Author

Chong-He Jiang

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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 Apr 2026

CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery

Evaluated on diverse mathematical, algorithmic, and systems optimization tasks, CORAL sets new state-of-the-art results on 10 tasks, achieving 3-10 times higher improvement rates with far fewer evaluations than fixed evolutionary search baselines across tasks.

Ao Qu, Handi Zheng, Zi-Jian Zhou et al. · arXiv.org · 38 citations · ⚡7