-ary hypergraphs to improve the accuracy and efficiency of complex question answering and knowledge retrieval tasks. • Through the new modules HyperRetriever and HyperMemory, the framework enables more precise multi-hop reasoning and enhanced context awareness. • Experimental results show that HyperRAG achieves superior performance across multiple benchmark datasets.
2026-02-16	PANINI: Continual Learning in Token Space via Structured Memory
	• This paper provides a detailed introduction to PANINI, a non-parametric continual learning framework designed to enhance continual learning and multi-hop question answering capabilities. • By incorporating structured memory and a Generative Semantic Workspace (GSW), PANINI can efficiently process new information and support reasoning through chains of question answering. • Compared with various baseline models, PANINI demonstrates superior performance across multiple evaluation benchmarks, showing particular robustness in scenarios with missing evidence.
2026-02-15	Choosing How to Remember: Adaptive Memory Structures for LLM Agents
	• Proposes the FluxMem framework, which enables LLM agents to adaptively select different memory structures based on interaction characteristics. • Introduces a three-layer memory hierarchy and employs probabilistic gating based on a Beta mixture model to enhance the robustness of memory fusion. • FluxMem demonstrates outstanding performance in handling heterogeneous interaction patterns, achieving significant performance gains across multiple long-term benchmarks.
2026-02-14	Hippocampus: An Efficient and Scalable Memory Module for Agentic AI
	• Proposes Hippocampus, an efficient and scalable memory module for agentic AI that replaces dense-vector or graph-heavy retrieval with compact binary signatures and lossless token-ID streams. • Introduces a Dynamic Wavelet Matrix (DWM) to jointly compress and index semantic signatures and reconstructable content, enabling ultra-fast search directly in the compressed domain. • Experiments on LoCoMo and LongMemEval show up to 31× lower end-to-end retrieval latency and up to 14× fewer per-query tokens while maintaining accuracy.
2026-02-14	HyMem: Hybrid Memory Architecture with Dynamic Retrieval Scheduling
	• Proposes a hybrid memory architecture, HyMem, which addresses the efficiency trade-offs in long conversations through dual-granularity storage (summary-level / deep-level). • Introduces a dynamic, on-demand scheduling mechanism: simple queries invoke efficient summaries, while complex queries activate deep-level modules. • Experiments show that HyMem maintains high performance while reducing computational costs by 92.6%.
2026-02-14	Neuromem: A Granular Decomposition of the Streaming Lifecycle in External Memory for LLMs
	• Introduces Neuromem, a scalable benchmarking platform for evaluating the full dynamic lifecycle of external memory modules for LLMs, from ingestion and maintenance to retrieval and integration. • The study reveals that memory accuracy and cost are jointly influenced by the entire lifecycle rather than any single stage in isolation. • The analysis indicates that performance generally degrades as memory scale increases, and that time-dependent queries remain the most significant challenge for current systems.
2026-02-13	Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning
	• This paper explores the development of the Unified Memory Agent (UMA) and its application in dynamic long-term state tracking and information retrieval. • By integrating memory operations with question answering, UMAs overcome the challenges faced by traditional large language models (LLMs) when handling long inputs. • UMA leverages reinforcement learning and novel policy optimization algorithms, demonstrating significant performance improvements in dynamic scenarios, with accuracy increasing from 61.38% to 76.46%.
2026-02-12	Learning to Forget Attention: Memory Consolidation for Adaptive Compute Reduction
	• The paper finds that 88% of attention operations in LLMs retrieve predictable information, a redundancy that persists throughout standard training without being eliminated. • It proposes CRAM, an architecture that adaptively reduces compute by gradually distilling frequently accessed episodic retrievals into efficient parametric semantic memory via a consolidation-aware router. • CRAM achieves a 37.8x reduction in attention compute while its learned consolidation dynamics quantitatively match the power-law transition curves observed in human cognitive psychology.
2026-02-12	Scene-Aware Memory Discrimination: Deciding Which Personal Knowledge Stays
	• It introduces the "memory discrimination" task, which acts as a filter during memory construction to identify and store only valuable personal knowledge from vast daily interactions. • It proposes the Scene-Aware Memory Discrimination (SAMD) framework, combining a Gating Unit Module (GUM) for efficient noise filtering and a Cluster Prompting Module (CPM) for adaptive memory standards. • Evaluations demonstrate that SAMD significantly enhances the efficiency and quality of memory construction, successfully recalling critical data while reducing computational costs for personalized AI agents.
2026-02-12	Recurrent Preference Memory for Efficient Long-Sequence Generative Recommendation
	• It proposes the Rec2PM framework with a tripartite memory mechanism that compresses long user histories into compact Preference Memory tokens, overcoming the computational bottlenecks of long-sequence modeling. • It introduces a self-referential teacher-forcing strategy that leverages a global view of history to generate reference targets, enabling fully parallelized training for recurrent memory updates. • It demonstrates superior storage and inference efficiency while acting as a denoising Information Bottleneck to filter interaction noise, achieving higher accuracy than full-sequence models.
2026-02-12	TS-Memory: Plug-and-Play Memory for Time Series Foundation Models
	• It proposes TS-Memory, a plug-and-play lightweight memory adapter that uses Parametric Memory Distillation to adapt frozen Time Series Foundation Models (TSFMs) to downstream domain shifts. • It employs a two-stage training strategy: first constructing an offline kNN teacher to generate privileged supervision signals, then distilling retrieval-induced distributional corrections into a parametric module via confidence-gated supervision. • It enables retrieval-free inference with constant-time complexity, consistently improving forecasting accuracy across benchmarks while maintaining the efficiency of the frozen backbone and avoiding catastrophic forgetting.
2026-02-11	Understand Then Memory: A Cognitive Gist-Driven RAG Framework with Global Semantic Diffusion
	• CogitoRAG is a retrieval-augmented generation (RAG) framework based on cognitive memory mechanisms, designed to enhance large language models’ (LLMs) capabilities in knowledge integration and reasoning. • The framework simulates human cognitive processes and optimizes information retrieval and answer generation through the collaboration of three main modules: query decomposition, entity diffusion, and CogniRank re-ranking. • CogitoRAG achieves strong performance across multiple question-answering benchmarks, particularly outperforming traditional RAG methods on complex reasoning tasks, emphasizing the principle that “understanding is superior to memorization.”
2026-02-11	When to Memorize and When to Stop: Gated Recurrent Memory for Long-Context Reasoning
	• It proposes GRU-Mem, a gated recurrent memory framework that processes long context chunk-by-chunk to overcome performance degradation and context window limits in LLMs. • It introduces two text-controlled gates, an update gate and an exit gate, to selectively update memory and enable early termination, effectively preventing memory explosion and reducing redundant computation. • Optimized via end-to-end reinforcement learning, GRU-Mem significantly outperforms existing methods in reasoning tasks and achieves up to 400% inference speed acceleration compared to vanilla recurrent memory agents.
2026-02-11	Towards Compressive and Scalable Recurrent Memory
	• It proposes Elastic Memory, a novel recurrent memory architecture grounded in the HiPPO framework that encodes long-range history into fixed-size states via optimal online function approximation. • It develops a parallelized block-level update and a flexible "polynomial sampling" mechanism for efficient retrieval, enabling the reconstruction of history summaries from compressed states without extra trainable parameters. • Experiments demonstrate that it outperforms SOTA baselines on 32k+ context tasks with superior efficiency, while its decoupled design allows for injecting inductive biases at test-time without retraining.
2026-02-11	UMEM: Uniffed Memory Extraction and Management Framework for Generalizable Memory
	• The paper proposes UMEM, a framework that jointly optimizes memory extraction and management to resolve the policy misalignment and poor generalization found in traditional static extraction methods. • It introduces Semantic Neighborhood Modeling and a Marginal Utility Reward via GRPO, forcing the agent to distill generalizable principles across clusters of related tasks instead of memorizing instance-specific noise. • Experiments show that UMEM significantly outperforms state-of-the-art baselines across multiple benchmarks and maintains a stable, monotonic performance growth curve during continuous self-evolution.
