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cs.AI2026
Why Agents Compromise Safety Under Pressure
Hengle Jiang, Ke Tang
Large Language Model agents deployed in complex environments frequently encounter a conflict between maximizing goal achievement and adhering to safety constraints. This paper iden…
cs.AI2024
Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization
Wenqi Zhang, Ke Tang, Hai Wu +7
Large Language Models (LLMs) exhibit robust problem-solving capabilities for diverse tasks. However, most LLM-based agents are designed as specific task solvers with sophisticated…