FileGram: Grounding Agent Personalization in File-System Behavioral Traces

• URL: http://arxiv.org/abs/2604.04901v1
• Date: Mon, 06 Apr 2026 17:49:31 GMT
• Title: FileGram: Grounding Agent Personalization in File-System Behavioral Traces
• Abstract summary: FileGram is a comprehensive framework that grounds agent memory and personalization in file-system behavioral traces.<n>It consists of FileGramEngine, FileGramBench, and FileGramOS.<n>By open-sourcing the framework, we hope to support future research on personalized memory-centric file-system agents.
• Score: 79.65734230265521
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• HitMem: Hierarchical Temporal 3D Memory with Multi-Modal Context-Aware Retrieval for Dynamic Environments [5.55387168911911]
  HitMem is a hierarchical temporal 3D memory framework with a multi-modal context-aware retrieval mechanism.<n>We show that HitMem significantly improves object relocation accuracy, reduces exploration costs, and enhances task execution performance in dynamic environments.
  arXiv  Detail & Related papers  (2026-09-01T09:09:21Z)
• Terminal Agents: A Survey of AI Agents in Command-Line Environments [102.48222742145474]
  Large language model agents increasingly act through terminals, yet existing surveys disperse terminal-mediated behavior across software engineering, tool use, and computer-use research.<n>We regard terminal agents as systems whose dominant progress-bearing action--observation loop is mediated by terminal command execution, textual feedback, and stateful environment interaction.<n>Our synthesis shows that realized behavior is jointly shaped by the model, interface, harness, runtime, and environment.
  arXiv  Detail & Related papers  (2026-08-20T18:16:44Z)
• PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution [49.776611937968]
  We present PhyAgentOS, a foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services.<n>Its Session-Centered treats a session, not an action, as the minimum unit of scheduling, compatibility, supervised execution, evidence collection, and acceptance.<n>SessionVerifier distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan.<n> Benchmarking reuses the deployment session and verification path, so results trace to real execution.
  arXiv  Detail & Related papers  (2026-07-18T04:46:53Z)
• REMIND: RE-Identification with Memory for INDoor Navigation [39.146761527401424]
  Online tracker designed for long-term multi-object re-identification of generic indoor objects from monocular RGB imagery.<n>Motivated by evidence from visual cognition that humans rely on accumulated appearance familiarity and spatial context rather than explicit self-localization.<n>On ScanNet++, it attains the highest IDF1 in every setting but one, end-to-end detection over all scenes.
  arXiv  Detail & Related papers  (2026-07-10T10:30:50Z)
• Are We Ready For An Agent-Native Memory System? [37.760278978612874]
  Memory for large language model (LLM) agents has evolved from simple retrieval-augmented mechanisms into a data management system.<n>Existing evaluations benchmark agent memory mainly through end-to-end task success metrics.<n>We present a systematic experimental study of agent memory from a data management perspective.
  arXiv  Detail & Related papers  (2026-06-23T16:34:55Z)
• The Model Is Not the Product: A Dual-Pillar Architecture for Local-First Psychological Coaching [51.56484100374058]
  This report introduces Psych LM, an iOS application that validates the thesis that, for such applications, the surrounding architecture is paramount.<n>Psych LM runs a local, on-device language model within a purpose-built, local-first runtime designed for behavioral and life-coaching applications.
  arXiv  Detail & Related papers  (2026-05-23T05:49:11Z)
• Mem-W: Latent Memory-Native GUI Agents [50.87647372904382]
  We introduce Mem-W, a series of latent-memory-native GUI agents that treat memory as part of the agent's continuous context.<n>Across four web and mobile navigation benchmarks, Mem-W consistently improves diverse backbones and memory-enhanced baselines.
