{
  "handle": "SurprisalUpdate",
  "mechanism": "A learning protocol where the agent updates its internal model (weights, embeddings, or context) based on failed predictions, weighted by the magnitude of surprise. Higher surprisal = larger update. 'Learn most from what confused you most.' This implements Surprisal-Weighted Fine-Tuning (SWFT): loss contribution is proportional to -log(P(observed|predicted)). Utilizes {{regime_sense}}, {{epistemic_roi}}.",
  "gloss": "Learning weighted by prediction failure magnitude",
  "failure_modes": [
    "Outlier Overfitting: Rare high-surprisal events dominating learning.",
    "Catastrophic Forgetting: Aggressive updates erasing previously stable knowledge.",
    "Compute Cost: High-surprisal events require expensive {{gradient}} updates."
  ],
  "invariants": [
    "Loss contribution is proportional to surprisal, -log P(observed|predicted).",
    "Stability Guard: no single update shifts the model more than `max_update_rate`, whatever the loss.",
    "Update magnitude follows from the loss through `learning_rate` and the optimiser, not from surprisal directly."
  ],
  "preconditions": [
    "Actual outcome observed and diverged from prediction",
    "Prediction was made with computable probability"
  ],
  "postconditions": [
    "Loss weighted by surprisal; model shift bounded by `max_update_rate`",
    "Update logged for audit trail"
  ],
  "parameters": [
    {
      "name": "learning_rate",
      "type": "Float",
      "range": "[0.0, 1.0]",
      "description": "How much to update the model based on surprisal"
    },
    {
      "name": "max_update_rate",
      "type": "Float",
      "range": "[0.0, 1.0]",
      "description": "Maximum allowed model shift per single update"
    }
  ],
  "_meta": {
    "tier": 1,
    "related": [
      "sema:BayesUpdate#mh:SHA-256:a762656f3df6a4b6322a6da1ae14f3093e675cba380bb5e488f397863f3e8b0a"
    ],
    "ring": 2,
    "supersedes": [
      "sema:SurprisalUpdate#mh:SHA-256:8eb2004c635b92c95a5dad62bbc4375a5a6728f493345334dd46f8ec2b4c3714",
      "sema:SurprisalUpdate#mh:SHA-256:6ef19d8b61ce228bbc61a34496b878f1e060e56790f48606fc4e81b3b8594fca",
      "sema:SurprisalUpdate#mh:SHA-256:41a9f896757eb4b0d7132604b60e0193eaba43329953359ef5e927f69112f8df",
      "sema:SurprisalUpdate#mh:SHA-256:6169da6dce9141595fc8b7ac8d8743e397cd44a2da04b3499b2ce910e174c2ef",
      "sema:SurprisalUpdate#mh:SHA-256:b2f104df0f114d6ef799bb343897bc4119585cd164821e63bfa6043cb717243f"
    ],
    "path": [
      "Mind",
      "Inference"
    ]
  },
  "sema_id": "sema:SurprisalUpdate#mh:SHA-256:26e53ade88809ac3ba9293b45db768fdbdb384a53cda1217c3a0dfc61d5f8408",
  "sema_ref": "SurprisalUpdate#26e5",
  "sema_stub": "26e5",
  "dependencies": {
    "references": {
      "epistemic_roi": "sema:EpistemicROI#mh:SHA-256:b0d863d10c0d67edc218444b72c75f05127b899af6ac0295899b6506cba9acc7",
      "gradient": "sema:Gradient#mh:SHA-256:ecfc2cd2d9eb95778ef04ede8a7e872c33822f174a9e3273fa7bba92b3a30e36",
      "regime_sense": "sema:RegimeSense#mh:SHA-256:50396d316674bf65b204938ee8cecc48cf294223cdb90c9789d6a72bb542cb07"
    }
  },
  "sema_layer": "Mind",
  "sema_category": "Inference",
  "used_by": {
    "references": {
      "BeliefTracking": "sema:BeliefTracking#mh:SHA-256:c78d68a1f4fe92e2f284e489f6987bc42183245181c6710b9d0c9e582b054e13",
      "CounterfactualAnchor": "sema:CounterfactualAnchor#mh:SHA-256:b8c3c5aca4a8adba07c19d0f6103387c6f7cf9b67ade40b412fa39e41fdb3adf",
      "TraceBelief": "sema:TraceBelief#mh:SHA-256:7b8a67006e9e0f510dd1937e9b420fb8cea880ca1fd0d3d1e86ef5bffffa021a"
    }
  }
}