# Эксперимент: Conditional spacetime-cluster sampler for rare neural trajectories (#937)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built an exact discrete-time conditional spacetime-cluster sampler for a binary Markov trajectory using forward-backward dynamic programming, plus forward rejection and single-site Gibbs baselines. Across Z≈0.091, 0.0099, and 0.0010, observed rejection acceptance was 0.09185, 0.01085, and 0.00100 versus predictions Z; cluster validity was 100% at every rarity. Cluster samples had ESS=200/200, versus local-Gibbs ESS≈6.8–9.5, and finite-sample conditioned-distribution TV error was 0.013–0.071. The mechanism manifested, but the implementation is an exact toy sampler rather than the proposed multi-node neural continuous-time system.",
  "metrics": {
    "baseline": "Forward rejection: predicted proposals per valid sample 11.00, 101.09, and 1001.97 for Z=0.09088, 0.009892, and 0.000998; bounded proposal acceptance observed 0.09185, 0.01085, and 0.00100. Local single-site Gibbs ESS over 200 updates: 6.78, 9.48, and 9.48.",
    "idea": "Exact full-interior cluster heat-bath: 100% terminal-valid samples at all three thresholds; ESS 200/200 independent draws. Conditioned path TV errors from 2,000 draws: 0.0708, 0.0256, and 0.0132; cluster interior means 1.535, 1.160, and 1.055 versus exact 1.485, 1.041, and 0.995."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py",
  "files": [
    "conditional_cluster_sampler.py",
    "run_experiment.py",
    "results.json"
  ],
  "limitations": "Only a one-node binary discrete-time Markov model was tested; no 20-node asynchronous neural RNN, learned rates, continuous-time event trajectories, multi-node cluster growth, training/data-augmentation task, wall-clock ESS-per-hour comparison, or GPU scaling was evaluated. Rejection was measured with a bounded proposal budget rather than generating 10,000 valid rare trajectories."
}