  arXiv  Detail & Related papers  (2026-05-10T04:31:23Z)
• Architecture-Aware Multi-Design Generation for Repository-Level Feature Addition [53.50448142467294]
  RAIM is a multi-design and architecture-aware framework for repository-level feature addition.<n>It shifts away from linear patching by generating multiple diverse implementation designs.<n>Experiments on the NoCode-bench Verified dataset demonstrate that RAIM establishes a new state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-03-02T12:50:40Z)
• VimRAG: Navigating Massive Visual Context in Retrieval-Augmented Generation via Multimodal Memory Graph [42.348770377488094]
  VimRAG is a framework tailored for multimodal Retrieval-augmented Reasoning across text, images, and videos.<n>We propose a Graph-Guided Policy Optimization strategy to disentangle step-wise validity from trajectory-level rewards.<n>Experiments demonstrate that VimRAG consistently achieves state-of-the-art performance on diverse multimodal RAG benchmarks.
  arXiv  Detail & Related papers  (2026-02-13T09:05:09Z)
• The AI Hippocampus: How Far are We From Human Memory? [77.04745635827278]
  Implicit memory refers to the knowledge embedded within the internal parameters of pre-trained transformers.<n>Explicit memory involves external storage and retrieval components designed to augment model outputs with dynamic, queryable knowledge representations.<n>Agentic memory introduces persistent, temporally extended memory structures within autonomous agents.
  arXiv  Detail & Related papers  (2026-01-14T03:24:08Z)
• MemRec: Collaborative Memory-Augmented Agentic Recommender System [57.548438733740504]
  We propose MemRec, a framework that architecturally decouples reasoning from memory management.<n>MemRec introduces a dedicated LM_Mem to manage a dynamic collaborative memory graph.<n>It achieves state-of-the-art performance on four benchmarks.
  arXiv  Detail & Related papers  (2026-01-13T18:51:16Z)
• OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agent [58.07447442040785]
  We introduce OS-Symphony, a holistic framework that comprises an Orchestrator coordinating two key innovations for robust automation.<n>Results demonstrate that OS-Symphony delivers substantial performance gains across varying model scales.
  arXiv  Detail & Related papers  (2026-01-12T17:55:51Z)
• RGMem: Renormalization Group-based Memory Evolution for Language Agent User Profile [8.224917568034572]
  We propose a self-evolving memory framework, inspired by the ideology of classic renormalization group (RG) in physics.<n>This framework enables to organize the dialogue history in multiple scales.<n>The core innovation of our work lies in modeling memory evolution as a multi-scale process of information compression and emergence.
  arXiv  Detail & Related papers  (2025-10-18T08:16:46Z)
• MEMTRACK: Evaluating Long-Term Memory and State Tracking in Multi-Platform Dynamic Agent Environments [6.12783571098263]
  MEMTRACK is a benchmark designed to evaluate long-term memory and state tracking in multi-platform agent environments.<n>Each benchmark instance provides a chronologically platform-interleaved timeline, with noisy, conflicting, cross-referring information.<n>Our benchmark tests memory capabilities such as acquistion, selection and conflict resolution.
  arXiv  Detail & Related papers  (2025-10-01T18:34:03Z)
• SEDM: Scalable Self-Evolving Distributed Memory for Agents [23.182291416527764]
  SEDM is a verifiable and adaptive framework that transforms memory from a passive repository into an active, self-optimizing component.<n>We show that SEDM improves reasoning accuracy while reducing token overhead compared with strong memory baselines.<n>Results highlight SEDM as a scalable and sustainable memory mechanism for open-ended multi-agent collaboration.
  arXiv  Detail & Related papers  (2025-09-11T14:37:37Z)
• Optimizing Cooperative Multi-Object Tracking using Graph Signal Processing [45.68287260385148]
  This paper proposes a novel Cooperative MOT framework for tracking objects in 3D LiDAR scene.<n>By exploiting a fully connected graph topology defined by the detected bounding boxes, we employ the Graph Laplacian processing optimization technique.<n>An extensive evaluation study has been conducted, using the real-world V2V4Real dataset.
  arXiv  Detail & Related papers  (2025-06-11T07:21:58Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.