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
AI assistants are increasingly used as advisors to guide decisions, yet little is known about how people evaluate such advice when the advisor's underlying intent conflicts with their interests. We examine how covert misalignment shapes choices in a randomized experiment (N = 233 participants; 699 observations) in whic...
Sahand Sabour, June M. Liu, Siyang Liu et al.· Proceedings of the National...· 1 citation
Precise instruction-following is a fundamental ability of large language models (LLMs), requiring their outputs to strictly satisfy objective constraints in input instructions. In complex application scenarios, these constraints often possess diverse scopes that govern specific response segments rather than the entire...
Bo-Si Wen, Yi-Lin Niu, Xiao-Ying Ning et al.· 0 citations
Recently, the rapid development of large language models (LLMs) has reshaped software engineering by enabling autonomous code agents that plan, execute, and utilize external tools iteratively to tackle complex tasks. Beyond achieving functional correctness, these agents must faithfully follow process instructions and c...
Bo-Si Wen, Cun-Xiang Wang, Jia-Yi Gui et al.· 0 citations
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