Modified fa#29
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Per-file advice model (reuse/modify/present_but_unused/write_fresh), best-state snapshot with restore on exit, enriched reviewer history, oscillation detection, FA 1.1.0-style single-call reviewer, strict rewriter prompt, End-based bypass removal, and env var cleanup. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Delete restore_best_node.py, remove 6 dead GraphState fields, drop generate_rewrite_plan and its Pydantic classes, remove _all_logs_end_with_end, simplify reviewer to pass no unused params. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Plumbs dataset logging through foambench_main.py → src/main.py → Config → LLMService. Callers can pass log_context to invoke() to emit per-call JSONL records for fine-tuning data. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
… sentence Recovers behavior from FA 2.0.0 best run (3e67ef7) that was stripped in cleanup commit 5fd021d: - nodes/restore_best_node.py + reviewer_node.py: snapshot case_dir to ${case_dir}_best when progress score (max log timestep) improves; router routes to restore_best on max_loop, restoring last-known-good state before exit. Prevents review-loop tail from corrupting result. - router_func.py: route_after_reviewer → restore_best on max_loop; new route_after_restore_best handles visualization/END branching. - services/plan.py + input_writer.py + review.py: restore log_context arg on every global_llm_service.invoke() call so dataset.jsonl emits. Without log_context, _log_dataset_record gate at utils.py:692 silently skips writes — JSONL was load-bearing for fine-tuning extraction. - services/plan.py: re-add "Make sure you generate all the necessary files" sentence to decompose_to_subtasks system prompt (only user prompt had it; matches 3e67ef7). - main.py: sync global_llm_service.dataset_log_path/case_id from CLI args after Config init (services/__init__.py instantiates with defaults at import time, before args are parsed). - utils.py: add best_case_snapshot_dir, best_progress_score to GraphState. Not re-added (intentional, per data on advice-step pareto-dominance): generate_rewrite_plan structured-JSON step, oscillation detection, File_Diff/Run_Outcome history enrichment. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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