Quick on the Uptake: Eliciting Implicit Intents from Human Demonstrations for Personalized Mobile-Use Agents
Pith reviewed 2026-05-18 23:59 UTC · model grok-4.3
The pith
IFRAgent extracts implicit user preferences from demonstrations to build personalized habit repositories for mobile agents.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
By analyzing explicit intention flows from human demonstrations to construct a query-level vector library of standard operating procedures and analyzing implicit intention flows to build a user-level habit repository, IFRAgent leverages a SOP extractor combined with retrieval-augmented generation and a query rewriter to generate personalized query and SOP from a raw ambiguous query, enhancing the alignment between mobile-use agents and human intent.
What carries the argument
Intention Flow Recognition, which separates explicit step sequences from implicit personal preferences to construct both SOP libraries and user habit repositories.
Load-bearing premise
That personal preferences observed in a fixed collection of demonstrations remain stable enough to generalize to new tasks without ongoing user feedback or explicit updates.
What would settle it
Running the trained agents on new demonstrations where users have altered their typical habits and checking whether alignment rates fall back to baseline levels.
read the original abstract
As multimodal large language models advance rapidly, the automation of mobile tasks has become increasingly feasible through the use of mobile-use agents that mimic human interactions from graphical user interface. To further enhance mobile-use agents, previous studies employ demonstration learning to improve mobile-use agents from human demonstrations. However, these methods focus solely on the explicit intention flows of humans (e.g., step sequences) while neglecting implicit intention flows (e.g., personal preferences), which makes it difficult to construct personalized mobile-use agents. In this work, to evaluate the \textbf{I}ntention \textbf{A}lignment \textbf{R}ate between mobile-use agents and humans, we first collect \textbf{MobileIAR}, a dataset containing human-intent-aligned actions and ground-truth actions. This enables a comprehensive assessment of the agents' understanding of human intent. Then we propose \textbf{IFRAgent}, a framework built upon \textbf{I}ntention \textbf{F}low \textbf{R}ecognition from human demonstrations. IFRAgent analyzes explicit intention flows from human demonstrations to construct a query-level vector library of standard operating procedures (SOP), and analyzes implicit intention flows to build a user-level habit repository. IFRAgent then leverages a SOP extractor combined with retrieval-augmented generation and a query rewriter to generate personalized query and SOP from a raw ambiguous query, enhancing the alignment between mobile-use agents and human intent. Experimental results demonstrate that IFRAgent outperforms baselines by an average of 6.79\% (32.06\% relative improvement) in human intention alignment rate and improves step completion rates by an average of 5.30\% (26.34\% relative improvement). The codes are available at https://github.com/MadeAgents/Quick-on-the-Uptake.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces the MobileIAR dataset containing human-intent-aligned actions and ground-truth actions to evaluate intention alignment rate for mobile-use agents. It proposes IFRAgent, which extracts explicit intention flows from demonstrations to build a query-level SOP vector library and implicit intention flows to build a user-level habit repository; these are then used via SOP extraction, retrieval-augmented generation, and query rewriting to produce personalized outputs from ambiguous queries. The central claim is that this yields average gains of 6.79% (32.06% relative) in human intention alignment rate and 5.30% (26.34% relative) in step completion rate over baselines.
Significance. If the reported gains are shown to arise from stable, generalizable user habits rather than demonstration-specific artifacts, the work would meaningfully advance personalized mobile agents by addressing the gap in implicit intent modeling. The release of the MobileIAR dataset and associated code at the cited GitHub repository are concrete strengths that support reproducibility and further research.
major comments (2)
- [Abstract] Abstract: The reported 6.79% alignment-rate improvement and 5.30% step-completion improvement rest on the claim that implicit intention flows extracted from MobileIAR demonstrations produce a user-level habit repository that generalizes to new queries. No information is provided on the number of users, demonstrations per user, task diversity, or any cross-task or held-out-query evaluation that would distinguish stable personal preferences from session-specific patterns.
- [Experimental results] Experimental results (as summarized): The evaluation of intention alignment rate and step completion rate does not report error bars, statistical significance, or ablations isolating the contribution of the implicit habit repository versus the explicit SOP library, making it impossible to verify that the gains are load-bearing for the personalization claim rather than artifacts of the data-collection or retrieval setup.
minor comments (1)
- [Abstract] Abstract: The precise definitions of the SOP extractor and query rewriter, and how they interact with the habit repository during inference, are stated at a high level; a forward reference to the corresponding methods subsection would improve clarity.
Simulated Author's Rebuttal
We thank the referee for the constructive feedback. The comments identify key areas where additional details and analyses would strengthen the presentation of our dataset and results. We address each major comment below and outline the revisions we will make.
read point-by-point responses
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Referee: [Abstract] Abstract: The reported 6.79% alignment-rate improvement and 5.30% step-completion improvement rest on the claim that implicit intention flows extracted from MobileIAR demonstrations produce a user-level habit repository that generalizes to new queries. No information is provided on the number of users, demonstrations per user, task diversity, or any cross-task or held-out-query evaluation that would distinguish stable personal preferences from session-specific patterns.
Authors: We agree that the abstract does not include these dataset and evaluation details due to length constraints. The full manuscript describes the MobileIAR collection process and evaluation protocol in the experimental section, including user counts, demonstrations, and held-out query splits to assess habit generalization. To directly address the concern, we will revise the abstract to summarize the number of users, demonstrations per user, task diversity, and the held-out query evaluation used to support generalization of the user-level habit repository. revision: yes
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Referee: [Experimental results] Experimental results (as summarized): The evaluation of intention alignment rate and step completion rate does not report error bars, statistical significance, or ablations isolating the contribution of the implicit habit repository versus the explicit SOP library, making it impossible to verify that the gains are load-bearing for the personalization claim rather than artifacts of the data-collection or retrieval setup.
Authors: We acknowledge that the current results presentation lacks error bars, significance testing, and targeted ablations. We will add error bars and statistical significance tests to the reported metrics. We will also include new ablation experiments that isolate the contribution of the implicit habit repository (from implicit flows) versus the explicit SOP library to demonstrate that the observed gains are attributable to the personalization components rather than other factors. revision: yes
Circularity Check
No significant circularity; derivation is self-contained
full rationale
The paper collects a new MobileIAR dataset of human-intent-aligned actions and proposes IFRAgent, which separately analyzes explicit intention flows to build a query-level SOP vector library and implicit flows to build a user-level habit repository. These components feed into an SOP extractor, RAG, and query rewriter to produce personalized outputs from raw queries. The evaluation metrics (intention alignment rate and step completion rate) are defined externally to these internal constructions and measured on held-out demonstrations. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text that would reduce the reported gains to definitional equivalence or input fitting. The framework therefore rests on independent empirical measurement rather than circular reduction.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Human demonstrations contain stable implicit intention flows that can be automatically separated from explicit step sequences.
Forward citations
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discussion (0)
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