Constructivist Agentic Memory

[📄 Paper] CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension

CAM (Constructivist Agentic Memory) is a constructivist-inspired memory framework that improves long-form reading comprehension for LLM-based agents. This is an early-stage release, and we plan to extend CAM with more advanced capabilities in our future work.

🛠️ Requirements

• python == 3.9.19
• numpy == 1.26.4
• tqdm == 4.66.1
• datasets == 2.20.0
• networkx == 3.4.2
• openai == 0.28.0
• rouge-score == 0.1.2
• scikit-learn == 1.6.1

📚 Datasets

Dataset	Task	Access
NovelQA	Narrative QA	Request Access
FABLES	Claim Verification	Request Access
QMSum	Query-Based Summarization	GitHub
ODSum	Query-Based Summarization	GitHub
MultiHop-RAG	Multi-Hop Reasoning	GitHub

QMSum, ODSum, and MultiHop-RAG are publicly available. For NovelQA and FABLES, full documents and ground truth labels are not released to prevent data contamination. Please contact the original authors to request access.

🚀 Quick Start

1. Document chunking:

python prototype/chunks.py --dataset <dataset_name> --chunk_size <maximum number of tokens per chunk>

2. Generate chunk embeddings and extract salient entities (optional) using an LLM:

python prototype/preprocess_chunks.py --dataset <dataset_name> --model <LLM model to use> --embedding model <embedding model to use> --generate_gist --extract_entity

This step creates a folder ./processed_data/ containing chunk embeddings and metadata.

3. Constructivist memory construction:

python prototype/constructivist_memory.py --dataset <dataset_name> --chunk_size <maximum number of tokens per chunk> --threshold <edge activation threshold> --weight <weight for text similarity vs proximity> --sigma <sigma for Gaussian proximity similarity> --k <top-k neighbors per node> --max_cluster_size <maximum nodes allowed in one cluster> --max_hierarchy_level <maximum hierarchy levels> --model <LLM model to use> --embedding model <embedding model to use>

This step creates ./super_graphs/ (memory structures) and ./super_embeddings/ (node embeddings).

4. Prune-and-Grow inference for NovelQA (multichoice setting):

python prototype/tasks/question_answering.py --dataset NovelQA --mode MC

Outputs are stored in the ./output/ directory.

📜 Acknowledgement

This project builds on insights from developmental cognitive theory, graph learning, and long-context LLM research. We thank the authors of the benchmark datasets for their valuable contributions.