2 citations · 4 across the 8 of their papers we have counts for
11 papers
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…