Эксперимент: Incidence-Matrix Structured Action Head (#502)

{ "worked": true, "confidence": 8, "verdict": "Built an incidence-matrix structured action head with shared atomic scoring, plus a padded fixed-action MLP baseline. Numerical checks confirmed exact incidence additivity with maximum error 9.5e-7, the predicted logit scaling k^(1-alpha) for alpha=0, 0.5, and 1.0, and the predicted equal-score size bias. In the small unseen-schema experiment, the structured head reached 82.8% accuracy versus 6.8% for the padded MLP, with 97 versus 3,136 parameters and approximately 168 versus 2,048 stored feature elements per instance; this is promising evidence for schema generalization, though the baseline is deliberately limited and not a full PPO comparison.", "metrics": { "baseline": "Padded MLP: unseen-schema accuracy 0.068, cross-entropy 13.9801, 3,136 parameters, 2,048 padded feature elements per instance, 61.87 seconds.", "idea": "Incidence head: unseen-schema accuracy 0.828, cross-entropy 0.3567, 97 parameters, approximately 168 incidence elements per instance, 51.76 seconds; additivity maximum error 9.54e-7; observed normalization slopes 1.0000, 0.5000, and 0.0000 for alpha 0, 0.5, and 1." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "The experiment uses synthetic additive utilities rather than a real graph-RL environment, PPO, or a learned GNN with typed input/output neighbors. The padded baseline is a simple fixed-index MLP and does not represent a production-quality permutation-invariant padded policy; sparse GPU matrix multiplication and peak memory were not benchmarked directly." }