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

2 citations · 3 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

Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training

Mengru Wang, Xingyu Chen, Yue Wang +12

Mixture-of-Experts (MoE) architectures within Large Reasoning Models (LRMs) have achieved impressive reasoning capabilities by selectively activating experts to facilitate structur…