FileGram: Grounding Agent Personalization in File-System Behavioral Traces
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.
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Shuai Liu
claimed_verified
2026-04-07T08:34:01.870Z
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Shuai Liu
Choiszt
user
Choiszt
69d47d9f2c523d36431fb15f
Shulin Tian
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Kairui Hu
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Yuhao Dong
claimed_verified
2026-04-07T08:34:05.496Z
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Yuhao Dong
THUdyh
user
THUdyh
69d47d9f2c523d36431fb162
Zhe Yang
69d47d9f2c523d36431fb163
Bo Li
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Jingkang Yang
69d47d9f2c523d36431fb165
Chen Change Loy
69d47d9f2c523d36431fb166
Ziwei Liu
FileGram: Grounding Agent Personalization in File-System Behavioral Traces
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.
FileGram is a framework for personalized AI agents that uses file-system behavioral traces to enhance memory systems and agent personalization, featuring a data engine, diagnostic benchmark, and memory architecture built from atomic actions and content changes.
file-system behavioral traces persona-driven data engine multimodal action sequences diagnostic benchmark memory systems profile reconstruction trace disentanglement persona drift detection multimodal grounding bottom-up memory architecture atomic actions content deltas procedural channels semantic channels episodic channels query-time abstraction