Step 23: assistant

thinking

Calls add_relationship_edge

{
  "child_id": "0cfd9ad2-65a2-49ef-8fa2-5f035e22073f",
  "relation": "contradicts",
  "parent_id": "20fee30f-0192-4b8c-b479-83c3f230014d",
  "reasoning": "Kantamneni et al. (2025) find SAE sparse probes underperform tuned L2 logistic regression under covariate shift and label noise, the regimes where the claimed robustness to spurious cues should show.",
  "argument_id": "5ee763da-20ce-4dca-b9da-7bb5524709d1"
}

Calls add_decomposition_edge

{
  "relation": "contradicts",
  "parent_id": "20fee30f-0192-4b8c-b479-83c3f230014d",
  "reasoning": "The inductive-bias argument needs the target concept to be more cleanly isolated in a few latents than the spurious cues; if spurious cues are equally well represented, a sparse probe has no reason to prefer the true signal.",
  "seed_note": "Raised by the GDM interpretability team as one explanation of their negative result. Simple surface cues (punctuation, formatting, question marks) were in fact captured by single Gemma Scope latents, which is how the team found them, suggesting spurious cues are often at least as cleanly represented as abstract target concepts. No study measures this directly.",
  "child_text": "Spurious correlates of a label are as likely as the target concept to be represented by individual sparse autoencoder latents.",
  "importance": 0.35,
  "argument_id": "9903c59d-51d9-4797-b696-379a92f9128e",
  "seed_credence": 0.5
}