2026-02-10	TraceMem: Weaving Narrative Memory Schemata from User Conversational Traces
	• It proposes TraceMem, a cognitively-inspired framework that weaves disjointed conversational traces into structured narrative memory schemata through a three-stage pipeline of processing and consolidation. • It mimics human memory consolidation by using topic segmentation and hierarchical clustering to transform episodic snippets into coherent, time-evolving narrative threads and structured user memory cards. • It implements an agentic search mechanism to enable human-like source attribution, achieving state-of-the-art performance in multi-hop and temporal reasoning for long-term dialogues.
2026-02-09	AMEM4Rec: Leveraging Cross-User Similarity for Memory Evolution in Agentic LLM Recommenders
	• It proposes AMEM4Rec, an agentic framework that models collaborative filtering signals end-to-end by introducing an evolving memory module without relying on pre-trained CF models. • It designs a cross-user memory evolution mechanism that aggregates abstract behavior patterns into a global pool, using dual validation to link and iteratively evolve shared memory entries across users. • Extensive experiments demonstrate that AMEM4Rec significantly outperforms existing baselines, particularly showing superior performance and generalization in sparse interaction scenarios.
2026-02-09	Position: Stateless Yet Not Forgetful: Implicit Memory as a Hidden Channel in LLMs
	• Introduces the concept of "Implicit Memory," demonstrating that LLMs can bypass their stateless nature by encoding state information in their own outputs to create a persistent hidden channel across independent sessions. • Implements and validates "Time Bombs," a new class of temporal backdoors that use implicit memory to accumulate hidden states over multiple interactions, activating only after a specific sequence of conditions is met. • Systematically analyzes risks such as covert communication and benchmark contamination, while outlining future research directions for detecting, evaluating, and controlling unintended persistence in LLMs.
2026-02-09	MemAdapter: Fast Alignment across Agent Memory Paradigms via Generative Subgraph Retrieval
	• It proposes MemAdapter, a framework that unifies heterogeneous agent memory paradigms (explicit, parametric, and latent) using a paradigm-agnostic generative subgraph retrieval approach. • It employs a two-stage training strategy: distilling a generative retriever from a unified memory space and then efficiently adapting it to new paradigms via lightweight alignment modules and contrastive learning. • MemAdapter consistently outperforms existing baselines, completing paradigm alignment in just 13 minutes and enabling effective zero-shot fusion across different memory types.
2026-02-07	MemPot: Defending Against Memory Extraction Attack with Optimized Honeypots
	• This paper proposes MemPot, the first defense framework against memory extraction attacks that proactively injects optimized honeypots (trap documents) into the agent's memory systems. • It utilizes a two-stage optimization strategy to maximize the statistical separability between attacker and user retrieval patterns, generating safe and inconspicuous trap texts through safety-constrained embedding inversion. • Based on Wald’s Sequential Probability Ratio Test (SPRT), MemPot achieves near-perfect detection accuracy with zero online inference latency while preserving the agent's core utility and performance.
2026-02-06	Agentic Unlearning: When LLM Agent Meets Machine Unlearning
	• A new framework called Synchronized Backflow Unlearning (SBU) is proposed to effectively remove sensitive information from large language models (LLMs) while ensuring the integrity of shared knowledge. • By coordinating the collaboration between parameter paths and memory paths, SBU addresses the limitations of traditional privacy-preserving methods, successfully eliminating residual effects of deleted information and reducing the risk of information recontamination. • Experimental results show that this approach significantly improves privacy protection on medical question-answering benchmarks while maintaining accuracy. It not only outperforms baseline methods in efficiency but also overcomes computational resource constraints.
2026-02-05	Learning to Share: Selective Memory for Efficient Parallel Agentic Systems
	• Studied how to share memory selectively across parallel agents to reduce redundancy and coordination overhead. • Proposed a selective sharing mechanism/policy to decide what to broadcast vs keep private per agent. • Evaluated on multi-agent settings, showing improved efficiency while maintaining (or improving) task performance.
2026-02-02	Live-Evo: Online Evolution of Agentic Memory from Continuous Feedback
	• Formulated memory as an evolving object updated online from dense/continuous feedback signals rather than sparse endpoints. • Proposed an update/evolution loop that revises stored memories based on feedback to improve future behavior. • Demonstrated online improvement over time in agent tasks under continuous supervision.
2026-02-02	Beyond RAG for Agent Memory: Retrieval by Decoupling and Aggregation
	• Revisited agent memory retrieval as a two-stage process: decouple candidate fetching from evidence aggregation. • Proposed an aggregation mechanism to combine multi-source/multi-hop evidence for downstream reasoning. • Showed gains vs vanilla RAG-style retrieval pipelines in agent memory usage and answer quality.
2026-01-30	Field-Theoretic Memory for AI Agents: Continuous Dynamics for Context Preservation
	• Proposes an AI agent memory system that treats stored information as a continuous field (governed by partial differential equations) rather than discrete entries. • Drawing inspiration from field theory, memory diffuses within a semantic space, decays thermodynamically based on importance, and enables multi-agent interaction through field coupling. • The approach achieves significant improvements in multi-session and temporal reasoning performance on the LongMemEval benchmark, demonstrating the advantages of collective intelligence.
2026-01-30	Traversal-as-Policy: Log-Distilled Gated Behavior Trees as Externalized, Verifiable Policies for Safe, Robust, and Efficient Agents
	• Proposes the Traversal-as-Policy paradigm, which distills execution logs into structured Gated Behavior Trees (GBT) and replaces unconstrained generation with deterministic tree traversal at runtime, achieving externalized and verifiable Agent policies. • Introduces Spine Memory to record traversal paths, effectively solving the problems of implicit strategy and post-hoc safety in long-horizon tasks. • Experiments validate that this method can simultaneously improve success rates and significantly reduce violation risks and token consumption across software engineering, Web Agent, and safety domains.
2026-01-29	E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory
	• Proposed the E-mem framework, shifting from traditional memory preprocessing to Episodic Context Reconstruction to prevent information loss caused by de-contextualization. • Adopted a heterogeneous Master-Assistant architecture where assistant agents maintain uncompressed context as memory nodes while the master agent handles global planning. • Introduced a routing mechanism allowing assistants to reason within locally restored original contexts, achieving SOTA performance on LoCoMo and HotpotQA while reducing token costs by over 70%.
2026-01-29	ShardMemo: Masked MoE Routing for Sharded Agentic LLM Memory
	• Proposed ShardMemo, a tiered memory architecture: Tier A (working state), Tier B (sharded evidence), and Tier C (versioned skill library). • Enforced a "scope-before-routing" strategy in Tier B and modeled shard selection as a Masked MoE routing problem under fixed budgets, using cost-aware gating. • Improved F1 by +6.87 on LoCoMo and HotpotQA compared to cosine similarity routing, while reducing retrieval work and latency by 20.5%.
2026-01-28	MemCtrl: Using MLLMs as Active Memory Controllers on Embodied Agents
	• Proposed MemCtrl, a framework using MLLMs as active memory controllers to filter redundant observations online for embodied agents. • Introduced a trainable memory head ($\mu$) acting as a gate to dynamically determine whether to retain, update, or discard observations during exploration. • Trained via offline supervision and online RL, MemCtrl improved task completion rates by ~16% for small MLLMs on EmbodiedBench, with >20% gains on specific instruction subsets.
2026-01-28	AMA: Adaptive Memory via Multi-Agent Collaboration
	• Proposed AMA (Adaptive Memory via Multi-Agent Collaboration), leveraging Constructor, Retriever, Judge, and Refresher agents to manage multi-granularity memory. • Utilized a hierarchical memory design (Raw Text, Fact Knowledge, Episode), where the Retriever dynamically routes queries and the Judge detects conflicts. • The Refresher maintains long-term consistency via logic-driven updates. AMA significantly outperformed baselines on LoCoMo and LongMemEval while reducing token consumption by 80%.
