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Author SHA1 Message Date
tobjend
36f9e9173d docs: update experiment log, results, and handover for Round 19-20
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2026-07-13 01:18:32 +02:00
tobjend
9ca56e2c69 MCP server: add decomposition + quality filtering params
- analyze_directory tool: add decompose, max_seq_length, cluster_method,
  crx_method params for Crucio-inspired improvements
- min_structure default bumped to 0.5 for MCP (only high-structure
  grammars returned to agents)
- _build_yaml_output: filter entries below min_structure threshold
- README: update source code analysis section, fix kORE/iDRegEx mentions
- 269 tests pass

Co-authored-by: OpenCode <opencode@corentic.eu>
2026-07-12 18:56:35 +02:00
tobjend
1d94c09344 experiment: CRX vs refined CRX across 3 codebases (Round 17)
Refined wins 7/9 when producing useful grammars (model_cost >= 2).
Trivial output (single symbol) in 5/13 cases on large groups.
CRX wins only once (FastAPI tests, 3618 methods).

Key finding: refined CRX is better ~78% of the time but needs
triviality check (model_cost >= 2) to avoid single-symbol grammars.
2026-07-12 17:08:15 +02:00
tobjend
b92b7653e2 feat: iDRegEx refinement for CRX flat bags (Round 16)
Heuristic: only run iDRegEx when n_methods ≤ 10 AND CRX grammar has
>50% top-level optional parts (flat chain signal). If iDRegEx grammar
is >10x tighter by lang_size, use it. Otherwise keep CRX.

RAGSAK result: agents/capability (5 methods) refined from
slot?.(defaultCapabilityId+summarize)?... (lang_size=1432) to
(defaultCapabilityId|summarize) (lang_size=3) — 477x tighter.

Speed cost: ~0.7s per candidate, negligible on 74s pipeline.
CLI: --idregex-refine flag (default off).

Also adds _count_optionals() and _should_try_idregex() helpers
with 8 pytest tests. 234 tests pass.
2026-07-12 16:54:40 +02:00
tobjend
e9f672cba4 experiment: kORE/iDRegEx vs CRX on flat bags (Round 15)
Key finding: iDRegEx/kORE return None on genuinely diverse groups (flat bags).
CRX is the only algorithm that produces anything for these groups.
For structured groups, CRX already captures ordering well.

Conclusion: kORE/iDRegEx are not better fallbacks for flat bags.
The pipeline's existing filtering (min_structure, split_mixed) is the right approach.
2026-07-12 16:20:01 +02:00
tobjend
93d53f164a fix: byte/char offset mismatch in tree-sitter text extraction
Root cause: parser.parse(code.encode()) returns byte offsets, but
code[node.start_byte:node.end_byte] indexed into a Python string
(character offsets). Non-ASCII chars caused cumulative drift → truncated symbols.

Fix: store code_bytes = code.encode(), index into that, decode only final text.

Impact:
- Zod: 653 truncated symbols → 0
- RAGSAK: 653 truncated symbols → 0
- RAGSAK grammars: 8 → 27 (3.4×)
- FastAPI grammars: 33 → 111 (3.4×)
- Malformed: 6 → 2 (RAGSAK), 0 (FastAPI/Flask)

Also removed sanitize_symbol() (dead code after fix) and call_only parameter.
2026-07-12 15:57:27 +02:00
tobjend
fe3261b9de docs: add Round 13 to experiment log (recursive split results) 2026-07-12 13:54:03 +02:00
tobjend
483526cb6a docs: Round 12 results — grammar structure scoring 2026-07-12 03:03:16 +02:00
tobjend
92af932e9d docs: Round 11 results — pipeline speed + GBNF conversion
- iDRegEx opt-in: Flask 55s→2.8s, RAGSAK 74s→13s, FastAPI 30s→30s
- GBNF validation: 130/130 OK, 0 FAIL (17 malformed skipped)
- Grammar quality: 43% structured, rest flat/trivial
- Logged to experiments/EXPERIMENT_LOG.md
2026-07-12 02:57:40 +02:00
tobjend
739000e8c6 feat: CRX refined — cluster-then-infer for tighter grammars
Standard CRX over-approximates when Hasse diagram is non-linear (24% of
RAGSAK packages). Cluster-then-infer groups sequences by (first, last,
length), infers per-cluster, picks largest cluster's grammar.

Results on RAGSAK:
  Avg max disjunction: 2.8 → 1.7 (39% tighter)
  Packages improved: 6/10

Tradeoff: cluster granularity (too coarse = over-approximation,
too fine = no generalization). Current: (first, last, length_bucket).

Exports crx_refined() and crx_with_confidence() from bex package.
20 new tests. All 199 tests pass.
2026-07-12 01:50:40 +02:00
tobjend
8028570ceb feat: frequency threshold sweep — 0.01-0.20 across 4 codebases
Sweet spot: 0.01-0.05. At 0.01 RAGSAK gets 61 SOREs (26.1% cov) with
real conventions like warn.status.body.ErrorResponse. At 0.05 coverage
jumps to 45.4% but that pattern disappears. Flask dies at 0.15+.

Current default min_coverage=0.2 is too aggressive for most codebases.
2026-07-12 01:26:45 +02:00
tobjend
dd183f6241 feat: 4-codebase evaluation — conventions vs completions tradeoff
Added kotlinx.coroutines (1040 .kt files) and FastAPI (1129 .py files).

Key findings across 4 codebases:
- Coarsening trades per-package coverage for cross-package reach
- FastAPI: coverage IMPROVES (12.4% -> 20.7%), cross-pkg +15
- Coroutines: cross-pkg +15, real exception-handling patterns
- RAGSAK: cross-pkg +18, null-check conventions across 8 packages
- Flask: slight cross-pkg loss, rendering conventions still visible

The conventions vs completions tradeoff is real and measurable.
2026-07-12 01:14:09 +02:00
tobjend
8540947eec docs: Round 6b results — Kotlin capture fix + minimal coarsening 2026-07-12 01:05:19 +02:00
tobjend
592974f039 feat: structural coarsening experiment — keeps function names raw, coarsens keywords
- coarsen_token() maps tree-sitter captures to categories (RETURN, IF, LOOP, etc.)
- Function calls kept as raw text (they ARE the behavioral content)
- Only ~5% structural tokens coarsened
- Results: Flask coverage 17.5% -> 28.8% (+11.3%), RAGSAK unchanged (95% calls)
- Cross-package shapes emerge: ('IF', 'KW', 'RETURN') in 4 Flask packages
2026-07-12 00:56:09 +02:00
tobjend
8e1c7f3767 docs: experiment log + cross-package analysis
- Full experiment history: context strategies, Reduce, scoring, GBNF, cross-package
- Documented failures: per-package sparsity, Reduce at wrong level, exact match rarity
- Designed next experiment: structural coarsening via tree-sitter categories
- Updated RESULTS.md with Round 5 findings and summary table
2026-07-12 00:45:21 +02:00