[2604.04901] FileGram: Grounding Agent Personalization in File-System Behavioral Traces

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.04901 (cs) [Submitted on 6 Apr 2026] Title:FileGram: Grounding Agent Personalization in File-System Behavioral Traces Authors:Shuai Liu, Shulin Tian, Kairui Hu, Yuhao Dong, Zhe Yang, Bo Li, Jingkang Yang, Chen Change Loy, Ziwei Liu View a PDF of the paper titled FileGram: Grounding Agent Personalization in File-System Behavioral Traces, by Shuai Liu and 8 other authors View PDF HTML (experimental) Abstract: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) Fil...