2026-01-27	GLOVE: Global Verifier for LLM Memory-Environment Realignment
	• Proposed the Global Verifier (GLOVE) framework to address memory-environment misalignment caused by dynamic environmental drifts. • Established "relative truth" via active probing to detect cognitive dissonance by comparing retrieved memories with fresh observations, realigning memory without ground truth. • Significantly improved agent adaptability and success rates in web navigation, discrete planning, and continuous control tasks under explicit and implicit environment drifts.
2026-01-26	FadeMem: Biologically-Inspired Forgetting for Efficient Agent Memory
	• Proposed FadeMem, a memory architecture inspired by the Ebbinghaus forgetting curve, introducing active forgetting mechanisms to prevent information overload. • Implemented a dual-layer hierarchy with adaptive exponential decay governed by semantic relevance, access frequency, and temporal patterns. • Combined with LLM-guided conflict resolution, FadeMem achieved superior multi-hop reasoning on Multi-Session Chat and LoCoMo while reducing storage by 45%.
2026-01-26	MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning
	• MemWeaver proposes a tri-layer memory framework (Graph, Experience, Passage) that consolidates long-term interactions into temporally grounded structures, ensuring evidence traceability. • It employs a dual-channel retrieval strategy to "weave" structured relational facts with original textual evidence, supporting complex multi-hop and temporal reasoning tasks. • Experiments on the LoCoMo benchmark show it improves reasoning accuracy while actively reducing input context length by over 95% compared to long-context baselines.
2026-01-24	Clustering-driven Memory Compression for On-device Large Language Models
	• Proposed a clustering-based memory compression strategy designed for on-device personalization under limited context windows. • Groups similar memories and merges them within clusters (instead of simple concatenation), reducing redundancy while preserving semantic coherence. • Demonstrated significant reduction in token usage and improved personalized generation quality compared to naive concatenation baselines.
2026-01-13	Chain-of-Memory: Lightweight Memory Construction with Dynamic Evolution for LLM Agents
	• Proposed CoM (Chain-of-Memory), advocating a shift from expensive structured construction to lightweight construction with dynamic utilization. • Introduced Dynamic Memory Chain Evolution to organize retrieved fragments into coherent inference paths with adaptive truncation to prune noise. • Achieved 7.5%–10.4% accuracy gains on LoCoMo and LongMemEval while reducing token consumption to ~2.7% compared to complex memory structures.
2026-01-15	TeleMem: Building Long-Term and Multimodal Memory for Agentic AI
	• TeleMem introduces a unified long-term and multimodal memory framework that extracts narrative-grounded information to maintain coherent user profiles without schema-driven hallucinations. • It employs a structured writing pipeline for batching, retrieval, and consolidation, significantly improving storage and token efficiency, and incorporates a multimodal memory module with ReAct-style reasoning for video understanding. • Experimental results on the ZH-4O benchmark show TeleMem outperforming the state-of-the-art Mem0 baseline by 19% in accuracy while reducing token usage by 43% and speeding up operations by 2.1×.
2026-01-15	Grounding Agent Memory in Contextual Intent
	• Proposes STITCH, an agentic memory system that indexes trajectory steps using "Contextual Intent"—comprising thematic scope, event type, and key entity types—to disambiguate recurring information in long-horizon tasks. • Introduces a retrieval mechanism that filters and prioritizes memory snippets based on structural intent compatibility rather than just semantic similarity, effectively suppressing context-incompatible history. • Presents CAME-Bench, a multi-domain benchmark designed to evaluate context-aware retrieval in realistic, goal-oriented trajectories, where STITCH achieves state-of-the-art performance.
2026-01-14	PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records
	• Introduces PersonalAlign, a new task requiring GUI agents to align with implicit user intents—specifically resolving vague instructions and anticipating routines—by leveraging long-term user records. • Presents AndroidIntent, a benchmark constructed from 20k long-term records, featuring hierarchically annotated user preferences and routines to evaluate personalization capabilities. • Proposes HIM-Agent (Hierarchical Intent Memory Agent), which utilizes a streaming aggregation module and hierarchical filters (Execution-based and State-based) to continuously update and organize user memory for improved reactive and proactive performance.
2026-01-13	AtomMem: Learnable Dynamic Agentic Memory with Atomic Memory Operation
	• Introduces AtomMem, a dynamic memory framework that reframes agentic memory management as a learnable sequential decision-making problem rather than a static, hand-crafted workflow. • Deconstructs memory processes into atomic CRUD (Create, Read, Update, Delete) operations and employs reinforcement learning (GRPO) to learn a task-aligned policy for autonomously orchestrating these operations. • Experimental results on long-context benchmarks (HotpotQA, 2WikiMultihopQA, Musique) demonstrate that AtomMem consistently outperforms static memory baselines by dynamically tailoring memory strategies to specific task demands.
2026-01-13	Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management
	• Fine-Mem is a unified reinforcement learning framework designed to optimize long-horizon memory management for LLM agents by aligning fine-grained feedback with memory operations. • It addresses reward sparsity through Chunk-level Step Reward (CSR), which provides immediate supervision via constructed QA tasks, and solves credit assignment issues with Evidence-Anchored Reward Attribution (EARA) by linking global rewards to specific memory operations. • Experimental results demonstrate that Fine-Mem consistently outperforms strong baselines on benchmarks like Memalpha and MemoryAgentBench, showing superior adaptability and generalization across different models.
2026-01-12	Active Context Compression: Autonomous Memory Management in LLM Agents
	• Proposed an autonomous strategy to compress/retain context so agents can operate under tight context budgets. • Treated memory management as an active decision problem: what to keep, summarize, discard, or externalize. • Demonstrated improved long-horizon performance vs passive truncation or naive summarization baselines.
2026-01-12	MemoBrain: Executive Memory as an Agentic Brain for Reasoning
	• MemoBrain introduces an "executive memory" paradigm for tool-augmented agents, functioning as a co-pilot to construct dependency-aware memory and actively manage context under bounded budgets. • The framework employs specific memory operations—Trajectory Folding and Selective Flush—to organize reasoning progress, retaining a high-salience structural backbone while discarding transient execution artifacts. • Experiments on benchmarks like GAIA, WebWalker, and BrowseComp-Plus demonstrate that MemoBrain consistently outperforms strong baselines by enabling coherent, goal-directed reasoning over long horizons.
2026-01-12	Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents
	• TSM is a memory framework that models semantic time for point-wise memory and supports the construction and utilization of durative memory. • It builds a semantic timeline to organize episodic interactions and consolidates them into time-aware durative memories (topics and personas) to capture long-term user states. • During memory utilization, TSM incorporates the query’s temporal intent to retrieve temporally appropriate durative memories, significantly improving performance on benchmarks like LONGMEMEVAL and LOCOMO.
2026-01-10	Bi-Mem: Bidirectional Construction of Hierarchical Memory for Personalized LLMs via Inductive-Reflective Agents
	• Bi-Mem is an agentic framework that constructs hierarchical memory (fact, scene, persona) bidirectionally using an inductive agent for bottom-up aggregation and a reflective agent for top-down calibration to mitigate noise and hallucination. • It employs an associative retrieval mechanism that leverages spreading activation to connect memory units across granularities, enabling coherent recall of both contextual scenes and specific facts. • Empirical evaluations on the LoCoMo benchmark demonstrate that Bi-Mem significantly outperforms leading memory baselines in long-term personalized conversational tasks.
2026-01-10	HiMem: Hierarchical Long-Term Memory for LLM Long-Horizon Agents
	• HiMem is a hierarchical long-term memory framework designed for long-horizon dialogues, integrating fine-grained "Episode Memory" (via topic-aware segmentation) with abstract "Note Memory" (via knowledge extraction) to bridge concrete events and stable knowledge. • It employs a conflict-aware "Memory Reconsolidation" mechanism that uses retrieval feedback to revise and supplement stored knowledge, enabling continual self-evolution and correction of memory over time. • Evaluations on long-horizon benchmarks demonstrate that HiMem outperforms baselines in accuracy, consistency, and reasoning, validating the effectiveness of its hierarchical organization and dynamic updating strategies.
