1 citations · 1 across the 8 of their papers we have counts for
10 papers
How Does Post-Training Shape LLM Value Preferences?
Yang Liu, Shaoyang Xu, Masahiro Kaneko +3
During post-training, large language models (LLMs) learn value preferences from corpora that may contain imbalanced proportions of opinions associated with different demographic gr…
Beyond Perfect Teammates: Multi-Agent Collaboration with Unreliable Colleagues
Chen Huang, Yiyang Zhang, Yuhao Wu +2
LLM-based multi-agent collaboration is increasingly used to solve complex tasks by dividing work among specialized agents and integrating complementary expertise. Yet existing syst…
Aligned but Flattened: Analyzing the Trade-off between Cultural Alignment and Diversity in LLMs
Jingshen Zhang, Shaoyang Xu, Wenxuan Zhang
Cultural fine-tuning has become the de facto paradigm for building culture-aware large language models (LLMs), yet existing optimization exclusively for alignment scores provides a…
Safety Paradox: How Enhanced Safety Awareness Leaves LLMs Vulnerable to Posterior Attack
Long P. Hoang, Hai V. Le, Shaoyang Xu +2
Large language models (LLMs) are rigorously aligned to refuse harmful requests, a process that inherently cultivates a latent capacity to evaluate and recognize unsafe content. In…
Multilingual Fine-Tuning via Localized Gradient Conflict Resolution
Long P. Hoang, Yiran Zhao, Wei Lu +1
Cross-lingual versatility has become a defining capability of Large Language Models (LLMs). However, fine-tuning these models often induces negative interference, where gains in on…
What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems
Chen Huang, Yuhao Wu, Wenxuan Zhang
Multi-agent systems (MAS) built on large language models are typically organized around roles, pipelines, and turn schedules, while the content that agents pass to one another is o…
Beyond Alignment: Value Diversity as a Collective Property in Multicultural Agent Systems
Shaoyang Xu, Jingshen Zhang, Long P. Hoang +2
Multicultural multi-agent systems are increasingly deployed in globally diverse settings, where different agents are grounded in different cultural backgrounds. Existing cultural e…
Process Rewards with Learned Reliability
Jinyuan Li, Langlin Huang, Chengsong Huang +5
Process Reward Models (PRMs) provide step-level feedback for reasoning, but current PRMs usually output only a single reward score for each step. Downstream methods must therefore…
Training Data Efficiency in Multimodal Process Reward Models
Jinyuan Li, Chengsong Huang, Langlin Huang +4
Multimodal Process Reward Models (MPRMs) are central to step-level supervision for visual reasoning in MLLMs. Training MPRMs typically requires large-scale Monte Carlo (MC)-annotat…
MoltNet: Understanding Social Behavior of AI Agents in the Agent-Native MoltBook
Yi Feng, Chen Huang, Zhibo Man +4
Large-scale communities of AI agents are becoming increasingly prevalent, creating new environments for agent-agent social interaction. Prior work has examined multi-agent behavior…