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cs.CY2026
Is Your LLM-as-a-Recommender Agent Trustable? LLMs' Recommendation is Easily Hacked by Biases (Preferences)
Zichen Tang, Zirui Zhang, Qian Wang +3
Current Large Language Models (LLMs) are gradually exploited in practically valuable agentic workflows such as Deep Research, E-commerce recommendation, and job recruitment. In the…
cs.CY2026
Making Bias Non-Predictive: Training Robust LLM Reasoning via Reinforcement Learning
Qian Wang, Xuandong Zhao, Zirui Zhang +4
Large language models (LLMs) increasingly serve as reasoners and automated evaluators, yet they remain susceptible to cognitive biases -- often altering their reasoning when faced…