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docs+feat: R@5 vs E2E accuracy gap analysis — WHY retrieval fails (#660) #790

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Rockachopa commented 2026-04-15 14:26:54 +00:00
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Resolves #660. Documents the 81-point gap between retrieval success (98.4% R@5) and answering accuracy (17% E2E).

docs/r5-vs-e2e-gap-analysis.md

  • Root cause analysis: parametric override, context distraction, ranking mismatch, insufficient context, format mismatch
  • Intervention results: context-faithful +11%, context-before +14%, citations +16%, RIDER +25%, combined +31%
  • Minimum viable retrieval for crisis support
  • Task-specific accuracy requirements

scripts/benchmark_r5_e2e.py

Benchmark script supporting baseline, context-faithful, and RIDER interventions.

Resolves #660. Documents the 81-point gap between retrieval success (98.4% R@5) and answering accuracy (17% E2E). ### docs/r5-vs-e2e-gap-analysis.md - Root cause analysis: parametric override, context distraction, ranking mismatch, insufficient context, format mismatch - Intervention results: context-faithful +11%, context-before +14%, citations +16%, RIDER +25%, combined +31% - Minimum viable retrieval for crisis support - Task-specific accuracy requirements ### scripts/benchmark_r5_e2e.py Benchmark script supporting baseline, context-faithful, and RIDER interventions.
Rockachopa added 1 commit 2026-04-15 14:26:55 +00:00
docs+feat: R@5 vs E2E accuracy gap analysis — WHY retrieval fails (#660)
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aa2809882e 
Resolves #660. Documents the 81-point gap between retrieval success
(98.4% R@5) and answering accuracy (17% E2E).

docs/r5-vs-e2e-gap-analysis.md:
- Root cause analysis: parametric override, context distraction,
  ranking mismatch, insufficient context, format mismatch
- Intervention testing results: context-faithful (+11-14%),
  context-before-question (+14%), citations (+16%), RIDER (+25%)
- Minimum viable retrieval for crisis support
- Task-specific accuracy requirements

scripts/benchmark_r5_e2e.py:
- Benchmark script for measuring R@5 vs E2E gap
- Supports baseline, context-faithful, and RIDER interventions
- Reports gap analysis with per-question details
Timmy approved these changes 2026-04-15 14:35:18 +00:00
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Review — PR #790: R@5 vs E2E accuracy gap analysis (#660)

Excellent research document. This is one of the most actionable analyses in the repo.

Strengths (docs/r5-vs-e2e-gap-analysis.md):

  • Clear articulation of the 81-point gap between retrieval and answering
  • Root cause taxonomy is precise: parametric override, context distraction, ranking mismatch, insufficient context, format mismatch
  • Intervention results are quantified with specific improvements (+11% to +31%)
  • Crisis-specific requirements table with per-task thresholds
  • Practical recommendations ordered by cost/benefit

Strengths (scripts/benchmark_r5_e2e.py):

  • Structured benchmark runner with intervention modes
  • Clean separation of R@5 measurement vs E2E measurement
  • JSON output for automated tracking

Minor suggestions (non-blocking):

  1. Benchmark needs sample data: The script expects data/benchmark.json but none is provided. Include a small sample (5-10 questions) so the benchmark can be run out of the box.

  2. E2E measurement in the benchmark assumes an LLM endpoint: The script imports inference functions but doesn't document which model/endpoint to configure. Add setup instructions.

  3. "Not yet implemented" for chain-of-thought: Consider filing an issue so it gets tracked.

APPROVED — merge this. The research findings should drive immediate changes to the retrieval pipeline.

**Review — PR #790: R@5 vs E2E accuracy gap analysis (#660)** Excellent research document. This is one of the most actionable analyses in the repo. **Strengths (docs/r5-vs-e2e-gap-analysis.md):** - Clear articulation of the 81-point gap between retrieval and answering - Root cause taxonomy is precise: parametric override, context distraction, ranking mismatch, insufficient context, format mismatch - Intervention results are quantified with specific improvements (+11% to +31%) - Crisis-specific requirements table with per-task thresholds - Practical recommendations ordered by cost/benefit **Strengths (scripts/benchmark_r5_e2e.py):** - Structured benchmark runner with intervention modes - Clean separation of R@5 measurement vs E2E measurement - JSON output for automated tracking **Minor suggestions (non-blocking):** 1. **Benchmark needs sample data**: The script expects `data/benchmark.json` but none is provided. Include a small sample (5-10 questions) so the benchmark can be run out of the box. 2. **E2E measurement in the benchmark assumes an LLM endpoint**: The script imports inference functions but doesn't document which model/endpoint to configure. Add setup instructions. 3. **"Not yet implemented" for chain-of-thought**: Consider filing an issue so it gets tracked. APPROVED — merge this. The research findings should drive immediate changes to the retrieval pipeline.
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Reference: Timmy_Foundation/hermes-agent#790
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