2 papers
hep-th2026
TheoPhysEval: Assessing AI agents at autonomous theoretical physics problem solving
A. Ramesh Chandra, Aaghaz Mahajan, Ainesh Sanyal +32
We present a suite of 50 unpublished research-level theoretical physics problems with verifiable answers. Without AI assistance, a typical problem in our collection could be solved…
cs.AI2026
From Outcomes to Strategies: Learning Strategy Utility for Mathematical Reasoning
Ruikang Zhang, Xiao An, Xuli Shen +5
Reinforcement learning with verifiable rewards has substantially improved mathematical reasoning. However, terminal correctness alone provides limited insight into the quality of h…