arxiv: 2604.04901 · v1 · submitted 2026-04-06 · 💻 cs.CV · cs.AI

Recognition: 1 theorem link

· Lean Theorem

FileGram: Grounding Agent Personalization in File-System Behavioral Traces

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

show 1 more author

Ziwei Liu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:29 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords AI agent personalizationfile-system behavioral tracesmemory architecturepersona simulationmultimodal groundingprofile reconstructionbottom-up encoding

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The pith

FileGram grounds AI agent personalization in file-system behavioral traces via simulation and bottom-up memory.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper addresses the data scarcity problem for personalizing coworking AI agents that act inside local file systems, where privacy rules and the cost of collecting real multimodal traces block scalable training. It introduces a three-part framework: an engine that generates large volumes of realistic persona-driven file operations, a benchmark that tests memory systems on profile reconstruction and drift detection from those traces, and an operating system that assembles memory directly from atomic actions and content changes instead of dialogue summaries. If the approach works, agents can learn individual user patterns without exposing private files. Experiments indicate that current memory systems perform poorly on the new benchmark while the proposed engine and memory architecture succeed at scale.

Core claim

FileGramEngine produces scalable multimodal action sequences from simulated personas, FileGramBench evaluates memory systems on profile reconstruction, trace disentanglement, persona drift detection, and multimodal grounding, and FileGramOS encodes atomic actions and content deltas into procedural, semantic, and episodic channels that support query-time abstraction, yielding effective personalization where prior interaction-centric methods fall short.

What carries the argument

FileGramOS, the bottom-up memory architecture that constructs user profiles directly from atomic file-system actions and content deltas rather than high-level summaries, then encodes them into procedural, semantic, and episodic channels.

If this is right

FileGramBench exposes clear weaknesses in existing memory systems when they must handle dense file-system behavioral data.
FileGramEngine supplies large-scale synthetic multimodal traces that enable training without real user data.
FileGramOS shows that starting from atomic actions rather than summaries improves reconstruction and drift detection tasks.
Open release of the full framework allows other researchers to build and compare memory-centric file-system agents.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The simulation-plus-bottom-up pattern could transfer to other private activity domains such as browser histories or email folders.
Direct comparison of simulated versus anonymized real traces would quantify how much the engine must be tuned for different user populations.
On-device deployment of FileGramOS might allow personalization while keeping all raw traces local.

Load-bearing premise

Simulated file-system traces generated by the persona-driven engine capture real-world multimodal behavioral patterns well enough for the bottom-up encoding to generalize.

What would settle it

A test showing that FileGramOS produces inaccurate profile reconstructions or misses persona drift when run on genuine human-collected file-system logs instead of the simulated traces.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

FileGram builds a simulator-benchmark-memory stack for file-system agent personalization, but all results stay inside the simulator's own generated traces.

read the letter

The paper's main move is to treat local file-system operations as the primary signal for personalizing AI agents instead of relying on dialogue history. They package this as FileGram with three pieces: FileGramEngine generates large-scale synthetic workflows from defined personas, FileGramBench tests memory systems on tasks like reconstructing user profiles from traces and spotting persona drift, and FileGramOS encodes atomic actions and content deltas into procedural, semantic, and episodic channels that can be queried at different abstraction levels. That bottom-up construction from raw file ops is the clearest departure from prior memory work that starts from conversation summaries. It directly targets the privacy barrier that blocks real trace collection, which is a practical constraint in this corner of agent research. The simulator approach lets them scale data and control variables in ways real logs probably cannot. The benchmark tasks themselves look reasonably diagnostic for the intended use case. The central limitation is that every reported result comes from traces produced by the same engine that defines the personas and workflows. This creates a closed loop where success can reflect internal consistency rather than generalization to actual user behavior. The abstract notes the privacy reason real data is unavailable, but that does not remove the need for some external check on whether the simulated distributions match real file-system patterns. Without numbers, error bars, or ablation details visible here, it is hard to judge how large the gains are or whether the three-channel design is doing the heavy lifting. The work is aimed at researchers building memory architectures for local, non-conversational agents. It is coherent on its own terms and engages the relevant literature on agent memory, so it is worth sending out for review even though the synthetic-data issue will need addressing in revisions.

