Author

Siyuan Liu

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#artificial intelligence Preprint Oct 2026

Evolving in Thought Space: Training a Small Model at Test Time Unlocks Better Discoveries

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 use test-time training (TTT) to update the solution-generating LLM from...

Chong-He Jiang, Ao Qu, Si-Yuan Liu et al. · 0 citations

#artificial intelligence Preprint Sep 2026

VST: Verifiable Structured Transport for Auditable Agent-to-Agent Alpha Discovery

Single-run agent-to-agent alpha discovery results are reported descriptively, gross of costs, and are explicit about their limits throughout; in particular they do not isolate the effect of the leap machinery from the inherited search substrate, which is left to future work.

Yu-Qi Li, Si-Yuan Liu, Bing-Jun Liu · 0 citations