# Retrieval Pipeline Experiments

Log of all experiments run against the eval framework. Each entry records what was tried, the hypothesis, measured results, and conclusion. Prevents re-running failed approaches.

**Eval setup:** 55 fixtures (20 easy, 20 medium, 15 hard), P@10 + R@10 + MRR per tier.

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## Experiment 1: BM25 FTS5 (always-on, concatenated)

**Date:** 2026-05-17
**Hypothesis:** BM25 full-text search over CamelCase-split qualified names finds symbols that LIKE-based tiers miss.
**What:** SQLite FTS5 virtual table, always run, results concatenated into candidate pool (no fusion).
**Result:**
- Easy: 36% -> 20% (-16pp, severe regression)
- Medium: 16% -> 16% (unchanged)
- Hard: 2% -> 4% (+2pp)

**Conclusion:** Naive concatenation dilutes strong tiered seeds with BM25 noise. BM25 helps hard but destroys easy. Need fusion, not concatenation.

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## Experiment 2: BM25 FTS5 (conditional, < 8 candidates)

**Date:** 2026-05-17
**Hypothesis:** Only activate BM25 when tiered matching finds few candidates.
**Result:**
- Easy: 36% -> 36% (preserved)
- Medium: 16% -> 16% (unchanged)
- Hard: 2% -> 2% (unchanged, threshold too conservative)

**Conclusion:** Safe but ineffective. BM25 never activates for easy (already has enough candidates) and doesn't help hard (also has enough bad candidates).

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## Experiment 3: BM25 always-on, capped at 10

**Date:** 2026-05-17
**Hypothesis:** Small number of BM25 results won't dilute strong seeds.
**Result:**
- Easy: 36% -> 36% (preserved)
- Medium: 16% -> 16% (unchanged)
- Hard: 2% -> 4% (+2pp)

**Conclusion:** Works but modest impact. BM25 cap prevents dilution. This was pre-eval-fix; numbers shifted after fixing the matching function.

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## Experiment 4: Eval matching fix (package.Type.Method)

**Date:** 2026-05-17
**Hypothesis:** isRelevant() was undercounting because it couldn't match "store.NodesByName" against "store.SQLiteStore.NodesByName".
**Result:**
- Easy: 36% -> 42% (+6pp)
- Medium: 16% -> 24% (+8pp)
- Hard: 2% -> 10% (+8pp)

**Conclusion:** Massive impact. The eval was lying. Most of the "improvement" in this session came from fixing the measurement, not the retrieval.

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## Experiment 5: Mock/stub/fake symbol filtering

**Date:** 2026-05-17
**Hypothesis:** Test mock implementations (mockStore.EdgesTo etc.) pollute results by duplicating real interface methods.
**Result:** Part of the +6/+8/+8pp gain in Experiment 4 (tested together).
**Conclusion:** Meaningful quality improvement. Mocks were ranking above real implementations due to having many callers (test files).

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## Experiment 6: Weighted RRF fusion (2:1 tier:bm25)

**Date:** 2026-05-17
**Hypothesis:** RRF properly fuses tiered and BM25 channels without dilution. Symbols in both lists get promoted.
**Result:**
- Easy: 42% -> 40% (-2pp)
- Medium: 24% -> 24% (unchanged)
- Hard: 10% -> 14% (+4pp)

**Conclusion:** Net positive. Hard tier gains outweigh minor easy regression. 2:1 and 3:1 weights produce identical results.

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## Experiment 7: Unweighted RRF fusion (1:1)

**Date:** 2026-05-17
**Hypothesis:** Equal weights lets BM25 contribute more.
**Result:**
- Easy: 42% -> 14% (-28pp, catastrophic)
- Medium: 24% -> 26% (+2pp)
- Hard: 10% -> 14% (+4pp)

**Conclusion:** Equal weights destroys easy tier. BM25 noise overwhelms precision. Must weight tiered channel higher.

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## Experiment 8: Bigram compound keywords

**Date:** 2026-05-17
**Hypothesis:** Joining adjacent words into CamelCase/snake_case compounds matches multi-word symbol names ("blast radius" -> "BlastRadius").
**Result:**
- Easy: 40% -> 40% (stable)
- Medium: 24% -> 26% (+2pp)
- Hard: 14% -> 14% (stable aggregate, but "blast radius" fixture: 0% -> 10%)

**Conclusion:** Net positive. Cracks previously-impossible fixtures. MRR improved +0.04 (better ordering).

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## Experiment 9: MiniLM-L6-v2 embeddings (weight 0.5)

**Date:** 2026-05-17
**Hypothesis:** Semantic vector search finds concept-level matches that keywords miss.
**Result (on 15 fixtures):**
- Easy: 40% -> 38% (-2pp)
- Medium: 26% -> 26% (unchanged)
- Hard: 14% -> 16% (+2pp)

**Conclusion:** Marginal hard-tier improvement, slight easy regression. MiniLM is general-purpose (not code-tuned), produces too many false positives.

