- iDRegEx now opt-in via --idregex flag (was running on every group,
causing 55s+ on Flask alone — src/flask/json took 55s in iDRegEx)
- GBNF tokenizer strips newlines from literals (multi-line symbols)
- Fix OverflowError: lang_size_score produces huge ints for large
disjunctions, format as string not float
- Flask: 2.7s (was 55s+), RAGSAK: 13s (was 74s)
Add _kore_trial() module-level worker for pickling. infer() accepts
n_workers param — >1 runs all (k x N) trials concurrently instead
of serial. Default 1 preserves existing behavior.
Thread n_workers through _run_kore() and infer_ensemble() to support
--kore flag with parallel kORE inference.
- Remove kORE from top-level imports, _ALGORITHMS, and infer_ensemble body
- Add include_kore=False parameter, runs kORE only when opted in
- Add --kore CLI flag threaded through all pipeline layers
- Remove koreinference from --prefer choices
- Keep kore.py module in repo for reference/future use
- Update tests: remove test_prefer_koreinference, update algorithm assertions
- Ensemble inference (infer_ensemble) runs both CRX and iDRegEx, picks best by MDL
- CRX: CRX algorithm for wide coverage (accepts all sequences, large vocabulary)
- iDRegEx: iDRegEx for minimal core grammar (tightest common pattern)
- MDL scoring: fixed model_cost to count alphabet symbol occurrences, fixed dispatch order in _count_words_fast
- Fixed _match_tokens: rewritten as _match_possible with proper backtracking
- Fixed _parse_parts disjunction: children use _parse_flat_symbol to avoid dot-splitting
- MCP server: infer_best_grammar and infer_grammar tools
- Added prefer parameter (crx/idregex) to skip ensemble
- 28 passing tests
- SHOWCASE.md with Geerlingguy Galaxy demonstration
- blog_post.md with full technical deep-dive
