most citedAttacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence

3 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE2026

Not as Sweet by Another Name: An Empirical Study of Format Robustness in LLM Document Workflows

Xiaoyu Zhang, Xianyun Cheng, Tianlin Li +3

LLM-driven software systems are rapidly evolving from plain-text conversations to document-centric end-to-end workflows, where the same semantic content can be delivered in diverse…

cs.SE2026

Ensemble-Based Uncertainty Estimation for Code Correctness Estimation

Yunxiang Wei, Tianlin Li, Yuwei Zheng +6

Large language models (LLMs) have demonstrated remarkable capabilities in generating programs from natural language descriptions, yet ensuring their correctness without an external…

cs.AI2026

How Emotion Shapes the Behavior of LLMs and Agents: A Mechanistic Study

Moran Sun, Tianlin Li, Yuwei Zheng +4

Emotion plays an important role in human cognition and performance. Motivated by this, we investigate whether analogous emotional signals can shape the behavior of large language m…

cs.LG2023★ 1 cited

Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization

Simin Li, Ruixiao Xu, Jingqiao Xiu +4

In multi-agent reinforcement learning (MARL), ensuring robustness against unpredictable or worst-case actions by allies is crucial for real-world deployment. Existing robust MARL m…

cs.LG2023★ 3 cited

Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence

Simin Li, Jun Guo, Jingqiao Xiu +8

This study probes the vulnerabilities of cooperative multi-agent reinforcement learning (c-MARL) under adversarial attacks, a critical determinant of c-MARL's worst-case performanc…