Эксперимент: Collective-Detectability Information Fusion for Asynchronous Latent States (#782)

{ "worked": true, "confidence": 8, "verdict": "Built a NumPy MVP for Gaussian natural-parameter updates, graph diffusion, collective Gramian checks, and asynchronous fusion. The three mechanism predictions were confirmed: the Gramian minimum eigenvalue matched min(1,q), posterior covariance scaled as sigma^2, and ring consensus contracted under the predicted rho^t envelope. Information diffusion did not improve task MSE in the tiny trial; arithmetic averaging was slightly better, so the evidence supports the mechanism but not a practical accuracy win.", "metrics": { "baseline": "Arithmetic mean fusion MSE: 0.1974311", "idea": "Natural-parameter information diffusion MSE: 0.1980828; analytic mechanism checks matched within numerical precision" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json", "REPORT.txt" ], "limitations": "Only a small linear NumPy toy was tested. Learned encoders, nonlinear latent dynamics, recurrent rollout stability, broader graph/connectivity sweeps, communication cost, and realistic stale-message scheduling were not evaluated." }