Referee Report

3 major / 2 minor

Summary. The paper proposes the FileGram framework to address data scarcity in personalizing coworking AI agents operating on local file systems. It introduces three components: FileGramEngine, a persona-driven simulator that generates scalable multimodal file-system action sequences; FileGramBench, a diagnostic benchmark evaluating memory systems on profile reconstruction, trace disentanglement, persona drift detection, and multimodal grounding; and FileGramOS, a bottom-up memory architecture that encodes atomic actions and content deltas into procedural, semantic, and episodic channels with query-time abstraction. The central claim is that extensive experiments demonstrate FileGramBench's challenge for state-of-the-art memory systems and the effectiveness of FileGramEngine and FileGramOS.

Significance. If the effectiveness claims hold under independent validation, the framework could meaningfully advance memory-centric personalization for file-system agents by providing a scalable simulation-based alternative to real traces blocked by privacy constraints. The bottom-up encoding from atomic actions rather than dialogue summaries represents a distinct technical direction, and open-sourcing the components would enable community follow-up on multimodal behavioral grounding.

major comments (3)

[Abstract] Abstract: The assertion that 'extensive experiments show that FileGramBench remains challenging for state-of-the-art memory systems and that FileGramEngine and FileGramOS are effective' is presented without any quantitative metrics, baselines, error bars, or description of how effectiveness was measured. This absence is load-bearing because the central claims of benchmark difficulty and component effectiveness rest entirely on these unspecified results.
[FileGramEngine and experiments] FileGramEngine and experiments description: All reported results use traces generated by FileGramEngine itself to define personas and workflows. No external validation against real user file-system logs is provided, nor is there a quantitative assessment of how well the simulated multimodal patterns (atomic actions, content deltas) match authentic behavioral distributions. This directly undermines the claim that FileGramOS's procedural/semantic/episodic channels demonstrate real utility and that the benchmark tasks are meaningfully challenging beyond simulator-internal consistency.
[FileGramBench] FileGramBench task definitions: The benchmark tasks (profile reconstruction, trace disentanglement, persona drift) are defined solely in terms of the synthetic personas and traces produced by FileGramEngine. Without an independent check on whether these tasks reflect real-world file-system usage distributions, it is unclear whether superior performance on FileGramBench would translate to improved personalization in deployed agents.

minor comments (2)

[Abstract and Introduction] The abstract and introduction would benefit from a brief explicit statement of the privacy-related data constraints that motivate the simulation approach, including any references to prior work on real file-system trace collection.
[FileGramOS] Notation for the three memory channels (procedural, semantic, episodic) should be introduced with a clear diagram or pseudocode in the FileGramOS section to clarify how atomic actions are mapped at query time.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract, the simulation-based evaluation, and the synthetic benchmark design. We address each major comment below with the strongest honest defense possible, noting where the manuscript will be revised for clarity and completeness.

read point-by-point responses

Referee: [Abstract] Abstract: The assertion that 'extensive experiments show that FileGramBench remains challenging for state-of-the-art memory systems and that FileGramEngine and FileGramOS are effective' is presented without any quantitative metrics, baselines, error bars, or description of how effectiveness was measured. This absence is load-bearing because the central claims of benchmark difficulty and component effectiveness rest entirely on these unspecified results.

Authors: The abstract is intentionally concise and summarizes the key findings at a high level, as is standard. The full manuscript provides the requested quantitative details, including specific metrics, baselines, and error bars, in the Experiments section. We will revise the abstract to incorporate a brief summary of the main quantitative results (e.g., performance deltas on benchmark tasks) to better support the claims without exceeding length constraints. revision: yes
Referee: [FileGramEngine and experiments] FileGramEngine and experiments description: All reported results use traces generated by FileGramEngine itself to define personas and workflows. No external validation against real user file-system logs is provided, nor is there a quantitative assessment of how well the simulated multimodal patterns (atomic actions, content deltas) match authentic behavioral distributions. This directly undermines the claim that FileGramOS's procedural/semantic/episodic channels demonstrate real utility and that the benchmark tasks are meaningfully challenging beyond simulator-internal consistency.