2026-01-10	Structured Episodic Event Memory
	• SEEM introduces a dual-layer memory framework combining a Graph Memory Layer for static facts and an Episodic Memory Layer for narrative progression, both anchored by provenance pointers to raw interaction passages. • The system employs a "Reverse Provenance Expansion" (RPE) mechanism to reconstruct coherent narrative contexts from fragmented evidence during retrieval, addressing the "scattered retrieval" problem in long-term interactions. • Experiments on benchmarks like LoCoMo and LongMemEval show SEEM significantly outperforms competitive memory-augmented baselines (like HippoRAG 2) in narrative coherence and logical consistency.
2026-01-09	MemBuilder: Reinforcing LLMs for Long-Term Memory Construction via Attributed Dense Rewards
	• MemBuilder is a reinforcement learning framework that trains LLMs to actively construct and manage a multi-dimensional memory system (Core, Episodic, Semantic, and Procedural) rather than relying on static prompting. • It introduces "Attributed Dense Rewards Policy Optimization" (ADRPO) to solve reward sparsity and credit assignment issues by using synthetic session-level QA for immediate feedback and gradient weighting based on memory component contribution. • Experimental results show that a lightweight 4B model trained with MemBuilder outperforms state-of-the-art closed-source models (including Claude 4.5 Sonnet) on long-term dialogue benchmarks like LoCoMo and LongMemEval.
2026-01-08	Beyond Static Summarization: Proactive Memory Extraction for LLM Agents
	• ProMem Framework: Addresses the limitations of "one-off" static summarization by proposing a proactive memory extraction framework inspired by Recurrent Processing Theory (RPT). • Recurrent Feedback Loop: Introduces a self-questioning mechanism where the agent actively probes dialogue history to verify facts and recover missing details, ensuring memory completeness and accuracy. • Performance: Outperforms state-of-the-art baselines (e.g., Mem0) on HaluMem and LongMemEval benchmarks, demonstrating high robustness in token compression and cost-effectiveness with Small Language Models.
2026-01-08	Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning
	• Proposed CompassMem, an event-centric memory framework inspired by Event Segmentation Theory, organizing memory as an Event Graph with explicit logical relations (causal, temporal). • Transforms memory from passive storage into a Logic Map, enabling agents to actively navigate structured dependencies via a Planner-Explorer-Responder mechanism. • Features active multi-path memory search that dynamically expands or skips nodes based on subgoal satisfaction, avoiding redundant retrieval. • Demonstrates superior performance on LoCoMo and NarrativeQA benchmarks, significantly improving multi-hop and temporal reasoning compared to baselines like HippoRAG and Mem0.
2026-01-08	Inside Out: Evolving User-Centric Core Memory Trees for Long-Term Personalized Dialogue Systems
	• PersonaTree Framework: Introduces a globally maintained **PersonaTree** grounded in the Biopsychosocial model as a dynamic user profile. By constraining the trunk with a schema and iteratively updating branches, it enables controllable memory growth and compression. • MemListener Training: Trains a lightweight MemListener model via **Reinforcement Learning with process-based rewards** to generate structured, executable memory operations (ADD, UPDATE, DELETE), achieving performance comparable to large reasoning models. • Adaptive Response Generation: Implements a dual-mode strategy that utilizes PersonaTree directly for low-latency responses or triggers an agentic recall mode guided by the tree for complex queries, outperforming baselines in consistency and noise suppression.
2026-01-07	Membox: Weaving Topic Continuity into Long-Range Memory for LLM Agents
	• Membox Architecture: Addresses the "fragmentation-compensation" flaw in existing systems by proposing a hierarchical architecture centered on **Topic Continuity** to preserve temporal and causal flow. • Topic Loom & Trace Weaver: Introduces a *Topic Loom* to group continuous dialogue into cohesive "memory boxes" and a *Trace Weaver* to link these boxes into long-range event timelines across discontinuities. • Performance: Achieves up to 68% F1 improvement on temporal reasoning tasks in the LoCoMo benchmark compared to baselines like Mem0, while using significantly fewer context tokens.
2026-01-06	HiMeS: Hippocampus-inspired Memory System for Personalized AI Assistants
	• HiMeS is a memory framework for AI assistants that emulates the hippocampus–neocortex interaction by integrating short-term dialogue compression with long-term user profile storage. • It utilizes a short-term memory extractor trained via reinforcement learning to proactively pre-retrieve knowledge, and a partitioned long-term memory network to re-rank results based on historical user interactions. • Evaluations on real-world industrial datasets demonstrate that HiMeS significantly outperforms traditional RAG baselines in personalized question-answering tasks.
2026-01-06	SYNAPSE: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation
	• SYNAPSE is a brain-inspired memory architecture that replaces static vector retrieval with a unified episodic–semantic graph, addressing the “context isolation” issue in traditional RAG systems, where semantically distant yet causally related memories cannot be effectively associated. • It introduces cognitive dynamics such as spreading activation, lateral inhibition, and temporal decay to dynamically propagate relevance and filter noise within the graph, rather than relying solely on precomputed links or vector similarity. • SYNAPSE achieves state-of-the-art performance on the LoCoMo benchmark, significantly improving multi-hop reasoning capabilities and robustness to adversarial queries through an uncertainty-aware gating mechanism.
2026-01-06	CODEMEM: AST-Guided Adaptive Memory for Repository-Level Iterative Code Generation
	• Proposed CODEMEM, a memory management system tailored for repository-level iterative code generation. • Introduces Code Context Memory: Uses AST-guided selection to dynamically update and merge repository context, keeping it relevant while discarding noise. • Introduces Code Session Memory: Uses AST-based change analysis to detect conflicts and forgetting, organizing history into code-centric units (diffs) rather than just text. • Achieves SOTA on CodeIF-Bench and CoderEval, improving instruction following by ~12% and reducing interaction rounds by 2–3.
2026-01-06	Implicit Graph, Explicit Retrieval: Towards Efficient and Interpretable Long-horizon Memory for Large Language Models
	• LatentGraphMem Framework: Proposes a memory framework combining implicit graph memory for stability and efficiency with explicit subgraph retrieval for interpretability, storing graph structures in latent space. • Three-Stage Training Strategy: Involves training a graph builder (global representation), a subgraph retriever (budgeted edge selection), and joint fine-tuning (coordination optimization) for effective end-to-end QA. • Performance: Consistently outperforms explicit-graph and latent-memory baselines on long-horizon benchmarks like HotpotQA, NarrativeQA, and WikiHop across multiple model scales, achieving up to 63.34% average accuracy.
2026-01-06	MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents
	• MAGMA Architecture: Proposes a multi-graph agentic memory architecture that explicitly models memory items across orthogonal semantic, temporal, causal, and entity graphs, overcoming the limitations of monolithic memory stores. • Adaptive Topological Retrieval: Introduces an intent-aware Adaptive Traversal Policy that dynamically routes retrieval through relevant relational views, decoupling memory representation from retrieval logic for transparent reasoning. • Performance: Consistently outperforms state-of-the-art agentic memory systems (e.g., Nemori, A-MEM) on long-horizon benchmarks like LoCoMo and LongMemEval, while reducing retrieval latency and token consumption.
2026-01-06	TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agents
	• TiMem Framework: Introduces a temporal-hierarchical memory framework using a Temporal Memory Tree (TMT) to progressively consolidate raw dialog into abstract persona representations, emphasizing temporal continuity. • Core Mechanisms: Features semantic-guided consolidation (fine-tuning free) and complexity-aware memory recall (Recall Planner + Gating) to balance precision and efficiency across query types. • Performance: Achieves SOTA accuracy on LoCoMo (75.30%) and LongMemEval-S (76.88%) benchmarks, while significantly reducing recalled context length (-52.20%) on LoCoMo.
