Adversarial games have driven advances from heuristic search to reinforcement learning, yet learning and adapting strategies from limited samples remain challenging. AI agents offer an alternative by turning game experience into revisions of executable policies. Building on heuristic learning (HL), we formalize Adversa...
Kai-Sen Yang, Qing-Le Liu, Ke-Jin Wang et al.· 0 citations
PPTBench establishes a measurable testbed for studying visual coding and advancing agents toward more reliable visual creation, and shows that agents can generally produce valid slide files, but struggle to produce high-quality reconstructions that faithfully recover the semantics and visual structure of the target.
Xiao-Qiu Wang, Yi-Zhe Chi, Wen-Yi Li et al.· 0 citations
This work presents AI4AI\mbox{-}Bench, 10 frozen research repositories spanning 10 training algorithm families, and releases the task suite, the evaluators and every scored submission, so that the measurement can be repeated as these systems change.
Yi-Zhe Chi, Wen-Yi Li, De-Yao Hong et al.· 11 citations· ⚡3
SWE Refactor Bench is introduced, a benchmark comprising 20 whole-repository migrations, covering 4 kinds of technical debt, and SWE Refactor Bench is positioned as a rigorous testbed for developing coding agents for reliable whole-repository migrations.
De-Yao Hong, Yi-Zhe Chi, Wen-Yi Li et al.· 4 citations· ⚡2
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