ConflictGuide: AutoResearch Improves When Competing Behaviors Are Made Visible

Excerpt

arXiv:2609.39933v1 Announce Type: cross Abstract: When designing machine learning models, desirable properties are often in tension: improving one behavior can impair another, so task progress can depend on alleviating the conflict. LLM-based AutoResearch systems, which iteratively edit model code and retain edits based on scalar task-performance feedback, have largely ignored this trade-off. We find that scalar feedback supports broad exploration early in search, but it does not reveal how edit