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cs.AI2026

CaveAgent: Transforming LLMs into Stateful Runtime Operators

Maohao Ran, Zhenglin Wan, Cooper Lin +21

LLM-based agents are increasingly capable of complex task execution, yet current agentic systems remain constrained by text-centric paradigms that struggle with long-horizon tasks…

cs.AI2026

MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems

Qianshu Cai, Yonggang Zhang, Xianzhang Jia +5

Autonomous agentic systems are largely static after deployment: they do not learn from user interactions, and recurring failures persist until the next human-driven update ships a…

cs.AI2026

ClawNet: Human-Symbiotic Agent Network for Cross-User Autonomous Cooperation

Zhiqin Yang, Zhenyuan Zhang, Xianzhang Jia +4

Current AI agent frameworks have made remarkable progress in automating individual tasks, yet all existing systems serve a single user. Human productivity rests on the social and o…

cs.AI2026

Reinforcement Learning of Large Language Models for Interpretable Credit Card Fraud Detection

Cooper Lin, Yanting Zhang, Maohao Ran +7

E-commerce platforms and payment solution providers face increasingly sophisticated fraud schemes, ranging from identity theft and account takeovers to complex money laundering ope…

cs.AI2026

Crisis-Bench: Benchmarking Strategic Ambiguity and Reputation Management in Large Language Models

Cooper Lin, Maohao Ran, Yanting Zhang +6

Standard safety alignment optimizes Large Language Models (LLMs) for universal helpfulness and honesty, effectively instilling a rigid "Boy Scout" morality. While robust for genera…