3 papers
cs.AI2026
Beyond Confidence: Stability-Aware Test-Time Adaptation for LLM Reasoning
Bincheng Gu, Min Gao, Zongwei Wang +3
Test-time adaptation has emerged as a lightweight alternative to costly post-training for improving the reasoning capabilities of Large Language Models (LLMs) on downstream tasks.…
cs.IR2026
Ahead of the Spread: Agent-Driven Virtual Propagation for Early Fake News Detection
Bincheng Gu, Min Gao, Junliang Yu +4
Early detection of fake news is critical for mitigating its rapid dissemination on social media, which can severely undermine public trust and social stability. Recent advancements…
cs.AI2026
When Agents See Humans as the Outgroup: Belief-Dependent Bias in LLM-Powered Agents
Zongwei Wang, Bincheng Gu, Hongyu Yu +5
This paper reveals that LLM-powered agents exhibit not only demographic bias (e.g., gender, religion) but also intergroup bias under minimal "us" versus "them" cues. When such grou…