fixtures/annotated_graph.json

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        "label": "Gradient Descent",
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      },
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        "id": "backpropagation",
        "kind": "concept",
        "label": "Backpropagation",
        "summary": "Reverse-mode autodiff that computes loss gradients through a network."
      },
      {
        "id": "chain_rule",
        "kind": "concept",
        "label": "Chain Rule",
        "summary": "Calculus rule for differentiating composed functions."
      },
      {
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        "kind": "concept",
        "label": "Neural Network",
        "summary": "Layered function approximator built from weighted connections."
      },
      {
        "id": "loss_function",
        "kind": "concept",
        "label": "Loss Function",
        "summary": "Scalar objective that quantifies prediction error."
      },
      {
        "id": "learning_rate",
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        "label": "Learning Rate",
        "summary": "Step-size hyperparameter scaling each gradient update."
      },
      {
        "id": "overfitting",
        "kind": "concept",
        "label": "Overfitting",
        "summary": "When a model memorizes noise instead of generalizable signal."
      },
      {
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        "kind": "concept",
        "label": "Regularization",
        "summary": "Techniques that penalize complexity to improve generalization."
      }
    ],
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      {
        "id": "e6",
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        "label": "consumes gradients from"
      },
      { "id": "e7", "source": "neural_network", "target": "overfitting", "type": "related_to", "label": "can suffer" },
      {
        "id": "e8",
        "source": "regularization",
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        "label": "counteracts"
      },
      {
        "id": "e9",
        "source": "regularization",
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        "label": "constrains"
      },
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        "id": "e10",
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      },
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        "id": "h1",
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      }
    ],
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        "review_state": "accepted"
      },
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        "origin": "user_asserted",
        "support_state": "unverified",
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      },
      {
        "id": "c3",
        "claim_type": "note",
        "target_id": "overfitting",
        "text": "Gemma inferred this is a relevant failure mode for the networks discussed here — a hypothesis awaiting your review.",
        "origin": "model_inferred",
        "support_state": "unverified",
        "review_state": "pending"
      },
      {
        "id": "n1",
        "claim_type": "note",
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        "text": "Dropout and weight decay are the most common regularization techniques here.",
        "origin": "user_asserted",
        "support_state": "unverified",
        "review_state": "pending"
      },
      {
        "id": "hyp1",
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        "text": "Hypothesis: an overly large learning rate amplifies overfitting on small datasets.",
        "origin": "user_asserted",
        "support_state": "unverified",
        "review_state": "pending"
      },
      {
        "id": "hyp2",
        "claim_type": "hypothesis",
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        "text": "Hypothesis edge: learning rate and overfitting may be causally linked.",
        "origin": "model_inferred",
        "support_state": "unverified",
        "review_state": "pending"
      },
      {
        "id": "q1",
        "claim_type": "question",
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        "text": "Open question: which loss best balances calibration and accuracy for this task?",
        "origin": "user_asserted",
        "support_state": "unverified",
        "review_state": "pending"
      },
      {
        "id": "q2",
        "claim_type": "question",
        "target_id": "chain_rule",
        "text": "Open question: how to explain the chain rule intuition without calculus prerequisites?",
        "origin": "user_asserted",
        "support_state": "unverified",
        "review_state": "pending"
      }
    ],
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  },
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    "selected_id": "",
    "callouts": [
      { "id": "co1", "target_id": "learning_rate", "text": "Open question attached: tuning vs. overfitting." }
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}