Auto Research with Specialist Agents Develops Effective and Non-Trivial Training Recipes
We study auto research as a closed empirical loop driven by external measurement. Each submitted trial carries a hypothesis, an executable code edit, an evaluator-owned outcome, and feedback that shapes the next proposal. The output is not a generated paper or a single model checkpoint, but an auditable trajectory of proposals, code diffs, experiments, scores, and failure labels. We instantiate this loop with specialist agents that partition recipe surfaces and share measured lineage across trials. The central empirical finding is that lineage feedback lets agents turn evaluator outcomes, including crashes, budget overruns, size failures, and accuracy-gate misses, into later program-level...
| Authors | Jingjie Ning, Xiaochuan Li, Ji Zeng et al. |
|---|---|
| Primary category | cs.MA |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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