ExpMem: Mitigating Hallucinations in LLM Agents via Expectation-Driven Memory

Large language models (LLMs) inherit extensive general priors from pre-training to support reasoning and decision-making, yet these generic priors often conflict with the specific rules and operational constraints of real deployment environments. Such mismatches lead to severe action-feasibility hallucinations, invalid agent behaviors, and stalled task progress. Existing post-training remedies, including supervised fine-tuning and reinforcement learning, can alleviate such issues but require costly data collection and computational overhead. To tackle this problem, we propose ExpMem, an expectation-driven memory framework that mitigates hallucinations in LLM agents. Our framework builds compact, structured environmental memory to encode heterogeneous environmental rules, and equips a memory-augmented reasoning module to retrieve constraint-aware knowledge and fuse it into real-time decision-making. This design accelerates environmental exploration, eliminates prior-induced reasoning biases, and effectively suppresses mismatch-caused hallucinations. Benefiting from its parameter-free and plug-and-play nature, our method provides a low-cost, adaptable solution for real-world LLM agent deployment. Experiments on ALFWorld and ScienceWorld show that ExpMem reduces repeated ineffective decisions while improving task success and interaction efficiency. Code is available at: https://anonymous.4open.science/r/expmem/