文件图谱：基于文件系统行为痕迹的智能体个性化建模

FileGram: Grounding Agent Personalization in File-System Behavioral Traces

April 6, 2026

作者: Shuai Liu, Shulin Tian, Kairui Hu, Yuhao Dong, Zhe Yang, Bo Li, Jingkang Yang, Chen Change Loy, Ziwei Liu

cs.AI

摘要

基于本地文件系统协同工作的AI智能体正迅速成为人机交互的新范式，但严格的数据限制导致有效个性化仍面临挑战——隐私壁垒与多模态现实行为轨迹的联合采集困难阻碍了可扩展的训练与评估，现有方法仍以交互为中心而忽视了文件系统操作中的密集行为轨迹。为此，我们提出FileGram框架，将智能体记忆与个性化能力锚定于文件系统行为轨迹，该框架包含三个核心组件：（1）FileGramEngine可扩展人设驱动数据引擎，能模拟真实工作流并生成细粒度多模态操作序列；（2）FileGramBench基于文件系统行为轨迹的诊断基准，支持档案重建、轨迹解耦、人设漂移检测和多模态 grounding 四项记忆系统评估任务；（3）FileGramOS自底向上的记忆架构，直接从原子操作与内容增量（而非对话摘要）构建用户画像，通过程序性、语义性和情景性三通道编码轨迹，并支持查询时抽象处理。大量实验表明，FileGramBench对当前最先进的记忆系统仍具挑战性，FileGramEngine与FileGramOS均表现优异。通过开源此框架，我们期望为个性化记忆中心型文件系统智能体的后续研究提供支持。

English

Coworking AI agents operating within local file systems are rapidly emerging as a paradigm in human-AI interaction; however, effective personalization remains limited by severe data constraints, as strict privacy barriers and the difficulty of jointly collecting multimodal real-world traces prevent scalable training and evaluation, and existing methods remain interaction-centric while overlooking dense behavioral traces in file-system operations; to address this gap, we propose FileGram, a comprehensive framework that grounds agent memory and personalization in file-system behavioral traces, comprising three core components: (1) FileGramEngine, a scalable persona-driven data engine that simulates realistic workflows and generates fine-grained multimodal action sequences at scale; (2) FileGramBench, a diagnostic benchmark grounded in file-system behavioral traces for evaluating memory systems on profile reconstruction, trace disentanglement, persona drift detection, and multimodal grounding; and (3) FileGramOS, a bottom-up memory architecture that builds user profiles directly from atomic actions and content deltas rather than dialogue summaries, encoding these traces into procedural, semantic, and episodic channels with query-time abstraction; extensive experiments show that FileGramBench remains challenging for state-of-the-art memory systems and that FileGramEngine and FileGramOS are effective, and by open-sourcing the framework, we hope to support future research on personalized memory-centric file-system agents.