2026-01-06	MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory
	• MemRL Framework: Proposes a non-parametric reinforcement learning framework that enables frozen LLM agents to self-evolve by optimizing episodic memory, avoiding the costs and forgetting issues of fine-tuning. • Intent-Experience-Utility Triplet: Introduces a Two-Phase Retrieval mechanism (semantic recall + value-aware selection) and a runtime utility update rule, using Q-values to distinguish high-utility strategies from noise. • Performance: Significantly outperforms MemP and RAG on benchmarks like HLE, BigCodeBench, and ALFWorld, demonstrating that agents can continuously improve via runtime trial-and-error without weight updates.
2026-01-05	SimpleMem: Efficient Lifelong Memory for LLM Agents
	• Introduces SimpleMem, an efficient memory framework tailored for lifelong LLM agents based on semantic lossless compression. • The system operates via a three-stage pipeline: Semantic Structured Compression to filter low-entropy noise, Recursive Memory Consolidation to synthesize abstract representations, and Adaptive Query-Aware Retrieval to minimize token usage. • Experiments on the LoCoMo benchmark demonstrate a 26.4% improvement in F1 score and up to 30× reduction in inference token consumption compared to full-context models, significantly outperforming baselines like Mem0.
2026-01-05	Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents
	• AgeMem Framework: Proposes a unified framework that integrates both Long-Term (LTM) and Short-Term (STM) memory management directly into the agent's policy via tool-based actions (e.g., Add, Update, Filter). • Three-Stage Progressive RL: Introduces a step-wise GRPO algorithm and a three-stage training strategy (LTM construction, STM control, integrated reasoning) to address sparse rewards and enable end-to-end optimization. • Performance: Outperforms strong baselines like LangMem and Mem0 across five long-horizon benchmarks (e.g., ALFWorld, HotpotQA), achieving higher task success rates, better memory quality, and more efficient context usage.
2025-12-31	Nested Learning: The Illusion of Deep Learning Architecture
	• Full arXiv version including all appendices — not the previously released trimmed version. • Presents a Nested Learning paradigm that unifies a large portion of optimizer and TTT-layer modules. • Architectural innovation: HOPE — composed of modified Titans attention and self-modified FFNs. By controlling the chunksize of self-modification of FFN parameter, FFN layers operating at different frequencies implicitly retain memories at different hierarchical levels during runtime. • Empirical results are modest.
2025-12-25	Beyond Heuristics: A Decision-Theoretic Framework for Agent Memory Management
	• Provides TeleAI background and introduces a decision-theoretic memory framework (DAM) that formulates the timing and content of memory read/write as an optimal decision problem, with relevance to RL-style formulations. • Contains minimal or no experimental validation.
2025-12-21	MemEvolve: Meta-Evolution of Agent Memory Systems
	• An OPPO-affiliated paper proposes a two-layer framework that, in RL settings, separates learning to extract memories (level-1) from learning the memory-extraction method itself (level-2). • Experiments using Flash-Searcher and GPT-5-Mini achieve SOTA on benchmarks including GAIA.
2025-12-20	MemR³: Memory Retrieval via Reflective Reasoning for LLM Agents
	• MemR³ closed-loop retrieval controller: designed for long-term conversational memory, it can dynamically choose among three actions—retrieve, reflect, and respond. • Evidence–gap state tracker: the system maintains a global (evidence, gap) state that explicitly tracks "what is known" and "what is missing," making the process interpretable. • Experiments show that on the LoCoMo benchmark, MemR³ significantly improves answer quality across different underlying memory systems (e.g., RAG, Zep).
2025-12-18	Learning Hierarchical Procedural Memory for LLM Agents through Bayesian Selection and Contrastive Refinement
	• A Bayesian procedural memory (experience) framework: MACLA. • Overall still a rule-based algorithm; operations include extraction, retrieval/storage, and refinement (Bayesian posterior calibration). • On unseen tasks in ALFWorld, performance (90.3%) exceeded that on seen tasks (87.2%), achieving +3.1% positive generalization.
2025-12-14	HINDSIGHT IS 20/20: BUILDING AGENT MEMORY THAT RETAINS, RECALLS, AND REFLECTS
	• HINDSIGHT is a unified memory architecture that treats memory as a structured, first-class substrate for reasoning, organizing information into four logical networks: world facts, agent experiences, synthesized entity summaries, and evolving beliefs. • The system introduces TEMPR (Temporal Entity Memory Priming Retrieval) for building temporal entity graphs and CARA (Coherent Adaptive Reasoning Agents) for preference-conditioned reasoning, enabling agents to epistemically distinguish evidence from inference. • Experimental results on LongMemEval and LoCoMo benchmarks demonstrate that HINDSIGHT significantly outperforms existing memory systems and full-context frontier models in multi-session consistency and open-domain question answering.
2025-12-11	Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution
	• ReMe (paper version): an Alibaba-affiliated framework for enhancing LLM procedural memory (experience), including the ReMe algorithm and the reme.library dataset. • Core idea: maintain an experience pool with operations—Acquisition, Reuse, and Refinement. • Experiments on BFCL-V3 and AppWorld show dynamic experience pools > static pools > baseline, with scaling studies for both model and judge models.
2025-12-10	LightSearcher: Efficient DeepSearch via Experiential Memory
	• LightSearcher is an efficient reinforcement learning (RL)-based search architecture grounded in experiential memory. During large language model (LLM)-driven reasoning, it autonomously optimizes agent tool invocation without relying on external data by transforming implicit reasoning trajectories into explicit experiential guidance through contrastive experiential memory. • Evaluated on four multi-hop question answering benchmarks—Natural Questions (NQ), HotpotQA, MuSiQue, and 2WikiMultihopQA—LightSearcher achieves accuracy comparable to the state-of-the-art DeepSearch baseline while significantly reducing both tool invocation latency and model response time. • The method reduces tool invocations by 39.6%, shortens reasoning time by 48.6%, and decreases token consumption by 21.2%, substantially improving tool-use efficiency without compromising task performance.
2025-12-3	MemVerse: Multimodal Memory for Lifelong Learning Agents
	• A lifelong learning memory framework for multimodal agents. • Retrieval-based long-term memory + parameterized fast memory + periodic distillation. • Multimodal handling: unified conversion into textual descriptions. • Experiments show improvements over baselines on ScienceQA (text) and MSR-VTT (video); LoCoMo (text) results remain unpublished (in appendix).
2025-11-12	ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning
	• Introduces ComoRAG, a retrieval-augmented generation framework inspired by the human Prefrontal Cortex, designed to achieve stateful reasoning in long narrative contexts. • The framework employs a dynamic memory workspace and a metacognitive regulation loop (including Self-Probe, Mem-Fuse, and Mem-Update) to iteratively fuse fragmented evidence into coherent context. • Experimental results demonstrate that ComoRAG consistently outperforms strong baselines on challenging benchmarks like NarrativeQA and ∞BENCH, particularly excelling in complex narrative queries requiring global understanding.
2025-11-04	MemSearcher Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning
	• MemSearcher is a large language model (LLMs) agent trained through end-to-end Reinforcement Learning (RL), aiming to enhance the efficiency of knowledge acquisition tasks. • MemSearcher optimizes memory management by adopting a new framework called multi-context Group Relative Strategy Optimization (Multi-Context GRPO), which enables the model to self-evolve in multiple conversations. • Compared with traditional ReAct search agents, MemSearcher offers significant performance improvements while maintaining low token consumption, especially on smaller models.
2025-10-15	D-SMART: Enhancing LLM Dialogue Consistency via Dynamic Structured Memory And Reasoning Tree
	• Proposes D-SMART, a model-agnostic framework designed to maintain logical and factual consistency in multi-turn dialogues by coupling a Dynamic Structured Memory (DSM) with a Reasoning Tree (RT). • DSM incrementally builds an OWL-compliant knowledge graph from conversation history to prevent context decay, while RT guides the LLM through explicit, traceable multi-step reasoning over this graph. • Comprehensive experiments on MT-Bench-101 demonstrate that D-SMART significantly outperforms state-of-the-art baselines, improving consistency scores by over 48% and exhibiting strong stability in extended dialogues.
