- frequency_filter(min_coverage=0.2) strips rare symbols before clustering/inference
- DEFAULT_COVERAGE=0.8 passed to infer_ensemble for sequence-level core/outlier detection
- Both filters active: symbol-level (0.2) then BEX sequence-level (0.8)
- Fix seq_of_file tracking broken by filter creating new list objects
- _extract_call_tokens filters to call-like captures only
- cluster_methods groups sequences by shared 3-gram call patterns
- analyze_clusters runs per-cluster CRX/iDRegEx/kORE inference
- Lists 17 convention clusters for RAGSAK test code
- iDRegEx and kOREInference now produce ordered grammars per cluster
- (other) cluster captures diverse conventions as CRX vocabulary
- min-cluster-size (default 3) and ngram-size (default 3) CLI flags
- _find_method_bodies uses tree-sitter's universal body field (9/10 grammars)
- Kotlin fallback: scan children for body-like types
- preprocess_by_method groups highlight captures by enclosing method body
- Returns per-method sequences for k-ORE ordering analysis
- analyze.py infer() now uses preprocess_by_method
- Document findings in ANALYSIS.md
- 97 tests pass
- 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
