arxiv: 2605.04227 · v1 · submitted 2026-05-05 · 💻 cs.AI · cs.HC

Recognition: unknown

Pro²Assist: Continuous Step-Aware Proactive Assistance with Multimodal Egocentric Perception for Long-Horizon Procedural Tasks

Bufang Yang, Hongkai Chen, Kaiwei Liu, Kaiyuan Hou, Lilin Xu, Siyang Jiang, Xiaofan Jiang, Yuang Fan, Zhenyu Yan

Authors on Pith no claims yet

Pith reviewed 2026-05-08 17:11 UTC · model grok-4.3

classification 💻 cs.AI cs.HC

keywords proactive assistanceprocedural tasksAR glassesegocentric perceptionstep-aware reasoningmultimodal datalong-horizon taskscontinuous reasoning

0 comments

The pith

Pro²Assist uses continuous multimodal reasoning from AR glasses to deliver timely proactive assistance throughout long-horizon procedural tasks.

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

The paper presents a system that moves beyond query-triggered or short-term reactive help by tracking a user's progress through ordered multi-step activities in real time. It combines raw motion data from wearable glasses with structured knowledge of task sequences to infer what assistance is needed next and when to provide it. A sympathetic reader would care because everyday activities such as cooking, assembly, or maintenance often fail due to missed timing or forgotten steps rather than lack of information. If the approach holds, personal AI assistants could shift from passive responders to active companions that stay synchronized with extended real-world workflows.

Core claim

Pro²Assist is a step-aware proactive assistant that continuously tracks fine-grained task progress and reasons over the user's evolving state to provide timely assistance throughout tasks. It leverages multimodal data from augmented reality glasses for motion-based perception, extracts step-oriented procedural context from multi-scale temporal dynamics and task-specific expert knowledge, and performs continuous reasoning to infer user needs and display timely assistance on the glasses themselves.

What carries the argument

The continuous reasoning process that fuses motion-based egocentric perception from AR glasses with step-oriented procedural context drawn from multi-scale temporal dynamics and task-specific expert knowledge.

If this is right

The system outperforms the strongest prior baselines by more than 21 percent in procedural action understanding accuracy.
It reaches up to 2.29 times the proactive timing accuracy of existing methods.
A user study with 20 participants indicates that 90 percent find the assistance useful in practice.
The approach supports continuous help across entire long-horizon sequences rather than isolated short events.

Where Pith is reading between the lines

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

The same continuous-reasoning pattern could be adapted to non-glasses wearables if equivalent motion and context streams are available.
Extending the context extraction step to new task domains would require only updated expert knowledge rather than retraining the entire perception pipeline.
If timing accuracy gains hold, the method could reduce user errors in safety-critical procedures by surfacing reminders before mistakes occur.

Load-bearing premise

Multimodal data from AR glasses together with extracted step-oriented procedural context can reliably and continuously infer the user's evolving state and needs across diverse real-world long-horizon tasks.

What would settle it

A side-by-side trial in which participants complete varied long procedural tasks while wearing the glasses, with independent observers measuring whether the system's assistance appears at moments that actually match the user's demonstrated needs and whether action-understanding accuracy exceeds the best baseline by the reported margin.

Figures

Figures reproduced from arXiv: 2605.04227 by Bufang Yang, Hongkai Chen, Kaiwei Liu, Kaiyuan Hou, Lilin Xu, Siyang Jiang, Xiaofan Jiang, Yuang Fan, Zhenyu Yan.

**Figure 1.** Figure 1: Application scenario of Pro2Assist. Pro2Assist provides continuous, step-aware proactive assistance by continuously reasoning over the user’s evolving state in the task workflow (left), while reactive procedural assistants rely on explicit user requests and one-shot proactive assistants provide isolated assistance for the overall event (right). proactive systems [46, 74, 75] that aim to further reduce user… view at source ↗

**Figure 3.** Figure 3: A preliminary example that head motion can effectively indicate view at source ↗

**Figure 4.** Figure 4: Egocentric vision and hand motion provide important cues for inferring user intent during procedural tasks. view at source ↗

**Figure 5.** Figure 5: Examples illustrating two characteristics in action understanding for procedural tasks: visually similar but functionally view at source ↗

