Эксперимент: Adversarial Decision-Equivalent Training (#767)

{ "worked": true, "confidence": 8, "verdict": "Built a reproducible two-route MVP with exact budgeted interdiction enumeration, hard shortest-path evaluation, and differentiable nominal/adversarial training surrogates. The math check observed the predicted regret onset, linear slope, and scaling law exactly. Adversarial training reduced worst-case regret from 0.099685 to 0 and flip rate from 0.325 to 0 versus nominal decision training at equal setup, but MSE performed best overall on this simple task, so this is evidence for the mechanism rather than a general performance win.", "metrics": { "baseline": "MSE: nominal regret 0.000000, worst-case regret 0.000000, flip rate 0.000, cost MSE 0.000513; nominal DFL: nominal regret 0.000000, worst-case regret 0.099685, max 0.438445, flip rate 0.325, cost MSE 0.041751", "idea": "A-DFL: nominal regret 0.000000, worst-case regret 0.000000, max 0.000000, flip rate 0.000, cost MSE 0.056332; mechanism slope 1.0000000000000002 and scaling error 0" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py", "files": [ "run_experiment.py", "results.json", "README.md" ], "limitations": "Only a two-edge/two-route synthetic system was tested, with a simple realizable predictor and B=1; no 20-node random graphs, road-network benchmark, larger budgets, beam search, GPU path, or broader seed sweep was evaluated. The training objective uses cross-entropy on oracle choices rather than a fully differentiable exact regret expression." }