# Redundant Evidence Collapse: Guardrails and Fix Pattern
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When many near-identical snippets flood the context, the model over-trusts repetition and ignores minority evidence. Plans drift, citations skew to one source, and answers flatten. Use this page to dedupe, cap source dominance, and keep reasoning balanced. --- ## Open these first - Visual map and recovery → [rag-architecture-and-recovery.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) - End to end retrieval knobs → [retrieval-playbook.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md) - Traceability and payload schema → [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) → [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md) - Related retrieval failures → [duplication_and_near_duplicate_collapse.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/duplication_and_near_duplicate_collapse.md) · [pattern_vectorstore_fragmentation.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_vectorstore_fragmentation.md) · [hybrid_retriever_weights.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/hybrid_retriever_weights.md) - Reasoning stability tools → [chain-of-thought-variance-clamp.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Reasoning/chain-of-thought-variance-clamp.md) · [anchoring-and-bridge-proofs.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Reasoning/anchoring-and-bridge-proofs.md) · [context-stitching-and-window-joins.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Reasoning/context-stitching-and-window-joins.md) --- ## Symptoms | Symptom | What you see | |---|---| | Majority echo | 70–90 percent of citations come from one source family | | Minority facts vanish | Correct but less frequent evidence never appears in the answer | | Plan flips with k | Increasing top-k changes conclusion even though meaning is the same | | Reruns reshuffle | Same inputs but different top-k mixes cause different claims | | JSON plan collapses | One long “summarize all” step instead of compare and weigh | --- ## Why it happens 1) **Near-duplicate clutter**. Chunks differ in offsets but carry the same claim. 2) **Per-source dominance**. One document type or site overruns the window. 3) **No cluster caps**. Reranker optimizes relevance, not diversity. 4) **Free-form plan**. Planner merges collect and decide into a single step. 5) **No minority probe**. Chains never force a best counterexample search. 6) **λ not observed**. Variance looks like disagreement instead of imbalance. --- ## Acceptance targets - Coverage of target section ≥ 0.70 and includes at least 1 minority citation when conflicts exist - Per-source cap ≤ 40 percent of active snippets in any window - Near-duplicate rate ≤ 10 percent by cluster (Jaccard or embedding distance) - ΔS(question, selected\_evidence) ≤ 0.45 and flat when k varies between 8 and 24 - λ remains convergent across three paraphrases and two seeds --- ## Fix in 60 seconds 1) **Cluster and cap** Cluster snippets by `{source_id, section_id}` and by semantic LSH. Keep `top 1–2` per cluster. Cap any source family at 40 percent of window size. → [duplication_and_near_duplicate_collapse.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/duplication_and_near_duplicate_collapse.md) 2) **Deterministic tie break** After rerank, order by `(doc_id, section_id, win_idx)` so runs are stable. → [rerankers.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md) 3) **Split plan into compare then decide** Use BBAM to clamp step count. Stage A collects balanced evidence, Stage B decides. → [chain-of-thought-variance-clamp.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Reasoning/chain-of-thought-variance-clamp.md) 4) **Minority probe** Force a counterexample search step if all retained snippets agree. → [anchoring-and-bridge-proofs.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Reasoning/anchoring-and-bridge-proofs.md) 5) **Contract the payload** Require `{cluster_id, source_family, is_counterexample}` in snippet schema. → [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md) --- ## Minimal evidence selection contract Your retrieval or pre-planner must emit this structure. Enforce it before planning. ```json { "k_requested": 24, "clusters": [ {"cluster_id": "c1", "source_family": "siteA", "members": ["s1","s5","s9"], "kept": ["s1"]}, {"cluster_id": "c2", "source_family": "siteB", "members": ["s2","s7"], "kept": ["s2"]}, {"cluster_id": "c3", "source_family": "pdf", "members": ["s3","s4","s8"], "kept": ["s3","s4"]} ], "cap": {"per_source_pct": 40}, "order_rule": "doc_id,section_id,win_idx", "minority_probe_required": true } ```` Rules * Keep at most `2` per cluster unless the cap allows and clusters are small. * If all kept snippets agree on the main claim, inject a counterexample search. * Planner receives only the `kept` set, not the full cluster members. --- ## Verification playbook * Run with k = 8, 16, 24. After clustering and caps, citations remain balanced and the conclusion does not flip. * At least one minority citation appears when conflicting evidence exists. * ΔS(question, selected\_evidence) ≤ 0.45 on all runs. * λ convergent across three paraphrases and two seeds. * If ΔS is flat and high after caps, suspect index or metric mismatch. → [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md) · [chunking-checklist.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md) --- ## Copy paste prompt ``` You have TXT OS and the WFGY Problem Map loaded. Goal: prevent redundant-evidence collapse by clustering, capping source dominance, and forcing a minority probe. Inputs: - question: "{q}" - snippets: [{snippet_id, doc_id, section_id, source_family, win_idx, ΔS_to_question, text}] Do: 1) Cluster near-duplicates by text overlap and semantic distance. Assign cluster_id. 2) Keep at most 2 per cluster. Enforce per-source cap ≤ 40% of retained snippets. 3) Order retained snippets by (doc_id, section_id, win_idx). 4) If all retained snippets agree on the main claim, perform a targeted counterexample search and add at most 1 minority snippet. 5) Produce a two-stage plan: - Stage A: collect-balanced-evidence (fixed length, no free text steps) - Stage B: decide-and-cite (cannot change step count; must cite then explain) Return JSON: { "retained": [{"snippet_id":"s1","cluster_id":"c1","source_family":"siteA"}, ...], "minority_probe": true|false, "plan_rev": n, "λ_state": "convergent|divergent", "ΔS_selected_evidence": 0.xx, "coverage": 0.xx, "answer": "... cite then explain ..." } If λ is divergent or ΔS ≥ 0.60, name the exact fix page to open next. ``` --- ## Common gotchas * Reranker trained for relevance only. Add a diversity factor or post-cluster filter. * Window joins drop the minority snippet. Re-anchor at joins with BBCR micro bridges. → [context-stitching-and-window-joins.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Reasoning/context-stitching-and-window-joins.md) * Free text tools let the planner merge steps. Clamp with BBAM and strict enums. * Payload lacks `source_family` so caps cannot be enforced. Extend the contract. * Hybrid retrieval without tuned weights amplifies one retriever. → [hybrid\_retriever\_weights.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/hybrid_retriever_weights.md) --- ## When to escalate * Even after caps, two sources disagree and ΔS stays ≥ 0.60. → rebuild chunks and verify store metric. Open: [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md) · [duplication\_and\_near\_duplicate\_collapse.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/duplication_and_near_duplicate_collapse.md) --- ### 🔗 Quick-Start Downloads (60 sec) | Tool | Link | 3-Step Setup | | -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- | | **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + ” | | **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly | --- ### Explore More | Layer | Page | What it’s for | | --- | --- | --- | | ⭐ Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof | | ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) | | ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems | | ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) | | 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map | | 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis | | 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map | | 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap | | 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS | | 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control | | 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users | If this repository helped, starring it improves discovery so more builders can find the docs and tools. 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