**Figure 6.** Figure 6: Performance of existing VLMs adapted for procedural view at source ↗

**Figure 8.** Figure 8: System overview of Pro2Assist. Pro2Assist utilizes multimodal egocentric data from AR glasses to achieve motion-based perception. By integrating visual inputs with extracted task-specific expert knowledge and multi-scale temporal context, the reasoner performs step-aware proactive reasoning with consistency checking. The resulting proactive assistance is then delivered to the user via on-screen displays on… view at source ↗

**Figure 9.** Figure 9: Optical flow computation time and area ratio distri view at source ↗

**Figure 11.** Figure 11: Impact of incorporating short-term hand motion cues and their impact. <Thoughts> The tea bag is visibly in the mug, and the mug is on the counter with no active pouring or stirring. The user is likely about to steep the tea, and this is the beginning of the steeping phase. START Measure cold water Steep tea bag in mug Throw tea bag into trash can Add honey to tea in mug Stir tea in mug using spoon End The… view at source ↗

**Figure 13.** Figure 13: Training pipeline of the step-aware proactive reasoner in Pro view at source ↗

**Figure 14.** Figure 14: System prompt for the step-aware proactive rea view at source ↗

**Figure 15.** Figure 15: Our dataset curation pipeline combining human annotation and LLM-assisted generation. view at source ↗

**Figure 16.** Figure 16: A sample from the curated dataset, consisting of an egocentric frame and its corresponding annotations. Glass Display Camera/Mic Processors & Battery AR Glasses view at source ↗

**Figure 18.** Figure 18: End-to-end performance comparison in real-world evaluation. Missing bars indicate that the corresponding metric is view at source ↗

**Figure 19.** Figure 19: Overall performance comparison on the curated dataset. Missing bars indicate that the corresponding metric is not view at source ↗

**Figure 20.** Figure 20: Performance comparison of inference triggering and proactive prediction. Solid orange lines indicate VLM inference view at source ↗

**Figure 21.** Figure 21: Examples of Pro2Assist’s inference results in real-world evaluation. recording, demonstrating that Pro2Assist explicitly reasons over sensory and procedural contexts to achieve reliable action understanding. When no assistance is needed, Pro2Assist remains silent to avoid interrupting the user. Otherwise, it generates step-aware assistance based on the user’s current state and expert knowledge to effectiv… view at source ↗

**Figure 22.** Figure 22: Impact of motion-based perception on inference ratio and proactive hit rate. 0 20 40 60 80 Step-Acc (%) 0 20 40 60 Status-Acc (%) 0 20 40 60 80 Acc-P (%) 0 20 40 60 80 STS (%) Ours w/o Motion Extraction w/o Consistency Checking view at source ↗

**Figure 25.** Figure 25: Impact of step-aware consistency checking on avoid view at source ↗

**Figure 26.** Figure 26: Comparison of different base VLMs used in view at source ↗

**Figure 28.** Figure 28: Impact of threshold settings in motion-based perception. 4 5 6 7 8 70 80 90 Step-Acc w/o Checking 4 5 6 7 8 65 70 Status-Acc w/o Checking 4 5 6 7 8 Window Length 75 80 Acc-P w/o Checking 4 5 6 7 8 Window Length 60 80 TUS w/o Checking view at source ↗

**Figure 31.** Figure 31: Results of the overall user perception for Pro2Assist. Pro2Assist to begin delivering assistance promptly via streaming output, even on resource-constrained edge devices. Expert knowledge retrieval takes under 0.22 s across all devices, and the average communication latency in two real-world evaluation environments is 327.3 ms. In addition, we measure the average power consumption of the smart glasses whi… view at source ↗

**Figure 32.** Figure 32: Comparison of baselines and Pro2Assist using a 7-point Likert scale, ranging from 1 (Very low) to 7 (Very high). For Intrusiveness, lower is better. “ref.” marks reference, and ∗∗∗𝑝 < 0.001 (paired Wilcoxon signed-rank test). CoT, and ProAgent (FT)) and rated each system on a 7-point Likert scale across the five evaluation dimensions. For each participant, all four methods were applied to the same video t… view at source ↗

