# Эксперимент: Regret-aware evidential cost compression (#840)

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built a small exhaustive layered-graph MVP with conservative evidential-cost compression, theorem verification, route-local scoring, and baseline comparisons. The monotonicity condition held numerically and the regret bound had zero observed violations across 10 trials, but the required compression-level scaling did not manifest: K' values produced identical results, and regret-aware, Jaccard-like, and random baselines were indistinguishable in the tested runs. Therefore the mechanism was not demonstrated, and this is not a validated win.",
  "metrics": {
    "baseline": "At K=8 to K'=2: mean regret 0.142435, flip rate 1.0 for the Jaccard-like baseline; random had the same mean regret and flip rate.",
    "idea": "Theorem check: maximum bound violation 0.0, mean regret/bound ratio 0.1239. At K=8: mean regret 0.213400, flip rate 1.0, mean bound 1.688280, total inflation 4.054034; K'=1,2,3 unexpectedly gave identical values."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 fast_experiment.py",
  "files": [
    "fast_experiment.py",
    "math_experiment.py"
  ],
  "limitations": "No neural graph model was trained or integrated, no Dijkstra implementation or runtime or memory measurement was performed, interval upper endpoints were not used in the cost calculation, and the toy sweep exposed an implementation/design issue because compression targets did not affect the reported outcomes. The focal-element merge surrogate is therefore only a conservative cost approximation, not a faithful implementation of the proposed interval-box merge."
}