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cs.CL2026
The Deliberative Illusion: Diagnosing Factual Attrition and Stance Homogenization in Multi-Agent LLM Deliberation
Herun Wan, Jiaying Wu, Minnan Luo +4
Multi-agent LLM systems often treat consensus as evidence of successful interaction. For deliberative problems, however, reliability depends on whether agents preserve the facts an…
cs.CL2026
Better with Experience: Self-Evolving LLM Agents for Evidence-Grounded Health Community Notes
Zihang Fu, Fanxiao Li, Jianyang Gu +5
Large Language Model (LLM)-augmented Community Notes offer a scalable path for timely, evidence-grounded correction of health misinformation on social platforms. However, they stil…
cs.CL2026
The Facade of Truth: Uncovering and Mitigating LLM Susceptibility to Deceptive Evidence
Herun Wan, Jiaying Wu, Minnan Luo +3
To reliably assist human decision-making, LLMs must maintain factual internal beliefs against misleading injections. While current models resist explicit misinformation, we uncover…