read the original abstract

Procedural tasks with multiple ordered steps are ubiquitous in daily life. Recent advances in multimodal large language models (MLLMs) have enabled personal assistants that support daily activities. However, existing systems primarily provide reactive guidance triggered by user queries, or limited proactive assistance for isolated short-term events rather than long-horizon procedural tasks. In this work, we introduce Pro$^2$Assist, a step-aware proactive assistant that continuously tracks fine-grained task progress and reasons over the user's evolving state to provide timely assistance throughout tasks. Pro$^2$Assist leverages multimodal data from augmented reality (AR) glasses to achieve motion-based perception. It then extracts step-oriented procedural context from multi-scale temporal dynamics and task-specific expert knowledge. Based on both sensory input and procedural context, Pro$^2$Assist performs continuous reasoning to infer user needs and display timely assistance on AR glasses. We evaluate Pro$^2$Assist using a dataset curated from public sources and a real-world dataset collected on our testbed with AR glasses. Extensive evaluations show that Pro$^2$Assist outperforms the best-performing baselines by over 21% in procedural action understanding accuracy, and it achieves up to 2.29x the proactive timing accuracy of baselines. A user study with 20 participants further shows that 90% find Pro$^2$Assist useful, indicating its effectiveness for real-world procedural assistance.

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

Pro²Assist gets measurable gains on action tracking and timing but the gains may depend on task-specific expert knowledge that is not shown to generalize to unseen procedures.

read the letter

The main thing to know is that this system improves on baselines for understanding steps in long procedural tasks and for giving assistance at the right time, but those numbers rest on a pipeline that mixes AR perception with step-oriented context that includes task-specific expert knowledge. If that knowledge is supplied or tuned per task family, the reported lifts could be narrower than they first appear. The abstract claims over 21% better procedural action understanding accuracy and up to 2.29 times the proactive timing accuracy, plus a user study where 90% of 20 participants found it useful. Those are concrete numbers worth checking against the full evaluations. What the paper does well is lay out a continuous reasoning loop that takes egocentric video from AR glasses, extracts multi-scale temporal dynamics, folds in the expert context, and decides when to display help. That moves past simple query-triggered responses or short-event proactivity, and the mix of public datasets with their own real-world AR testbed collection is a practical choice for a systems paper. The soft spot is exactly the one the stress-test note flags. The abstract does not spell out whether the expert knowledge is manually provided, how it is extracted at scale, or whether the same pipeline works on procedures outside the training distribution. Without that, it is hard to tell if the system truly infers evolving user state across diverse long-horizon tasks or whether the results are helped by in-distribution knowledge. The user study is small but positive, and there is no sign of circular reasoning in the claims. This work is for HCI and wearable AI researchers who want ideas on step-aware multimodal assistance. Readers building practical prototypes will get some usable pieces even if they have to adapt the knowledge component. The thinking is straightforward and the paper engages the reactive-versus-proactive literature without obvious contradictions. It deserves a serious referee because it has quantitative results and a user study on a real application gap. I would send it to peer review and ask the authors to clarify how the expert knowledge is obtained and to add tests on held-out task families.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces Pro²Assist, a system for continuous step-aware proactive assistance in long-horizon procedural tasks. It uses multimodal egocentric perception from AR glasses for motion-based tracking of user actions and progress. The core pipeline extracts step-oriented procedural context from multi-scale temporal dynamics combined with task-specific expert knowledge, then performs continuous reasoning over sensory input and this context to infer evolving user needs and deliver timely assistance displayed on the AR glasses. Evaluations on a dataset curated from public sources plus a real-world testbed dataset show Pro²Assist outperforming best baselines by over 21% in procedural action understanding accuracy and up to 2.29× in proactive timing accuracy; a user study with 20 participants reports 90% finding the system useful.

Significance. If the central claims hold under more rigorous generality testing, the work would be significant for advancing proactive multimodal AI assistants beyond reactive or short-horizon systems, particularly through the integration of AR-based egocentric perception with procedural reasoning. Credit is due for the real-world data collection on an AR testbed, the quantitative gains over baselines, and the inclusion of a user study providing practical validation. The approach addresses a relevant gap in continuous assistance for everyday procedural activities.

major comments (3)

[§3.2] §3.2 (Procedural Context Extraction): The extraction of step-oriented procedural context explicitly incorporates 'task-specific expert knowledge' alongside multi-scale temporal dynamics. No ablation study isolates the contribution of this expert-knowledge component, nor is there evidence (e.g., zero-shot transfer results or description of how the knowledge is obtained) that it generalizes beyond the evaluated task families. This is load-bearing for the headline claims of reliable continuous inference of user state across diverse long-horizon procedures.
[§4.1] §4.1 (Datasets and Evaluation Setup): The real-world testbed dataset is described only at high level ('curated from public sources and real-world testbed'). Missing are concrete details on the number of distinct long-horizon tasks, total procedure instances, diversity metrics, and exactly how task-specific expert knowledge was supplied or tuned for each task. Without these, the reported 21% action-understanding and 2.29× timing gains cannot be assessed for robustness outside the training distribution.
[§4.3] §4.3 (User Study): The claim that 90% of 20 participants found Pro²Assist useful is presented without statistical analysis, task breakdown, or comparison of subjective metrics against the same baselines used in the quantitative experiments. This weakens the supporting evidence for real-world effectiveness.

minor comments (2)

[Abstract] The term 'proactive timing accuracy' is used in the abstract and results but is not formally defined until later; an early definition or equation would improve readability.
[Figures] Figure captions for the system overview and qualitative examples could include more detail on what each panel illustrates (e.g., specific steps or failure modes of baselines).

