发表机构
Tencent（腾讯）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MemoryForge是从简短目标画像合成终身记忆的框架，通过记忆条件让冻结LLM动态检索相关记忆，在两类任务实验中使其表现更类人。
AI 中文摘要
为大型语言模型（LLM）配备类人角色对角色扮演、用户模拟等智能体应用至关重要。传统基于提示的方法依赖注入静态文本画像的描述性条件，因缺乏真实生活记忆，常导致智能体表现出通用行为。为填补这一空白，我们引入基于记忆的条件，这一范式受认知心理学启发，用自传体记忆库替代抽象画像，使冻结的LLM能动态检索情境相关记忆以指导行为。我们将其支撑任务形式化为定制终身记忆合成，并提出MemoryForge，这一从简短目标画像合成此类终身记忆的新框架。MemoryForge包含三个关键组件：用于社会历史接地的上下文生成器、用于向目标身份发展连贯性的生活组织者，以及平衡广泛时间摘要与高保真事件体验的多分辨率模拟器。在PersonaGym（角色扮演）和SimulatorArena（用户模拟）上的实验表明，MemoryForge合成的记忆库，在多个指标和LLM主干上，使冻结的LLM表现出比强大的描述性条件基线更类人的行为。
英文摘要
Equipping Large Language Models (LLMs) with human-like personas is crucial for agentic applications, such as role-play and user simulation. Traditional prompt-based methods rely on descriptive conditioning by injecting static textual profiles, which often makes agents show generic behaviors due to a lack of realistic life memory. To fill this gap, we introduce memory-based conditioning, a paradigm inspired by the cognitive psychology, which replaces abstract profiles with an autobiographical memory base, enabling frozen LLMs to dynamically retrieve situation-relevant memory to guide their behaviors. We formalize its enabling task as customized lifelong memory synthesis and propose MemoryForge, a novel framework to synthesize such lifelong memory from brief target personas. MemoryForge has three key components: a context generator for socio-historical grounding, a life organizer for developmental coherence toward the target identity, and a multi-resolution simulator that balances broad temporal summaries with high-fidelity episodic experiences. Experiments on PersonaGym for role-play and SimulatorArena for user-simulation, show that the synthesized memory base by MemoryForge enables frozen LLMs to exhibit more human-like behaviors than strong descriptive conditioning baselines across multiple metrics and LLM backbones.