Authors: The exclusive use of simulated traces is a core design decision motivated by privacy regulations that prohibit collection and release of real user file-system logs, as stated in the Introduction. FileGramEngine generates traces from explicit persona and workflow specifications to ensure controllability and scalability. While direct quantitative fidelity metrics against real distributions are not feasible without violating privacy, the simulator incorporates patterns drawn from published studies on file-system behavior. We will add a new subsection detailing the simulator's grounding in prior empirical observations and explicitly discuss this as a limitation, including plans for future indirect validation methods. revision: partial
Referee: [FileGramBench] FileGramBench task definitions: The benchmark tasks (profile reconstruction, trace disentanglement, persona drift) are defined solely in terms of the synthetic personas and traces produced by FileGramEngine. Without an independent check on whether these tasks reflect real-world file-system usage distributions, it is unclear whether superior performance on FileGramBench would translate to improved personalization in deployed agents.

Authors: FileGramBench is explicitly positioned as a diagnostic, controlled benchmark to enable precise, reproducible evaluation of memory capabilities that lack ground truth in real deployments. The synthetic construction allows isolation of factors such as persona drift and multimodal grounding. We recognize the translation gap to real-world settings and will expand the Discussion section to address how benchmark results can guide agent design, while noting that real-world transfer remains an open question for future work involving consented user studies. revision: partial

Circularity Check

0 steps flagged

No circularity: framework components are modular proposals evaluated empirically on generated data without self-referential derivations.

full rationale

The paper introduces FileGramEngine as a simulator, FileGramBench as a diagnostic benchmark, and FileGramOS as a memory architecture. Effectiveness is claimed via 'extensive experiments' on synthetic traces, but no equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the abstract or described structure. The derivation chain consists of independent component definitions followed by external-style empirical testing rather than any reduction by construction. This matches the default expectation of no significant circularity for framework proposals.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 3 invented entities

The central claims rest on the assumption that file-system traces are sufficient and privacy-friendly for personalization, plus the design choices for the three invented components; no numerical free parameters are mentioned.

axioms (2)

domain assumption File-system behavioral traces contain sufficient multimodal information to reconstruct user profiles and detect persona drift.
Invoked in the motivation and in the design of FileGramBench and FileGramOS.
domain assumption Simulated workflows from FileGramEngine produce traces that generalize to real users.
Required for the claim that the engine addresses data constraints.

invented entities (3)

FileGramEngine no independent evidence
purpose: Scalable persona-driven simulator of file-system workflows and multimodal action sequences.
New data generation component introduced to overcome privacy barriers.
FileGramBench no independent evidence
purpose: Diagnostic benchmark for memory systems on profile reconstruction, trace disentanglement, persona drift, and multimodal grounding.
New evaluation suite grounded in file-system traces.
FileGramOS no independent evidence
purpose: Bottom-up memory architecture encoding atomic actions into procedural, semantic, and episodic channels.
New memory system that builds profiles from file deltas rather than dialogue.

pith-pipeline@v0.9.0 · 5555 in / 1563 out tokens · 56412 ms · 2026-05-10T19:29:57.519349+00:00 · methodology

discussion (0)

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FileGramOS... encodes these traces into procedural, semantic, and episodic channels with query-time abstraction

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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Reference graph

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While effective for conversational recall, they lack procedural modeling and cannot ingest non-textual behavioral evidence

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organizes user knowledge through an ontology-driven tagging scheme, mapping each interaction to a domain taxonomy before storage; this top-down design contrasts withFileGramOS’s bottom-up approach, where behavioral dimensions emerge from trace statistics rather than a pre-defined ontology. O-Mem (Wang et al., 2025a) introduces a multi-store persona memory...

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2024