activity
20242026
most citedMitigating Social Bias in Large Language Models: A Multi-Objective Approach within a Multi-Agent Framework

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

collaborators

14 papers

cs.AI2026

SALA: Semantic-Aware Logical Alignment for Complex Reasoning in In-Context Learning

Zhao Ji, Wenqing Chen, Zhixuan Chu +4

Effective in-context learning (ICL) for complex reasoning relies on selecting the right demonstrations. Traditional retrieval methods based on surface similarity fail to capture th…

cs.AI2026

CoMerge: Conflict-Driven Preference Optimization for Multi-Task Model Merging

Mingjie Zheng, Zihao Chen, Wenqing Chen +4

Model merging provides an efficient paradigm for constructing multi-task large language models (LLMs) without full model retraining, yet it remains challenged by parameter interfer…

cs.CR2026

AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents

Ruoyu Wang, Heng Zhao, Renjie Wu +4

Large language model (LLM) agents automate penetration testing through an observation-action loop, selecting actions based on observations returned by tools. This dependence allows…

cs.AI2026

REIN: Bridging the Gap between Reasoning and Reliability via Reflection and Abstention Alignment

Zhengze Huang, Luyang Yu, Di Hong +5

Large reasoning models (LRMs) are prone to hallucination, which undermines their reliability and poses challenges for safe deployment. Hallucinations in LRMs arise from two distinc…

cs.AI2026

EHR-Complex: Benchmarking Medical Agents for Complex Clinical Reasoning

Yitong Qiao, Lei Liu, Yue Shen +4

Clinical agents promise to democratize access to electronic health records (EHRs), yet existing benchmarks fail to reflect the complexity of practical EHR analysis, e.g., often ope…

cs.CL2026

ConsisGuard: Aligning Safety Deliberation with Policy Enforcement in LLM Guardrails

Yan Wang, Zhixuan Chu, Zihao Xue +9

Reasoning-based LLM guardrails improve safety moderation by generating explicit rationales before issuing final decisions. However, their rationales do not always lead to faithful…