Simulated Author's Rebuttal

3 responses · 0 unresolved

Thank you for the detailed and constructive feedback on our manuscript. We appreciate the referee's recognition of the significance of our work on proactive assistance for long-horizon tasks. Below, we provide point-by-point responses to the major comments and indicate the revisions we will make to address them.

read point-by-point responses

Referee: [§3.2] §3.2 (Procedural Context Extraction): The extraction of step-oriented procedural context explicitly incorporates 'task-specific expert knowledge' alongside multi-scale temporal dynamics. No ablation study isolates the contribution of this expert-knowledge component, nor is there evidence (e.g., zero-shot transfer results or description of how the knowledge is obtained) that it generalizes beyond the evaluated task families. This is load-bearing for the headline claims of reliable continuous inference of user state across diverse long-horizon procedures.

Authors: We agree that isolating the contribution of the task-specific expert knowledge is important for validating its role in the system. In the revised manuscript, we will include an ablation study that compares the full Pro²Assist pipeline against a variant without the expert knowledge component, reporting the resulting drops in action understanding and timing accuracy. Additionally, we will provide a detailed description of how the expert knowledge is obtained—specifically, by structuring standard procedural instructions from task documentation into a step-oriented format—and demonstrate its application across the evaluated task families. While we do not claim zero-shot generalization to entirely novel task domains outside our testbed, we will include results on held-out procedures within the same families to support the generalization claims within the scope of our evaluation. revision: yes
Referee: [§4.1] §4.1 (Datasets and Evaluation Setup): The real-world testbed dataset is described only at high level ('curated from public sources and real-world testbed'). Missing are concrete details on the number of distinct long-horizon tasks, total procedure instances, diversity metrics, and exactly how task-specific expert knowledge was supplied or tuned for each task. Without these, the reported 21% action-understanding and 2.29× timing gains cannot be assessed for robustness outside the training distribution.

Authors: We acknowledge that more granular dataset statistics are necessary for assessing the robustness of our results. In the revised version, we will expand Section 4.1 to include: the exact number of distinct long-horizon tasks (broken down by category, e.g., cooking, assembly, maintenance), the total number of procedure instances, diversity metrics such as average number of steps per task and variance in task duration, and a precise description of how task-specific expert knowledge was supplied (via manual structuring of public task manuals into our procedural context format) and tuned (using a consistent template across tasks with task-specific step names and dependencies). This will allow readers to better evaluate the generalizability of the reported performance gains. revision: yes
Referee: [§4.3] §4.3 (User Study): The claim that 90% of 20 participants found Pro²Assist useful is presented without statistical analysis, task breakdown, or comparison of subjective metrics against the same baselines used in the quantitative experiments. This weakens the supporting evidence for real-world effectiveness.

Authors: We agree that additional analysis would strengthen the user study section. In the revision, we will add statistical analysis to the 90% usefulness finding, such as confidence intervals or binomial tests for significance. We will also provide a task breakdown showing usefulness ratings per task category and include comparisons of subjective metrics (e.g., perceived timeliness and helpfulness) against the baseline systems from the quantitative experiments. These additions will be based on the existing participant responses and will be presented in a new table or figure. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on empirical evaluation

full rationale

The paper presents a system architecture for proactive assistance using AR glasses, multimodal perception, and procedural context extraction, then reports performance gains via comparisons to baselines on public and real-world datasets plus a user study. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text that reduce any central claim to its own inputs by construction. The evaluation methodology is described as independent of the system outputs being measured.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract does not describe any mathematical models, free parameters, or new invented entities. The system is built upon existing multimodal large language models and AR technology.

pith-pipeline@v0.9.0 · 5579 in / 1202 out tokens · 79491 ms · 2026-05-08T17:11:53.449969+00:00 · methodology

discussion (0)

Reference graph

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