让大模型世界模型在运行时自我修正，提升规划准确性。
Self-Evolving World Models for LLM Agent Planning
- 通过记忆检索和错误分析，动态优化预测上下文。
- 在两个基准上预测准确率最高，下游任务成功率显著提升。
- 适合需要长期规划的智能体系统，尤其关注可靠性问题。
世界模型为长周期大语言模型智能体提供前瞻性预测：在执行前预判动作后果。然而不可靠的预测可能被忽视、误用，甚至损害下游决策。本文提出WorldEvolver——一种自演化世界模型框架，在不改变下游智能体和模型参数的前提下，于部署时动态修订上下文。该框架包含三个模块：(i) 情景记忆，基于真实动作转移进行检索式模拟；(ii) 语义记忆，从预测与观察的偏差中提取持久性启发规则；(iii) 选择性前瞻，过滤低置信度预测后融入智能体推理上下文。我们在ALFWorld和ScienceWorld上评估，使用Word2World衡量世界模型预测准确率，使用AgentBoard衡量下游智能体成功率。大量实验表明，WorldEvolver在三种骨干模型上均取得最高预测准确率，并在下游任务中超越其他世界模型基线，证明测试时记忆修正能同时提升预测保真度与规划性能。
原文摘要 · Abstract (English)
World models offer a principled way to equip long-horizon LLM agents with foresight: predictions of action consequences before execution. However, unreliable foresight can be ignored, misused, or even degrade downstream decision-making. In this paper, we introduce WorldEvolver, a self-evolving world model framework that revises its deployment-time context while keeping the downstream agent and all model parameters frozen. WorldEvolver integrates three modules: (i) Episodic Memory, which exploits real action transitions through retrieval-based simulation; (ii) Semantic Memory, which extracts persistent heuristic rules from prediction-observation mismatches; and (iii) Selective Foresight, which filters low-confidence predictions before integrating them into agent reasoning context. We evaluate WorldEvolver on ALFWorld and ScienceWorld, measuring world model prediction accuracy on Word2World and downstream agent success rate on AgentBoard. Extensive experiments show that WorldEvolver achieves the highest prediction accuracy across three backbones and leads other world model baselines on downstream agent success rate, demonstrating that test-time memory revision enhances both predictive fidelity and planning performance.
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