Scientific discovery with language models requires finding the best valid solution within a finite computational budget. We study how feedback from this search can improve the procedure used to conduct it. We introduce HarnessTTT, a test-time learning framework that trains a harness proposer while keeping the executor...
Ying-Zi Ma, Yu-Long Cao, Nan-Xi Li et al.· 0 citations
What makes great scientists great? Even as AI systems start to make progress on open problems, scientists remain far ahead of them at sensing which prior idea, buried in an ever-growing archive of research, a new problem needs. To study this skill, we draw on researchers who know firsthand which earlier work advanced t...
Sohyeon Kim, Yoonho Lee, Bo Liu et al.· 0 citations
It is shown that ZebraArena requires a combination of in-depth reasoning and accurate external tool calling, which remains a challenge as frontier reasoning models such as GPT-5 and Gemini 2.5 Pro only achieves 60% accuracy on the hard instances.
Wan-Jia Zhao, Ludwig Schmidt, Ye-Jin Choi et al.· arXiv.org· 1 citation
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