2025-10-14	Memory as Action Autonomous Context Curation for Long-Horizon Agentic Tasks
	• Memory-as-action (MemAct) addresses the issue of working Memory management for large language models (LLMS) in long-duration tasks. • MemAct transforms memory management into a learnable intrinsic capability, enabling agents to dynamically manage memories while performing tasks, and introduces the Dynamic Context Policy Optimization (DCPO) algorithm to handle the trajectory breakage problem caused by memory editing. • MemAct performs exceptionally well in multi-objective question answering tasks, demonstrating higher accuracy and robustness than traditional models.
2025-10-12	MemGen Weaving Generative Latent Memory for Self-Evolving Agents
	• MemGen is a dynamic generative memory framework designed to enhance the reasoning and decision-making capabilities of agents based on large language models (LLMS). • MemGen simulates human cognitive patterns by interweaving memory with the reasoning process. • This framework consists of two parts: memory triggers and memory weavers, which can dynamically determine when to invoke potential memories and integrate them into the reasoning process.
2025-10-10	How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior
	• The paper investigates memory management in large language model (LLM) agents and its impact on long-term performance. • It identifies issues such as error propagation and misaligned experience replay, highlighting the importance of high-quality memory. • By comparing multiple memory insertion and deletion strategies, the study finds that selective insertion performs better for long-term learning, while historical deletion is particularly effective at reducing low-quality memory records.
2025-10-09	Enabling Personalized Long-term Interactions in LLM-based Agents through Persistent Memory and User Profiles
	• Introduces a framework for adaptive, user-centered AI agents that combines persistent memory, dynamic coordination, and evolving user profiles to enable personalized long-term interactions. • The approach integrates established agentic AI patterns—such as Multi-Agent Collaboration and Multi-Source Retrieval—with mechanisms like self-validation and implicit user profiling to tailor responses to individual needs. • Evaluations on three public datasets and a pilot user study demonstrate improvements in retrieval accuracy, response correctness, and perceived personalization compared to standard RAG baselines.
2025-10-08	ToolMem: Enhancing Multimodal Agents with Learnable Tool Capability Memory
	• TOOLMEM, a memory-augmented agent that learns from past tool use. It stores summarized, retrievable “what this tool is good/bad at” knowledge and injects the relevant memories into context to better predict tool quality and choose the right tool for new tasks. • TOOLMEM maintains structured capability entries per tool. From each experience, it retrieves similar memories and updates them via a RAG-style merge/refinement, keeping a compact, evolving capability memory. At inference time, it retrieves the most relevant capability memories to guide scoring and tool selection. • They evaluate on text generation tools and text-to-image tools, comparing against no-memory and few-shot baselines. TOOLMEM improves quality prediction and makes better tool choices overall.
2025-10-07	CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension
	• The paper introduces CAM, a Constructivist Agentic Memory system inspired by Jean Piaget’s theory, designed to enhance Large Language Models (LLMs) in long-form document comprehension. • CAM features structured schemata, flexible assimilation, and dynamic accommodation, utilizing an incremental overlapping clustering algorithm for efficient memory development and an adaptive Prune-and-Grow strategy for retrieval. • Experimental results across diverse benchmarks show that CAM achieves dual advantages in both performance and efficiency compared to existing structured and unstructured memory approaches.
2025-09-30	MEM-α: LEARNING MEMORY CONSTRUCTION VIA REINFORCEMENT LEARNING
	• Proposes Mem-α, a reinforcement learning framework that trains agents to effectively manage complex memory systems (comprising core, episodic, and semantic components) through interaction and feedback. • Unlike approaches relying on pre-defined instructions, Mem-α treats memory construction as a sequential decision-making problem, optimizing directly for downstream question-answering accuracy. • Experimental results show that Mem-α significantly outperforms existing baselines and demonstrates remarkable generalization, effectively handling contexts exceeding 400k tokens despite being trained on 30k token sequences.
2025-09-29	ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory
	• ReasoningBank, a test-time learning framework that distills an agent’s own successful and failed trajectories into reusable reasoning memories. For new tasks, the agent retrieves relevant memories to guide decision-making and then writes new experience back into the bank, forming a self-improving loop without requiring ground-truth feedback. • Each memory is stored as a compact structured item and retrieved via embedding similarity (top-k) to augment the agent’s prompt. After task execution, an LLM-as-a-judge provides proxy success/failure signals: successful trajectories yield transferable strategies, while failed ones yield pitfalls and corrective rules. In addition, MaTTS expands test-time computation through parallel trajectory sampling and serial self-reflection, both of which generate stronger memory signals. • Experiments are conducted on WebArena and Mind2Web and SWE-Bench-Verified, comparing against No Memory and prior memory-based baselines. Performance is evaluated using success rate, efficiency (steps), and task-specific metrics. Results show consistent improvements across different backbone models
2025-09-29	Pretraining with hierarchical memories: separating long-tail and common knowledge
	• Proposes a "pretraining-with-memories" architecture that decouples reasoning capabilities (anchor model) from long-tail world knowledge (hierarchical memory bank). • The system dynamically retrieves and attaches context-dependent parameter blocks from a massive memory bank to a small anchor model during inference, enabling efficient scaling. • Experiments demonstrate that a 160M model augmented with memories matches the performance of a standard model with over twice the parameters, specifically excelling at long-tail knowledge tasks.
2025-09-27	Look Back to Reason Forward: Revisitable Memory for Long-Context LLM Agents
	• Addressed long-context reasoning where relevant evidence is dispersed across very long inputs. • Proposed a “revisitable” memory design that allows the agent to look back and selectively retrieve from the broader history. • Evaluated on long-context QA settings to show improved evidence recovery and reasoning accuracy.
2025-09-26	Conflict-Aware Soft Prompting for Retrieval-Augmented Generation
	• The "Conflict-Aware Retrieval Enhancement Generation" (CARE) model aims to address the context-memory conflict problem that occurs in Retrieval Enhancement Generation (RAG). • CARE optimizes the performance of large language models (LLMs) by introducing context evaluators, especially in dealing with conflicts between external and internal knowledge. • This method significantly enhances the accuracy and reliability of the model in multiple tasks through techniques such as conflict-aware fine-tuning, soft prompts, and adversarial soft prompts.
2025-09-26	PRIME Planning and Retrieval-Integrated Memory for Enhanced Reasoning
	• PRIME is a multi-agent inference framework. PRIME provides intuitive answers to simple questions through fast-response agents. • PRIME performs complex reasoning through multiple specific agents, such as memory, planning, search and reading agents. • PRIME still needs to improve its belief correction mechanism and optimize the interaction among agents.
2025-09-25	SGMEM: Sentence Graph Memory for Long-Term Conversational Agents
	• SGMem is a hierarchical memory management framework designed to address memory fragmentation in long-term conversational agents by organizing dialogue into sentence-level graphs. • It explicitly models associations across turns, rounds, and sessions, and uses a multi-hop retrieval mechanism to integrate raw dialogue history with generated memory such as summaries, facts, and insights. • Extensive experiments on LongMemEval and LoCoMo benchmarks demonstrate that SGMem consistently improves retrieval coherence and outperforms strong baselines in question answering accuracy.
2025-09-22	PRINCIPLES: Synthetic Strategy Memory for Proactive Dialogue Agents
	• PRINCIPLES builds a retrievable memory of dialogue strategy principles from offline self-play. At inference time, the model retrieves and applies these principles to guide strategy selection and response generation, without any additional training. • In the offline stage, the agent conducts multi-turn self-play with a user simulator and uses rewards to identify success or failure. Successful cases directly yield principles, while failed cases trigger strategy revision and rollback until success; principles are then extracted by contrasting failure-to-success trajectories in a structured form. In the online stage, relevant principles are retrieved using contextual embeddings, reinterpreted to fit the current dialogue, and then used to guide planning and response generation. • Experiments on emotional support and persuasion tasks show that PRINCIPLES improves success rates and strategy prediction performance while increasing strategy diversity. Ablation studies confirm the importance of retrieval and reinterpretation, and human evaluations indicate overall preference for the proposed method.