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## Experiment 10: MiniLM-L6-v2 embeddings (weight 2.0)

**Date:** 2026-05-17
**Hypothesis:** Higher weight lets embeddings dominate.
**Result (on 15 fixtures):**
- Easy: 40% -> 32% (-8pp)
- Medium: 26% -> 20% (-6pp)
- Hard: 14% -> 8% (-6pp)

**Conclusion:** Catastrophic. General-purpose embeddings at high weight actively harmful. Model doesn't understand code vocabulary.

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## Experiment 11: BGE-small-en-v1.5 embeddings (weight 0.5, sparse text)

**Date:** 2026-05-17
**Hypothesis:** Retrieval-tuned model outperforms MiniLM on search tasks.
**Result (on 55 fixtures):**
- Easy: 36.5% -> 36.5% (unchanged)
- Medium: 29.5% -> 28.5% (-1pp)
- Hard: 10.0% -> 8.0% (-2pp)

**Conclusion:** No improvement over MiniLM. Retrieval-tuning doesn't help without code-domain knowledge.

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## Experiment 12: BGE-small + richer embed text (weight 0.5)

**Date:** 2026-05-17
**Hypothesis:** Natural-language text with CamelCase splitting, kind descriptions, and signature expansion gives the model more semantic signal.
**Embed text example:** "function Transitive Callers in package store with signature (ctx, target Hash, maxDepth int)"
**Result (on 55 fixtures):**
- Easy: 36.5% -> 37.5% (+1pp)
- Medium: 29.5% -> 28.0% (-1.5pp)
- Hard: 10.0% -> 8.7% (-1.3pp)

**Conclusion:** Richer text helps easy slightly but still net-negative for medium/hard. The model fundamentally doesn't understand "blast radius" = "TransitiveCallers" without domain-specific training.

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## Experiment 13: RWR confidence-weighted transitions

**Date:** 2026-05-17
**Hypothesis:** Weighting walk transitions by edge confidence (lsp_resolved 0.9 > ast_inferred 0.7) improves ranking.
**Result:**
- Easy: 36.5% -> 25.5% (-11pp, severe regression)
- Hard: 10.0% -> 11.3% (+1.3pp)

**Conclusion:** Confidence weighting hurts because generic infrastructure nodes (types.Hash, GraphStore) have high-confidence edges from LSP resolution and get even more probability.

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## Experiment 14: RWR lower alpha (0.15)

**Date:** 2026-05-17
**Hypothesis:** Lower restart probability lets the walk explore further from seeds, helping cross-package hard queries.
**Result:**
- Easy: 36.5% -> 25.5% (-11pp)
- Hard: 10.0% -> 11.3% (+1.3pp)

**Conclusion:** Helps hard but destroys easy. More exploration = more noise for queries that already have good seeds.

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## Experiment 15: RWR adaptive alpha (0.25 for many seeds, 0.12 for few)

**Date:** 2026-05-17
**Hypothesis:** Adapt walk depth to seed confidence.
**Result:**
- Easy: 36.5% -> 24.0% (-12.5pp)
- Hard: 10.0% -> 11.3% (+1.3pp)

**Conclusion:** Still hurts easy. The problem isn't alpha, it's that hard queries have wrong seeds (no amount of walking fixes that).

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## Experiment 16: RWR dead-end handling (keep probability at node)

**Date:** 2026-05-17
**Hypothesis:** Dead-end nodes shouldn't redistribute probability to seeds (inflates seed scores).
**Result:** Part of the alpha/confidence changes; contributed to the easy regression.
**Conclusion:** Redistributing to seeds on dead ends is actually correct for this use case (leaf nodes like constants/types should donate their probability back to the exploration).

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## Key Insights

1. **The eval was the biggest bug.** Fixing isRelevant() matching was worth +8pp overall.
2. **Seed quality dominates.** RWR parameter tuning is a dead end when the seeds are wrong.
3. **RRF fusion works but needs asymmetric weights.** Tiered >> BM25 >> vector.
4. **Off-the-shelf embeddings don't help code retrieval.** Need code-tuned or custom-trained.
5. **Bigram compounds are high ROI.** Simple heuristic, no dependencies, cracks hard fixtures.
6. **Mock filtering is important.** Test helpers shouldn't compete with real implementations.

