most citedMoltNet: Understanding Social Behavior of AI Agents in the Agent-Native MoltBook

1 citations · 1 across the 8 of their papers we have counts for

collaborators

8 papers

cs.SI2026

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…

cs.AI2026

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…

cs.AI2026

Multilingual Fine-Tuning via Localized Gradient Conflict Resolution

Long P. Hoang, Yiran Zhao, Wei Lu +1

The rapid evolution of Large Language Models (LLMs) has established cross-lingual versatility as a defining feature of modern systems. However, fine-tuning these models frequently…

cs.AI2026

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…

cs.CL2026

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…

cs.CL2026

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…