FileGram: Grounding Agent Personalization in File-System Behavioral Traces

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) 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.

Figures (7)

• Figure 1: Overview of the FileGram Project. FileGram introduces a personalized AI coworker natively integrated into the user file system. By consolidating cross-session activities and file outputs into long-term behavioral memory, the agent infers intent and proactively synchronizes workspaces, establishing a new paradigm for real-world interactive coworking.
• Figure 2: Data generation pipeline. FileGramEngine generates one trajectory per profile--task pair. Agents execute in profile-isolated workspaces for each task; raw tool traces are filtered and canonicalized to retain real action signals while removing simulation artifacts, and outputs are materialized as standardized behavioral traces with aligned text/document/visual views for cross-modal evaluation.
• Figure 3: Data distribution. 20 profiles $\times$ 32 tasks yield 640 trajectories comprising ${\sim}$10K output files and 20,028 atomic actions.
• Figure 4: FileGramQA distribution. 4.6K questions by track (inner) and sub-task (outer).
• Figure 5: QA examples from FileGramBench. Representative questions from the four tracks, including both MCQ and open-ended formats.