Hybrid Retriever Weights — Guardrails and Fix Pattern

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WFGY Problem Map 1.0 — 16 reproducible failure modes

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Use this page when hybrid retrieval underperforms a single retriever or when results look noisy after fusing BM25, dense vectors, HyDE, or filters. Failures usually come from score scale mismatch, duplicate dominance, or query-type priors not reflected in weights.

Open these first

Visual map and recovery: RAG Architecture & Recovery
Retrieval knobs: retrieval-playbook.md
Ordering control: rerankers.md
Query parsing split (HyDE, BM25): patterns/pattern_query_parsing_split.md
Embedding vs meaning: embedding-vs-semantic.md
Vector store fragmentation: vectorstore_fragmentation.md
Metric mismatch: metric_mismatch.md
Normalization and scaling: normalization_and_scaling.md
Tokenization and casing: tokenization_and_casing.md

Core acceptance

ΔS(question, retrieved) ≤ 0.45 on 3 paraphrases and 2 seeds.
Coverage ≥ 0.70 to the target section after fusion and rerank.
λ remains convergent when weights are perturbed within ±10 percent.
Jaccard overlap against the best single retriever’s top-k ≥ 0.60.
No single source type or domain exceeds 40 percent of the final top-k unless configured.

Symptoms → likely cause → open this

Hybrid is worse than dense alone
→ raw scores on different scales or rank fusion mis-tuned
→ normalization_and_scaling.md, rerankers.md
BM25 dominates multilingual queries
→ tokenizer or casing divergence for CJK or mixed scripts
→ tokenization_and_casing.md
HyDE helps recall yet increases wrong-meaning hits
→ HyDE prompts off-domain, no rerank clamp
→ patterns/pattern_query_parsing_split.md, rerankers.md
Same snippet appears many times and crowds others
→ duplicate and near-duplicate collapse missing
→ (next page) duplication_and_near_duplicate_collapse.md
Fusion order unstable across shards
→ partial index rollout or fragmented store
→ vectorstore_fragmentation.md

Fix in 60 seconds

Normalize each retriever’s scores inside the candidate pool
Use one of: min-max to 0–1 per retriever, z-score per retriever, or pure rank-based RRF.
De-duplicate by snippet identity
Collapse near-duplicates using stable keys: {doc_id, section_id, hash_64}.
Fuse with a simple, auditable rule
Start with RRF: score = Σ 1 / (rank_i + k) with k ∈ [50, 100].
Then try weighted sum on normalized scores: S = wdense*sdense + wbm25*sbm25 + whyde*shyde.
Rerank with a cross-encoder
Rerank top 50–100 to top 10–20. Enforce cite-then-explain in the prompt.
Measure ΔS and λ
If λ flips when weights move by ±10 percent, clamp with BBAM and lock schema headers.

Minimal reference recipe


retrievers:

* name: dense
  k: 60
  norm: z
  weight: 0.55
* name: bm25
  k: 200
  norm: rank   # convert to ranks 1..k
  weight: 0.35
* name: hyde
  k: 60
  norm: z
  weight: 0.10

fusion:
method: RRF
rrf\_k: 60
dedupe: snippet\_id  # or doc\_id+section\_id+hash64

rerank:
model: cross-encoder-v2
take\_top: 15

accept:
deltaS\_max: 0.45
coverage\_min: 0.70
jitter\_weight: 0.10   # weights +/- 10 percent must keep λ convergent

Weighting heuristics that actually work

Short factual queries
Increase dense weight to 0.6–0.7. Keep BM25 at 0.3–0.4. HyDE optional.
Long verbose queries or code
Push BM25 to 0.5. Keep dense at 0.4. Use reranker to clean length bias.
Multilingual or mixed-script
Reduce BM25 weight if tokenizer mismatch is suspected. Verify casing and analyzer.
Highly structured data
Use BM25 boost on fielded terms. Keep dense for semantic recall.
Safety or policy queries
HyDE at most 0.15. Prefer deterministic BM25 plus strict reranker.

Observability probes you must log

Per retriever: raw score mean and stdev before normalization.
After fusion: source mix histogram and duplicate collapse count.
ΔS(question, retrieved) and λ states at steps: retrieve, fuse, rerank, answer.
A/B against best single retriever and report ΔS improvement or regression.

Common gotchas

Mixing cosine dense scores with BM25 raw scores without normalization.
HyDE prompts built with a different tokenizer than the dense model.
Reranker trained on passages while you fuse at document level.
Language-specific analyzers differ across shards and you fuse their outputs.
Latency cutoffs truncate candidate lists unevenly and bias the fusion.

Verification

Gold set of 100 queries with 3 paraphrases.
Require ΔS ≤ 0.45 and coverage ≥ 0.70 after fusion plus rerank.
Jaccard with best single retriever ≥ 0.60.
Weight jitter ±10 percent must keep λ convergent and citations stable.

🔗 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 + <your question>”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

Explore More

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Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
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