{ "idea": "Effective-Exploration Bias Correction", "seed": 1653, "mechanism_predictions": [ { "prediction": "For a suboptimal/non-uniquely-optimal arm, standardized post-bandit bias is negative.", "predicted": "Z < 0", "observed": {"c=0.5": -0.4776, "c=1": -0.3690, "c=2": -0.0705, "c=4": -0.2323}, "assessment": "confirmed in all four sweeps" }, { "prediction": "For UCB f_T=c sqrt(log(T+1)), Z approximately -1/f_T, so f_T Z approximately -1.", "predicted": "-1", "observed_fT_times_Z": {"T=400": -0.8864, "T=800": -0.9733, "T=1400": -1.1050, "T=2400": -1.2763}, "assessment": "direction and scale are plausible, but drift at larger T exceeds a strict asymptotic confirmation" }, { "prediction": "Adding 1/f_T to the standardized statistic should remove the negative first-order bias.", "predicted": "corrected Z approximately 0", "observed": {"raw_Z": -0.4155, "predicted_raw_Z": -0.3715, "corrected_Z": -0.0440}, "assessment": "confirmed for this suboptimal-arm toy setting" } ], "neural_comparison": { "baseline_heldout_mse": 0.5267864284, "idea_heldout_mse": 0.5806032153, "baseline_rare_signed_bias": 0.3651251499, "idea_rare_signed_bias": 0.4055727350, "assessment": "no improvement; both metrics worsened" }, "conclusion": "The sign and a direct debiasing effect were observed, but the exploration-coefficient sweep was not consistently quantitative enough to validate all strict mechanism predictions, and the contextual neural experiment showed no win. Therefore worked=false.", "limitations": [ "Small reduced Monte Carlo repetitions after the initial run exceeded the runtime budget.", "Only stationary synthetic Gaussian rewards were tested.", "The contextual policy used an oracle nonlinear mean for arm selection to isolate sampling bias rather than a fully learned online neural UCB policy.", "The soft optimality gate was not separately calibrated against known optimality labels.", "No repeated neural seeds or FLOP-normalized training study was performed." ] }