Step 13: assistant

Calls add_decomposition_edge

{
  "relation": "supports",
  "parent_id": "d3311aac-502f-4e69-ab97-53ee2b6de622",
  "reasoning": "The for-argument needs filtering to raise SAE scores; this is the within-pipeline effect, distinct from the cross-paper comparison with supervised baselines.",
  "seed_note": "Arad et al. (EMNLP 2025) show generation-success gains over random filtering on four models and AxBench harmonic-mean gains on Gemma-2-9B-it (0.293 to 0.546 at L20, 0.387 to 0.470 at L31, Claude 3.7 judge), but the gains are not monotonic in threshold and are measured on retained features only. An outside evaluation on Gemma-2-2B L20 under the AxBench protocol (gpt-4o-mini judge) reported no comparable gain.",
  "child_text": "Selecting SAE features by high output score improves their steering performance relative to unfiltered SAE features.",
  "importance": 0.35,
  "argument_id": "793235b0-7c74-4b32-a837-d43270f3a133",
  "seed_credence": 0.65
}