2025-09-16	WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research
	• Introduces WebWeaver, a dual-agent framework comprising a Planner and a Writer designed to tackle open-ended deep research (OEDR) by emulating human research processes. • The Planner uses a dynamic cycle to interleave evidence acquisition with outline optimization, building a memory bank of evidence; the Writer performs hierarchical, citation-grounded retrieval to compose the report section by section. • WebWeaver achieves state-of-the-art performance on benchmarks like DeepResearch Bench by effectively managing long contexts and mitigating hallucinations through targeted memory retrieval.
2025-09-15	MOOM: Maintenance, Organization and Optimization of Memory in Ultra-Long Role-Playing Dialogues
	• MOOM is a dual-branch memory extraction framework designed for ultra-long role-playing dialogues, modeling "plot development" and "character portrayal" as core storytelling elements. • It incorporates a novel forgetting mechanism based on "competition-inhibition" theory to effectively control memory capacity and prevent uncontrolled expansion. • The authors introduce ZH-4O, a large-scale Chinese role-playing dataset with average 600-turn dialogues and manual memory annotations, demonstrating MOOM's superior performance over state-of-the-art methods.
2025-09-13	Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue
	• PREMem (Pre-storage Reasoning for Episodic Memory) is a novel approach that shifts complex reasoning processes from response generation to the memory construction phase. • It extracts fine-grained memory fragments (categorized into factual, experiential, and subjective information) and establishes explicit cross-session relationships based on cognitive schema theory, capturing evolution patterns like extensions and transformations. • Experiments on LongMemEval and LoCoMo benchmarks show significant performance improvements, enabling smaller models to achieve results comparable to larger baselines while reducing inference computational demands.
2025-09-11	OpenUnlearning:Accelerating LLM unlearning via unified benchmarking of methods and metrics
	• Introduces the “OpenUnlearning” framework, designed to advance research on unlearning in large language models (LLMs). • OpenUnlearning integrates a wide range of unlearning algorithms and evaluation methods, streamlining the research workflow for studying forgetting. • Through targeted and task-specific evaluations, OpenUnlearning ensures the credibility and robustness of unlearning assessment standards.
2025-08-27	Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
	• Memory-R1 is an RL-driven framework that empowers LLMs to actively manage and utilize external memory via two specialized agents: a Memory Manager and an Answer Agent. • The Memory Manager learns structured operations (ADD, UPDATE, DELETE) to maintain memory, while the Answer Agent filters retrieved memories for accurate reasoning. • With only 152 training samples, it outperforms strong baselines on LoCoMo, MSC, and LongMemEval, demonstrating high data efficiency and generalization.
2025-08-26	MemoryVLA Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation
	• MemoryVLA is a newly developed robot operation framework, aiming to enhance the performance of robots in complex tasks by integrating visual, language, and perception-cognitive mechanisms. • This framework adopts an architecture similar to the human dual memory system, enhancing the robot's ability to handle long-sequence tasks. • MemoryVLA introduces perception-cognitive memory banks (PCMB), which can effectively integrate historical information with current decisions, thereby enhancing the success rate of robots in responding to complex scenarios.
2025-08-22	Memento: Fine-tuning LLM Agents without Fine-tuning LLMs
	• Proposed a paradigm to improve agent behavior via “agent-side” learning while keeping the base LLM frozen. • Focused on adapting the agent’s components (e.g., memory/reasoning/routing) rather than model weights. • Reported performance gains across agent tasks without conventional LLM fine-tuning.
2025-08-21	Multiple Memory Systems for Enhancing the Long-term Memory of Agent
	• Proposes a Multiple Memory System (MMS) inspired by cognitive psychology to address the issue of low-quality memory content in existing agent memory modules. • The system processes short-term memory into diverse fragments—keywords, cognitive perspectives, episodic memory, and semantic memory—to construct specialized retrieval and contextual memory units. • Experimental results on the LoCoMo dataset demonstrate that MMS significantly outperforms methods like MemoryBank and A-MEM, particularly in multi-hop reasoning and open-domain tasks.
2025-08-18	Semantic Anchoring in Agentic Memory: Leveraging Linguistic Structures for Persistent Conversational Context
	• Semantic Anchoring is a hybrid agentic memory architecture designed to enhance the long-term context retention of LLMs by enriching vector-based storage with explicit linguistic cues such as syntactic dependencies, discourse relations, and coreference links. • The proposed framework employs a multi-stage pipeline involving dependency parsing, coreference resolution, and discourse tagging to construct a hybrid index, allowing retrieval systems to access memories based on both semantic similarity and structural linguistic roles. • Experimental results on adapted long-term dialogue datasets (MultiWOZ-Long and DialogRE-L) demonstrate that Semantic Anchoring outperforms strong RAG baselines, improving factual recall and discourse coherence by up to 18% while maintaining higher user satisfaction.
2025-08-13	Memp: Exploring Agent Procedural Memory
	• Memp treats procedural memory as an external, learnable store of past successful experiences so an LLM agent can reuse effective “how-to” routines on new tasks, improving success and reducing wasted steps. • Memp follows a Build–Retrieve–Update loop: it builds memory items from trajectories/scripts, retrieves the most relevant items via semantic keys and vector similarity, and updates memory online by adding, filtering, and correcting items so the memory becomes more reliable over time. • On TravelPlanner and ALFWorld, Memp outperforms a ReAct baseline with higher success/score and fewer steps; vector-based retrieval beats random selection; online updates yield further gains, and learned memories can transfer from stronger to weaker models with diminishing returns as retrieval size grows.
2025-08-12	Context as Memory Scene-Consistent Interactive Long Video Generation with Memory Retrieval
	• "Context-as-memory" significantly enhances the scene consistency and Memory capacity of long video generation by leveraging historical Context as memory. • The paper studies key designs such as context learning mechanisms, camera control, and memory retrieval strategies, and points out the balance between computational efficiency and generation quality. • Based on the long video generation architecture of the diffusion model, the current technological progress, challenges and future directions are expounded.
2025-08-12	Intrinsic Memory Agents: Heterogeneous Multi-Agent LLM Systems through Structured Contextual Memory
	• Introduces Intrinsic Memory Agents, a multi-agent framework designed to address context limitations and role inconsistency using structured, agent-specific memories. • The method employs role-aligned memory templates and intrinsic updates derived directly from agent outputs, preserving heterogeneous perspectives and domain expertise without external summarization. • Evaluations on the PDDL benchmark demonstrate a 38.6% performance improvement with high token efficiency, while case studies show enhanced quality in complex planning tasks.
2025-08-06	RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory
	• RCR-Router is a role-aware context routing framework designed for multi-agent LLM systems to address the limitations of static and full-context routing, such as excessive token consumption and redundant memory exposure. • The framework dynamically selects semantically relevant memory subsets for each agent based on their specific role and the current task stage, enforcing a strict token budget and utilizing an iterative feedback mechanism to refine context. • Experiments on multi-hop QA benchmarks (HotPotQA, MuSiQue, 2WikiMultihop) demonstrate that RCR-Router reduces token usage by 25–47% while maintaining or improving answer quality compared to baseline strategies.
2025-08-03	MLP Memory: A Retriever-Pretrained Memory for Large Language Models
	• Introduces MLP Memory, a lightweight parametric module that learns to internalize retrieval patterns without requiring explicit document access during inference, effectively bridging the gap between RAG and parametric fine-tuning. • By pretraining an MLP to imitate a kNN retriever’s behavior on the entire pretraining dataset, the model compresses large datastores into a differentiable memory component that integrates with Transformer decoders via probability interpolation. • Experimental results show that MLP Memory achieves superior scaling behavior, improves QA performance by 12.3% relative to baselines, reduces hallucinations by up to 10 points, and offers 2.5× faster inference than RAG.
