{ "selected_step": 700, "validation_improved": true, "metric": "uncalibrated macro-source NLL; improvement must exceed 1e-6", "base": { "n": 786, "nll": 0.8177371901208593, "brier": 0.3928857406198933, "argmax_agreement": 0.6424936386768448, "soft_target_fraction": 0.10941475826972011, "by_source": { "KSE-RESEARCH-Group/UAReviews": { "n": 200, "nll": 0.3008418807003928, "brier": 0.12895151835801125, "argmax_agreement": 0.915, "soft_target_fraction": 0.0 }, "nvidia/Aegis-AI-Content-Safety-Dataset-2.0": { "n": 200, "nll": 0.5550802189421661, "brier": 0.3796225903166879, "argmax_agreement": 0.695, "soft_target_fraction": 0.0 }, "nvidia/HelpSteer3": { "n": 200, "nll": 1.377823997184949, "brier": 0.5669972128285794, "argmax_agreement": 0.43, "soft_target_fraction": 0.255 }, "openai/coval": { "n": 36, "nll": 1.3788071523285632, "brier": 0.17675586768508195, "argmax_agreement": 0.5, "soft_target_fraction": 0.9722222222222222 }, "rabuahmad/climatecheck": { "n": 150, "nll": 0.9757010305711111, "brier": 0.5822047772661176, "argmax_agreement": 0.5266666666666666, "soft_target_fraction": 0.0 } }, "by_kind": { "choice": { "n": 468, "nll": 0.7127335941312066, "brier": 0.3476115614992396, "argmax_agreement": 0.7115384615384616, "soft_target_fraction": 0.0811965811965812 }, "noul": { "n": 200, "nll": 0.5550802189421661, "brier": 0.3796225903166879, "argmax_agreement": 0.695, "soft_target_fraction": 0.0 }, "score": { "n": 118, "nll": 1.6793734372301579, "brier": 0.594927654934361, "argmax_agreement": 0.2796610169491525, "soft_target_fraction": 0.4067796610169492 } }, "macro_source_nll": 0.9176508559454364, "note": "Accuracy is argmax agreement with annotation targets; ties use first-index argmax. Soft targets represent finite human votes or subjective preferences, not objective truth. Brier is sum of squared candidate-probability errors; its scale varies with candidate count." }, "candidate": { "n": 786, "nll": 0.5571027568779557, "brier": 0.2618064378625637, "argmax_agreement": 0.7544529262086515, "soft_target_fraction": 0.10941475826972011, "by_source": { "KSE-RESEARCH-Group/UAReviews": { "n": 200, "nll": 0.16488824965745785, "brier": 0.08122107897078124, "argmax_agreement": 0.955, "soft_target_fraction": 0.0 }, "nvidia/Aegis-AI-Content-Safety-Dataset-2.0": { "n": 200, "nll": 0.32759895917618914, "brier": 0.2073110988938831, "argmax_agreement": 0.855, "soft_target_fraction": 0.0 }, "nvidia/HelpSteer3": { "n": 200, "nll": 1.0104743724896212, "brier": 0.42899340445147355, "argmax_agreement": 0.52, "soft_target_fraction": 0.255 }, "openai/coval": { "n": 36, "nll": 1.1635381012599184, "brier": 0.07591942035684947, "argmax_agreement": 0.6666666666666666, "soft_target_fraction": 0.9722222222222222 }, "rabuahmad/climatecheck": { "n": 150, "nll": 0.6360205266404155, "brier": 0.39694429709267093, "argmax_agreement": 0.6866666666666666, "soft_target_fraction": 0.0 } }, "by_kind": { "choice": { "n": 468, "nll": 0.450252531502061, "brier": 0.2214117972369437, "argmax_agreement": 0.8141025641025641, "soft_target_fraction": 0.0811965811965812 }, "noul": { "n": 200, "nll": 0.32759895917618914, "brier": 0.2073110988938831, "argmax_agreement": 0.855, "soft_target_fraction": 0.0 }, "score": { "n": 118, "nll": 1.3698711044734813, "brier": 0.5143806718161742, "argmax_agreement": 0.3474576271186441, "soft_target_fraction": 0.4067796610169492 } }, "macro_source_nll": 0.6605040418447203, "note": "Accuracy is argmax agreement with annotation targets; ties use first-index argmax. Soft targets represent finite human votes or subjective preferences, not objective truth. Brier is sum of squared candidate-probability errors; its scale varies with candidate count." }, "fallback": null }