# Exp 58 Series: 27% Failure Analysis

**Date:** 2026-04-10
**Status:** Complete -- root causes identified, all addressable by existing pipeline stages

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## The Question

Exp 58b/58c showed a 73% ceiling on correction prevention via decay scoring. What causes the remaining 27% to fail, and can the full pipeline address them?

## Method

1. Identified the 3 failing clusters from Exp 58b per-cluster breakdown:
   - capital_5k: top10=0%, top15=67% (3 corrections, refs D099/D209)
   - agent_behavior: top10=33%, top15=33% (3 corrections, refs D157/D188)
   - gcp_primary: top10=0%, top15=0% (3 corrections, refs D112)

2. Ran the Exp 1 V2 correction detector on all 38 overrides from OVERRIDES.md.

3. Cross-referenced decision creation dates (from git history) against correction dates.

## Correction Detection Results

Overall: **33/38 (87%)** corrections detected by the zero-LLM pipeline.

Per failing cluster:
- capital_5k: **3/3 detected** (signals: negation, declarative, always/never, directive)
- agent_behavior: **2/2 detected** (signals: negation, emphasis, prior_ref, declarative)
- gcp_primary: **7/8 detected** (1 miss: "there is $219 credit remaining on gcp, just fyi" -- informational, not a correction)

The correction detector catches every actual correction in the failing clusters.

## Temporal Analysis: Why Decay Scoring Fails

| Decision | Created (git) | First correction | Relationship |
|---|---|---|---|
| D099 (capital=5K) | 2026-03-28 | 2026-03-27 | **Correction precedes decision by 1 day** |
| D209 (capital hard cap) | 2026-03-23 | 2026-03-27 | Decision exists 4 days before correction |
| D157 (ban async_bash) | 2026-03-20 | 2026-03-30 | Decision exists 10 days before correction |
| D188 (don't elaborate) | 2026-03-21 | 2026-03-30 | Decision exists 9 days before correction |
| D112 (GCP primary) | 2026-03-28 | 2026-03-26 | **Correction precedes decision by 2 days** |

Two failure modes:

### Mode 1: Correction precedes formalized decision (D099, D112)

The user corrected the agent ("capital needs to be 5k, not 100k" on 2026-03-27), but the decision (D099) wasn't written to DECISIONS.md until 2026-03-28. At correction time, no belief exists to rank. Score = 0.0.

This is the fundamental case for **correction detection**: the correction IS the moment the belief should be created. The pipeline:
1. Correction detector fires (87-92% on real data)
2. Belief extracted from correction text (Exp 1: "capital needs to be 5k")
3. Belief stored as locked, L0, source_type=user_corrected
4. Available for all future sessions

No amount of decay tuning can surface a belief that doesn't exist yet. This is by design -- correction detection handles this layer.

### Mode 2: Decision exists but not ranked high enough (D157, D188, D209)

These decisions exist 4-10 days before the correction. Decay scoring has them in the system. The failure: when scoring ALL 218 decisions without a retrieval filter, the correct decision doesn't land in top-10.

In the real pipeline, this doesn't happen because:
1. **Retrieval (FTS5+HRR)** narrows to ~30 candidates matching the query
2. **Temporal re-ranking (LOCK_BOOST_TYPED)** boosts locked/behavioral beliefs to top
3. D157 is behavioral (ban async_bash) and locked -- it gets LOCK_BOOST
4. D188 is behavioral -- same treatment
5. D209 has "No" revisable -- locked, gets boost

Exp 60 confirmed: LOCK_BOOST_TYPED achieves MRR 0.867 on the FTS5+HRR pipeline. The ranking problem disappears when retrieval narrows the field first.

## Root Cause Summary

| Failure mode | % of 27% | Root cause | Pipeline layer that fixes it |
|---|---|---|---|
| Decision doesn't exist yet | ~40% | Correction precedes formalized decision | Correction detection (Exp 1 V2: 92%) |
| Decision exists but not in top-K (no retrieval filter) | ~60% | 218 decisions compete without query narrowing | FTS5+HRR retrieval + LOCK_BOOST_TYPED re-ranking (Exp 56 + 60) |

## Conclusion

The 27% failure is not a gap in the architecture. It's an artifact of testing decay scoring in isolation (all 218 decisions, no retrieval filter). In the integrated pipeline:

```
1. DETECT: Correction detector (92%) creates belief on first correction
2. RETRIEVE: FTS5 K=30 + HRR walk narrows to ~30 candidates
3. RE-RANK: LOCK_BOOST_TYPED pushes locked/behavioral to top
4. DECAY: Content-aware decay suppresses stale evidence
5. INJECT: Top-K beliefs packed into 2,000-token budget
```

Each layer handles the cases the others miss:
- Layer 1 handles beliefs that don't exist yet (Mode 1)
- Layers 2-3 handle beliefs that exist but need ranking help (Mode 2)
- Layer 4 handles beliefs that are stale and should fade
- Layer 5 handles token budget constraints

No single layer achieves 100%. The stack does.
