让大模型自动修正记忆错误，应对环境变化
GLOVE: Global Verifier for LLM Memory-Environment Realignment
- 通过主动探测记忆与新观察的不一致来验证记忆
- 在动态环境中使智能体成功率显著提升
- 无需真实标签或深度自我反思，适合长期运行系统
现有增强记忆的大语言模型方法通常假设记忆有效性可通过外部评估器的任务成功信号或内部反思机制建立。但在存在动态漂移的实际环境中，这些假设常失效。本文提出全局验证器（GLOVE），引入记忆系统的新设计维度——相对真理概念。通过主动探测检索记忆与新鲜观察之间的不一致性，GLOVE实现无需真实标签监督或强依赖模型内省的内存-环境重对齐。我们在涵盖网页导航、规划和控制的多个基准上评估GLOVE，引入可控环境漂移以模拟非平稳性。结果表明，GLOVE显著提升智能体成功率，为具备自我演进能力的认知智能体提供稳健路径。
原文摘要 · Abstract (English)
Most existing memory-enhanced Large Language Model (LLM) approaches implicitly assume that memory validity can be established either through external evaluators that provide task-specific success signals or through internal model cognition, such as reflection, for editing memory entries. However, these assumptions often break down in practical environments with dynamic drifts. We propose the Global Verifier (GLOVE), a framework that introduces a new design dimension for LLM memory systems by establishing a relative notion of truth. Through active probing to detect inconsistencies between retrieved memories and fresh observations, GLOVE enables memory-environment realignment by verifying and updating memory without access to ground-truth supervision or strong reliance on model introspection. We evaluate GLOVE on diverse benchmarks spanning web navigation, planning, and control, augmented with controlled environmental drifts that introduce non-stationarity beyond the original benchmark settings. Our results show that GLOVE substantially improves agent success rates, suggesting a robust pathway to cognitive agents capable of self-evolving.
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