fix ALNS rewards and tabu diversification

PROGRESS.md

@@ -41,3 +41,21 @@ python src/experiments/run_main_comparison.py --config paper --seeds 30 --iterat
 ### Interpretation rule
 - Check whether Static > TA-Greedy > ALNS-Base > T-ALNS > T-ALNS-RRD in total cost, CES decreases, and OTDR improves.
 - If the paper trend is not reproduced, keep the generated tables and record the failed metrics honestly instead of tuning results by hand.
+
+## 2026-06-02 follow-up algorithm fidelity fix
+
+### Findings addressed
+- ALNS, T-ALNS, and T-ALNS-RRD did not distinguish `sigma_2` current-solution improvements from `sigma_3` simulated-annealing accepted non-improving moves.
+- T-ALNS diversification intensity used adaptive Tabu tenure instead of the paper's actual move-memory occupancy `|T_move| / |T_move|max`.
+
+### Changes made
+- Reward assignment now follows the paper's four levels:
+  - `reward_global_best` for new global best.
+  - `reward_improvement` for accepted moves that improve the previous current solution.
+  - `reward_accepted` for accepted non-improving moves.
+  - `reward_rejected` for rejected moves.
+- Added `MoveTabu.utilization` and changed Eq.27 diversification intensity to use actual move Tabu list occupancy.
+
+### Local validation
+- Per user instruction, no local Python tests, smoke tests, or full experiments were run.
+- Static checks only: source inspection and `git diff --check`.