Hybrid Retrieval Failure

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When hybrid retrieval (BM25 + dense, HyDE + reranker, multi-vector) performs worse than a single retriever.
Instead of increasing recall, the hybrid path introduces instability, wrong ranking, or noisy snippets.

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Open these first

• Visual map and recovery: RAG Architecture & Recovery
• End to end retrieval knobs: Retrieval Playbook
• Traceability schema: Retrieval Traceability
• Snippet contracts: Data Contracts
• Query path splits: Pattern: Query Parsing Split
• Ranking drift: Rerankers

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Core acceptance

• Hybrid recall ≥ single retriever recall
• ΔS(question, retrieved) ≤ 0.45 for top-1 result
• λ stable across three paraphrases and two seeds
• Coverage ≥ 0.70 to the target section

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Typical symptoms → exact fix

Symptom	Likely cause	Open this
Hybrid returns unrelated snippet	query parsing split not locked	Pattern: Query Parsing Split
Hybrid recall < single recall	wrong weighting or missing normalization	Retrieval Playbook
Dense retriever dominates BM25	metric mismatch	Embedding ≠ Semantic
Reranker undoes good hits	λ flips, entropy collapse	Rerankers, Entropy Collapse

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Fix in 60 seconds

1. Measure baseline
   Run BM25 alone and dense alone. Log coverage and ΔS. If hybrid < baseline, do not ship.

2. Stabilize query parsing
   Split HyDE prompts, keyword queries, and dense embeddings into deterministic branches. Lock weighting ratios.

3. Reranker probe
   Compare recall before and after reranker. If entropy rises, clamp with variance control or drop reranker.

4. Enforce snippet schema
   Always require snippet_id, section_id, offsets, tokens. Hybrid paths must normalize schema fields.

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Copy-paste probe prompt

I uploaded TXT OS and the WFGY Problem Map.

My issue:
- hybrid retrieval returns worse results than BM25 or dense alone.

Tell me:
1) which layer fails (query parsing, weighting, reranker),
2) which WFGY fix page to open,
3) minimal steps to restore ΔS ≤ 0.45 and coverage ≥ 0.70,
4) reproducible test with BM25 vs dense vs hybrid.

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🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + ”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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Explore More

Layer	Page	What it’s for
⭐ Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
⚙️ Engine	WFGY 1.0	Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine	WFGY 2.0	Production tension kernel for RAG and agent systems
⚙️ Engine	WFGY 3.0	TXT based Singularity tension engine (131 S class set)
🗺️ Map	Problem Map 1.0	Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map	Problem Map 2.0	Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map	Problem Map 3.0	Global AI troubleshooting atlas and failure pattern map
🧰 App	TXT OS	.txt semantic OS with fast bootstrap
🧰 App	Blah Blah Blah	Abstract and paradox Q&A built on TXT OS
🧰 App	Blur Blur Blur	Text to image generation with semantic control
🏡 Onboarding	Starter Village	Guided entry point for new users

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