V0.3.1 unify gain computation#9
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Sequential and parallel gain paths now use the same _compute_single_action_gain function via SequentialGainCache, fixing a lifted-vs-grounded divergence in success potential. Export/PDDL/metrics code moved to model_export.py, reducing information_gain.py from 2200 to 1823 lines. - Fix _calculate_potential_gain_success to use grounded-space - Add SequentialGainCache for in-process gain delegation - Add _create_sequential_context (references, no deep copies) - Deterministic tie-breaking in both sort paths - Extract to_pddl_string, export_model_snapshot, get_learned_model, get_action_model_metrics to algorithms/model_export.py - Bump version to 0.3.1
- Delete pddl_types.py, inline ParameterBinding into expression_converter.py (one-consumer rule: only expression_converter used it) - Remove unused ExpressionConverter.to_cnf_clauses() and its tests (no production callers) - Update imports in up_adapter.py and test files
Combine model_validator.py + model_metrics.py + model_reconstructor.py into a single model_evaluation.py (-420 LOC net). These are tightly coupled BRIDGE modules always used together. Breaking old import paths is fine at v0.x.
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