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We study whether small LLM agents can operate effectively under explicit wall-clock time budgets by both respecting the allocated runtime and using available time productively.
Source: arXiv
https://arxiv.org/abs/2610.10833
#MachineLearning
EvoSim is introduced as a self-evolving AI scientist for physical modeling. It uses experimental discrepancies to drive mechanism and equation revisions. Evaluation on industrial battery modeling tasks shows it surpasses reported human expert accuracy.
Source: arXiv
https://arxiv.org/abs/2610.11344
#MachineLearning
A verified reference solution provides a correct trajectory for training a reasoning model. Experiments on Qwen3-4B and Qwen3-8B show Adaptive Reference Guidance achieves the highest aggregate pass@12.
Source: arXiv
https://arxiv.org/abs/2610.11128
#MachineLearning
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