2025-07-29	SynapticRAG:Enhancing temporal memory retrieval in large language models through synaptic mechanisms
	• The paper proposes MemTool, a short-term memory framework for managing dynamic tool sets across multi-turn conversations. It offers three architectures: Autonomous Agent, Workflow, and Hybrid, balancing autonomy and control. • In Autonomous Mode, the agent autonomously adds/removes tools using Search_Tools and Remove_Tools. Workflow Mode follows a fixed pipeline: pruning tools, then searching and adding new ones. Hybrid Mode separates tool removal and adding, offering a balance of stability and flexibility. • Using ScaleMCP’s 5,000 MCP servers and a 100-turn dialogue, the authors evaluate 13 LLMs with a 128-tool limit. Autonomous Mode achieves 90-94% tool removal efficiency, while Workflow and Hybrid perform consistently well, with Autonomous and Hybrid excelling in task completion.
2025-07-27	SynapticRAG:Enhancing temporal memory retrieval in large language models through synaptic mechanisms
	• SynapticRAG is a novel memory retrieval framework for large language models (LLMs), designed to enhance memory retrieval in cross-session conversations. • By combining temporal association triggers with biologically inspired synaptic propagation mechanisms, SynapticRAG significantly improves the identification of relevant conversational history. • Experimental results show that the framework achieves improvements of up to 14.66% across multiple performance metrics and demonstrates clear advantages in dynamic memory management.
2025-07-17	MEM1 Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents
	• MEM1 is an innovative end-to-end reinforcement learning framework designed to enhance the efficiency of large language models (LLMs) in long-term multi-round interactions. • MEM1 effectively solves the problem of memory dilation in context processing of traditional models by constructing a compact shared internal state. • The experimental results show that MEM1 significantly improves performance in multiple tasks while reducing memory usage, demonstrating its wide applicability and optimization potential in dynamic environments.
2025-07-03	MemAgent Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent
	• MemAgent is a long text processing method that uses reinforcement learning (RL) to dynamically update memory, aiming to address the performance degradation and high computational complexity issues of large language models (LLMS) when dealing with long texts. • The model can maintain a linear time complexity while handling inputs of infinite length by treating memory as a latent variable and introducing stream processing and multi-session strategies. • The experimental results show that MemAgent performs outstandingly with high accuracy in ultra-long text tasks, especially having obvious advantages in complex multi-hop reasoning tasks.
2025-06-19	From RAG to Memory: Non-Parametric Continual Learning for Large Language Models
	• The paper proposes HippoRAG 2, a “long-term memory–inspired” structured RAG system. It builds a knowledge graph from text and retrieves evidence via graph-based propagation (PPR) to support multi-hop association, while improving basic factual recall that earlier structured RAGs often hurt. • Offline, an LLM performs OpenIE to extract triples and form a KG, and adds passages as nodes linked to phrase nodes to fuse concept-level structure with context-rich passages. Online, it first retrieves top-k triples with embeddings, then uses an LLM for triple filtering to remove irrelevant triples; the remaining nodes seed a PPR run to rank the most relevant passages for the generator. • It evaluates factual QA, multi-hop reasoning, and narrative understanding, reporting Recall@5 for retrieval and F1 for QA. Compared with BM25, dense retrievers, and multiple structured-RAG baselines, HippoRAG 2 generally improves retrieval and end-to-end QA, and ablations plus “growing-corpus” settings support the contribution of its components.
2025-06-09	G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems
	• Introduces G-Memory, a hierarchical memory system designed to address the lack of self-evolution capabilities in Large Language Model (LLM)-based Multi-Agent Systems (MAS). • Implements a three-tier graph architecture—Insight Graph, Query Graph, and Interaction Graph—to manage lengthy interaction histories by abstracting generalizable insights and condensing specific collaborative trajectories. • Experimental results across embodied action and knowledge QA benchmarks demonstrate that G-Memory significantly enhances agent team performance, improving success rates by up to 20.89% without modifying the original frameworks.
2025-05-30	M+：Extending MemoryLLM with scalable Long-Term Memory
	• M+ is a memory-augmented model designed to improve long-term information retention in large language models (LLMs). • Built upon MemoryLLM, M+ integrates long-term memory mechanisms with a jointly trained retriever, substantially enhancing the model’s ability to handle knowledge spanning over 20,000 tokens while maintaining comparable GPU memory overhead. • M+ achieves strong performance across multiple benchmarks, outperforming MemoryLLM and other competitive baselines, and demonstrates efficient information compression and end-to-end training, exhibiting mechanisms that closely resemble human memory.
2025-05-26	MemGuide: Intent-Driven Memory Selection for Goal-Oriented Multi-Session LLM Agents
	• MemGuide is a two-stage framework designed to enhance multi-session task-oriented dialogue (TOD) by incorporating task intent and slot-level guidance into memory selection. • It employs Intent-Aligned Retrieval to match current context with stored intent descriptions and Missing-Slot Guided Filtering to prioritize memory units that fill information gaps using a Chain-of-Thought reasoner. • The authors also introduce MS-TOD, a multi-session TOD benchmark. Evaluations show MemGuide significantly improves task success rates and reduces dialogue turns compared to strong baselines.
2025-05-23	Towards General Continuous Memory for Vision-Language Models
	• CoMEM addresses the token overload and performance degradation issues in traditional Retrieval-Augmented Generation (RAG) for Vision-Language Models (VLMs) by introducing a general continuous memory mechanism. • The method innovatively utilizes the VLM itself as a memory encoder combined with a lightweight Q-Former, efficiently compressing diverse multimodal and multilingual knowledge into a compact set of continuous embeddings. • CoMEM is data- and parameter-efficient (requiring only 1.2% trainable parameters) and plug-and-play, significantly enhancing performance on complex multimodal reasoning tasks while keeping the inference model frozen.
2025-05-21	Pre-training Limited Memory Language Models with Internal and External Knowledge
	• Introduces Limited Memory Language Models (LMLM), a new class of models that externalizes factual knowledge to an external database during pre-training rather than encoding it in parameters. • The approach uses a modified pre-training objective that masks retrieved factual values from the loss, encouraging the model to perform targeted lookups for facts instead of memorizing them. • Experiments demonstrate that LMLMs match the factual precision of significantly larger models while enabling instant, verifiable knowledge updates and effective machine unlearning through simple database operations.
2025-05-11	In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents
	• Proposes Reflective Memory Management (RMM), a novel framework for long-term dialogue agents that addresses the limitations of rigid memory granularity and fixed retrieval mechanisms. • Integrates Prospective Reflection to dynamically organize dialogue history into topic-based memories, and Retrospective Reflection to iteratively refine retrieval using online reinforcement learning guided by LLM attribution signals. • Experimental results on MSC and LongMemEval benchmarks demonstrate that RMM significantly outperforms strong baselines, achieving over 10% improvement in accuracy and enhancing response personalization.
2025-04-22	MemoRAG Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation
	• MemoRAG aims to enhance the ability of large language models (LLMs) in handling long contexts by improving the information retrieval and generation process through a global memory-enhanced retrieval mechanism. • This framework adopts a lightweight global memory module and a complex generation system, which can effectively manage long contexts and generate useful clues to assist in answer generation. • This model is applicable to a variety of tasks, including long document question answering and summarization, demonstrating its potential in handling complex long text scenarios.
2025-04-20	SAGE: Self-evolving Agents with Reflective and Memory-augmented Abilities
	• SAGE addresses the long-term memory and multitasking challenges of large language models (LLMs) in dynamic environments through three collaborative agents. SAGE integrates a reflection mechanism and memory optimization based on the Ebbinghaus forgetting curve, helping the model effectively filter and store important information while reducing cognitive load. • SAGE continuously optimizes the Assistant’s decisions through an iterative feedback mechanism and reflection functionality. Its MemorySyntax component simulates human memory decay, dynamically managing both short-term and long-term memo