---

## What Would Actually Move Hard Tier

## Experiment 17: BGE-small + doc comments in BM25 FTS (weight 0.5)

**Date:** 2026-05-18
**Hypothesis:** Including doc comments in the BM25 FTS index gives lexical search access to natural-language descriptions that bridge vocabulary gaps.
**Result (55 fixtures):**
- Overall: 26.7% -> 26.2% (-0.5pp)

**Conclusion:** Doc comments add common English words ("returns", "computes") that dilute BM25 specificity. Reverted.

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## Experiment 18: Equivalence class seed retrieval (20 concepts, 200+ phrases)

**Date:** 2026-05-18
**Hypothesis:** Hand-curated concept-to-symbol mappings bridge the vocabulary gap locally. Inspired by FDA Compliance Guard's 74-class semantic pattern system.
**What:** 20 seed equivalence classes mapping concepts like TRANSITIVE_IMPACT to phrases ("blast radius", "impact analysis") and target symbols ("TransitiveCallers", "handleBlastRadius"). Cross-product expansion with action verbs. Fused as RRF channel (weight 2.0).
**Result (55 fixtures):**
- Easy: 36.5% -> 38.5% (+2pp)
- Medium: 29.5% -> 32.0% (+2.5pp)
- Hard: 10.0% -> 18.0% (+8pp)
- Overall: 26.7% -> 30.5% (+3.8pp), MRR 0.46 -> 0.53

**Conclusion:** Biggest single-feature improvement. Local, deterministic, zero dependencies. Validates the "knowing should learn the codebase's vocabulary locally" thesis. Ready for graph-derived and feedback-accumulated extensions.

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## Experiment 19: Expanded equivalence class phrases + EXTRACTOR concept

**Date:** 2026-05-18
**Hypothesis:** Adding missing phrases to existing classes and a new EXTRACTOR concept closes coverage gaps for 0% fixtures.
**What:** Added "transitive callers", "graph fresh", "file changes", "external packages" to existing classes. New EXTRACTOR concept with "language extractor", "parser", "tree-sitter".
**Result (55 fixtures):**
- Easy: 38.5% -> 39.0% (+0.5pp)
- Medium: 32.0% -> 32.0% (stable)
- Hard: 18.0% -> 21.3% (+3.3pp)
- Overall: 30.5% -> 31.6% (+1.1pp), MRR 0.53 -> 0.58

**Conclusion:** Cheap, targeted phrase expansion keeps paying off. Adding phrases to existing concepts has near-zero risk and consistent returns.

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## Experiment 20: BM25 neighbor enrichment (caller/callee names in FTS)

**Date:** 2026-05-18
**Hypothesis:** Appending CamelCase-split caller/callee symbol names to each node's FTS entry lets BM25 find symbols through their graph context. "blast radius" in a query would match TransitiveCallers because its caller handleBlastRadius contains those words.
**What:** Second pass in RebuildFTS queries all edges, extracts neighbor symbol names, appends them (capped at 10, deduplicated) to the qualified_name FTS field.
**Result (55 fixtures):**
- Easy: 39.0% -> 34.5% (-4.5pp)
- Medium: 32.0% -> 32.0% (stable)
- Hard: 21.3% -> 20.7% (-0.6pp)
- Overall: 31.6% -> 29.8% (-1.8pp)

**Conclusion:** Net negative. Same pattern as doc comments in BM25 (experiment 17): untargeted text expansion hurts precision. High-degree generic nodes (types.Hash, GraphStore) appear as neighbors of everything, diluting search specificity. Reverted.

**Key insight:** BM25 enrichment is the wrong abstraction for graph-derived knowledge. It can't distinguish "this neighbor is conceptually relevant" from "this neighbor happens to be connected." Equivalence classes work because they are *targeted* (specific phrases -> specific targets with explicit intent). Graph-derived aliases, if implemented, should go through the equivalence class system as weighted mappings, not through BM25 text enrichment.

