arxiv
PublishedOctober 15, 2026 at 4:00 AM
Evolving in Thought Space: Training a Small Model at Test Time Unlocks Better Discoveries
Publisher summary· verbatim
arXiv:2610.06269v2 Announce Type: replace Abstract: Open-ended scientific discovery often requires repeatedly proposing and evaluating candidate solutions. LLM-based systems can support this process by generating and refining executable solutions from verifier feedback. Methods such as TTT-Discover
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivL2G-SR: Learning to Guide LLM-based Symbolic Regression8harxivBridging the Gap: Converting Read Text to Conversational Dialogue8harxivWhen Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence8harxivARIA: Runtime Inspection and Amendment for Multi-Agent Systems8hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