{"family":"saliencew_whitened_tight","part":"adamw_second_moment","class":"Gate","note":"Measure salience in Adam-normalized coordinates and bake in the tight near-unity defaults so promotive gating follows the actual second-moment-scaled update instead of raw-gradient outliers.","edits":[{"old":"    grad_f = grad.float().view(-1)\n    avg_f = exp_avg.float().view(-1)\n    delta_f = (grad - exp_avg).float().view(-1)\n    grad_norm = torch.linalg.vector_norm(grad_f).clamp_min(1e-12)\n    avg_norm = torch.linalg.vector_norm(avg_f).clamp_min(1e-12)","new":"    denom_f = denom.float().view(-1)\n    grad_f = grad.float().view(-1) / denom_f\n    avg_f = exp_avg.float().view(-1) / denom_f\n    delta_f = (grad - exp_avg).float().view(-1) / denom_f\n    grad_norm = torch.linalg.vector_norm(grad_f).clamp_min(1e-12)\n    avg_norm = torch.linalg.vector_norm(avg_f).clamp_min(1e-12)"},{"old":"SALIENCEW_PROMOTIVE_GAIN = _env_float(\"AUTORESEARCH_SALIENCEW_PROMOTIVE_GAIN\", 0.18)\nSALIENCEW_AVERSIVE_GAIN = _env_float(\"AUTORESEARCH_SALIENCEW_AVERSIVE_GAIN\", 0.04)\nSALIENCEW_CONFLICT_GAIN = _env_float(\"AUTORESEARCH_SALIENCEW_CONFLICT_GAIN\", 0.04)\nSALIENCEW_GATE_MIN = _env_float(\"AUTORESEARCH_SALIENCEW_GATE_MIN\", 0.95)\nSALIENCEW_GATE_MAX = _env_float(\"AUTORESEARCH_SALIENCEW_GATE_MAX\", 1.15)","new":"SALIENCEW_PROMOTIVE_GAIN = _env_float(\"AUTORESEARCH_SALIENCEW_PROMOTIVE_GAIN\", 0.20)\nSALIENCEW_AVERSIVE_GAIN = _env_float(\"AUTORESEARCH_SALIENCEW_AVERSIVE_GAIN\", 0.00)\nSALIENCEW_CONFLICT_GAIN = _env_float(\"AUTORESEARCH_SALIENCEW_CONFLICT_GAIN\", 0.01)\nSALIENCEW_GATE_MIN = _env_float(\"AUTORESEARCH_SALIENCEW_GATE_MIN\", 0.99)\nSALIENCEW_GATE_MAX = _env_float(\"AUTORESEARCH_SALIENCEW_GATE_MAX\", 1.10)"}]}