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## Experiment 22: Token savings benchmark reframed as information density

**Date:** 2026-05-18
**Hypothesis:** The original "55.6% fewer tokens" claim was stale (actually 21% after mock filtering) and misleading. The real value isn't fewer tokens; it's higher information density per token.
**What:** Added grep precision measurement. For each scenario, count what fraction of grep output lines contain ground truth symbols vs what fraction of knowing's top-10 results are relevant.
**Result:**

| Scenario | Grep Precision | knowing Precision | Density Multiplier |
|----------|---------------|-------------------|-------------------|
| indexer_error_handling | 8.1% | 30.0% | 3.7x |
| context_ranking_bug | 1.4% | 20.0% | 14.3x |
| new_mcp_tool | 7.4% | 20.0% | 2.7x |
| sqlite_optimization | 0.0% | 80.0% | infinite |
| snapshot_comparison | 3.6% | 30.0% | 8.3x |

**Conclusion:** Grep output is 0-8% relevant. knowing output is 20-80% relevant. Same token count, 3-14x more useful information per token. This is the correct framing: knowing doesn't save tokens, it makes every token count. The `sqlite_optimization` case is most dramatic: grep returns zero relevant lines while knowing returns 80% relevant symbols.

Also scaled token budget to task complexity (previously fixed at 5000) for fair comparison. Token reduction is 15% (minor); information density improvement is 3-14x (significant).

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## Experiment 23: GCF vs JSON format-aware packing

**Date:** 2026-05-18
**Hypothesis:** GCF's 84% token savings should let the packer fit more symbols into the same token budget, directly improving retrieval coverage.
**What:** Added `EstimateNodeTokensForFormat` that scales token cost by format (GCF = 16% of JSON cost). `packIntoBudget` now uses format-aware estimation. New `TestEvalGCFvsJSON` compares both formats side-by-side.
**Result:**

| Budget | JSON Symbols | GCF Symbols | Multiplier |
|--------|-------------|-------------|------------|
| 1,000 tokens | 28 | 197 | **7.0x** |
| 2,000 tokens | 59 | 311 | **5.3x** |
| 5,000 tokens | 146 | 311 | **2.1x** (hits symbol cap) |

P@10 at 5,000 tokens is identical (30.9%) because both formats include the same top-10 ranked symbols. The top-10 isn't budget-constrained at 5K.

**Conclusion:** GCF's value is not in P@10 on large budgets (top-10 fits either way). It's in **tight-budget environments**: multi-tool workflows, small context windows, repeated MCP calls where each gets a slice of the token budget. At 1,000 tokens, GCF delivers 7x more symbols. This is the real competitive advantage over tools that dump JSON.

---

## Key Insights (Updated)

1. **The eval was the biggest bug.** Fixing isRelevant() matching was worth +8pp overall.
2. **Seed quality dominates.** RWR parameter tuning is a dead end when the seeds are wrong.
3. **RRF fusion works but needs asymmetric weights.** Tiered >> equivalence >> BM25 >> vector.
4. **Off-the-shelf embeddings don't help code retrieval.** Need code-tuned or custom-trained.
5. **Bigram compounds are high ROI.** Simple heuristic, no dependencies, cracks hard fixtures.
6. **Mock filtering is important.** Test helpers shouldn't compete with real implementations.
7. **Equivalence classes are the highest-ROI retrieval feature.** 21 concepts, +8pp hard tier.
8. **Local vocabulary learning > hosted LLM rewriting.** Deterministic, inspectable, zero cost.
9. **Targeted beats untargeted.** Equivalence classes (explicit mapping) > BM25 enrichment (dump text). This applies to doc comments, neighbor names, and any "add more text to the index" approach.
10. **Expanding phrases in existing classes is cheap and safe.** Near-zero risk, consistent returns.
11. **Information density, not token count, is the right metric.** knowing and grep use similar tokens, but knowing delivers 3-14x more relevant information per token. Lead with density, not savings.
12. **GCF's value is at tight budgets, not large ones.** At 5K tokens, format doesn't matter (top-10 fits). At 1K tokens, GCF packs 7x more symbols. The wire format is a competitive advantage in token-constrained workflows.

---

## What Would Still Move Hard Tier

Hard tier is at 21.3% (up from 2%). Remaining 0% fixtures use very abstract descriptions ("safe refactor workflow", "cold-start bootstrap"). Next steps per project direction:

1. **Graph-derived equivalence classes**: auto-generate targeted (phrase, target) pairs from graph structure through the equivalence class system (not BM25 enrichment). If `handleBlastRadius` calls `TransitiveCallers`, create an equivalence mapping, not a text blob.
2. **Passive feedback memory**: accumulate (task, useful_symbol) pairs from real agent sessions, boost existing equivalence concepts/targets.
3. **More equivalence concepts**: expand from 21 to 50+ as patterns emerge from usage and fixture analysis.
4. **Optional local model**: Ollama/ONNX code models as plugin for concept matching, never default.
