most citedToo Good to be Bad: On the Failure of LLMs to Role-Play Villains

2 citations · 2 across the 5 of their papers we have counts for

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cs.AI2026

Think Fast and Slow: Step-Level Cognitive Depth Adaptation for LLM Agents

Ruihan Yang, Fanghua Ye, Xiang We +10

Large language models (LLMs) are increasingly deployed as autonomous agents for multi-turn decision-making tasks. However, current agents typically rely on fixed cognitive patterns…

cs.AI2025

CodeTool: Enhancing Programmatic Tool Invocation of LLMs via Process Supervision

Yifei Lu, Fanghua Ye, Jian Li +6

Tool invocation significantly enhances the capabilities of Large Language Models (LLMs), yet challenges persist, particularly in complex task scenarios. Current methods, such as in…