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cs.AI2026
Right Answer, Wrong Method: Shortcut Hacking Misleads the Evaluation of LLM Reasoning on Frontier Science Benchmarks
Xuan Ren, Weiqi Zhai, Tianle Pu +3
Scientific reasoning benchmarks typically evaluate large language models (LLMs) using final-answer accuracy. However, a correct answer does not necessarily demonstrate the reasonin…
cs.AI2026
A2DEPT: Large Language Model-Driven Automated Algorithm Design via Evolutionary Program Trees
Bin Chen, Shouliang Zhu, Beidan Liu +4
Designing heuristics for combinatorial optimization problems (COPs) is a fundamental yet challenging task that traditionally requires extensive domain expertise. Recently, Large La…
cs.AI2026
Learn to Relax with Large Language Models: Solving Constraint Optimization Problems via Bidirectional Coevolution
Beidan Liu, Zhengqiu Zhu, Chen Gao +4
Large Language Model (LLM)-based optimization has recently shown promise for autonomous problem solving, yet most approaches still cast LLMs as passive constraint checkers rather t…