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

2 citations · 4 across the 12 of their papers we have counts for

collaborators

14 papers

cs.CL2026

Don't Wait to Reply: Towards Responsive yet Thoughtful Dialogue through Proactive Thinking

Ante Wang, Jiaqi Fu, Xuanyi Chen +4

Thinking has emerged as a critical capability for Large Language Models (LLMs) tackling complex tasks. However, its reactive nature, where reasoning is passively triggered only upo…

cs.CV2026

Homer: Understanding Long-form Videos with Hierarchical Memory and Agentic Reasoning

Yixin Ji, Fanghua Ye, Juntao Li +5

Multimodal large language models excel on short clips but struggle on hour-long videos in an online setting, where frames are processed incrementally under limited memory. Existing…

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.CV2026

The Script is All You Need: An Agentic Framework for Long-Horizon Dialogue-to-Cinematic Video Generation

Chenyu Mu, Xin He, Qu Yang +13

Recent advances in video generation have produced models capable of synthesizing stunning visual content from simple text prompts. However, these models struggle to generate long-f…

cs.CL2025★ 2 cited

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

Zihao Yi, Qingxuan Jiang, Ruotian Ma +8

Large Language Models (LLMs) are increasingly tasked with creative generation, including the simulation of fictional characters. However, their ability to portray non-prosocial, an…

cs.CL2025

Social Welfare Function Leaderboard: When LLM Agents Allocate Social Welfare

Zhengliang Shi, Ruotian Ma, Jen-tse Huang +14

Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare. However, the principles and values that guide these models when distri…

cs.CL2025

BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs

Yue Wang, Ruotian Ma, Xingyu Chen +12

The rise of Large Language Models (LLMs) is reshaping multimodel models, with speech synthesis being a prominent application. However, existing approaches often underutilize the li…

cs.CL2025★ 1 cited

The Hunger Game Debate: On the Emergence of Over-Competition in Multi-Agent Systems

Xinbei Ma, Ruotian Ma, Xingyu Chen +14

LLM-based multi-agent systems demonstrate great potential for tackling complex problems, but how competition shapes their behavior remains underexplored. This paper investigates th…

cs.LG2025

RLVMR: Reinforcement Learning with Verifiable Meta-Reasoning Rewards for Robust Long-Horizon Agents

Zijing Zhang, Ziyang Chen, Mingxiao Li +2

The development of autonomous agents for complex, long-horizon tasks is a central goal in AI. However, dominant training paradigms face a critical limitation: reinforcement learnin…

cs.CL2025

CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards

Cheng Liu, Yifei Lu, Fanghua Ye +5

Role-Playing Language Agents (RPLAs) have emerged as a significant application direction for Large Language Models (LLMs). Existing approaches typically rely on prompt engineering…

cs.CL2025★ 1 cited

RLVER: Reinforcement Learning with Verifiable Emotion Rewards for Empathetic Agents

Peisong Wang, Ruotian Ma, Bang Zhang +13

Large language models (LLMs) excel at logical and algorithmic reasoning, yet their emotional intelligence (EQ) still lags far behind their cognitive prowess. While reinforcement le…

cs.CL2025

Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models

Bang Zhang, Ruotian Ma, Qingxuan Jiang +10

Assessing how well a large language model (LLM) understands human, rather than merely text, remains an open challenge. To bridge the gap, we introduce Sentient Agent as a Judge (SA…

cs.CV2025

VISTA: Enhancing Vision-Text Alignment in MLLMs via Cross-Modal Mutual Information Maximization

Mingxiao Li, Na Su, Fang Qu +5

Current multimodal large language models (MLLMs) face a critical challenge in modality alignment, often exhibiting a bias towards textual information at the expense of other modali…

cs.CL2025

SPC: Evolving Self-Play Critic via Adversarial Games for LLM Reasoning

Jiaqi Chen, Bang Zhang, Ruotian Ma +5

Evaluating the step-by-step reliability of large language model (LLM) reasoning, such as Chain-of-Thought, remains challenging due to the difficulty and cost of obtaining high-qual…