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REVIEW 1 major objections 1 minor 77 references

PageGuide browser extension uses visual overlays to ground LLM answers on web page elements, raising accuracy and cutting time in user tests for find, guide, and hide tasks.

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2026-07-01 09:06 UTC pith:BYTMOF6F

load-bearing objection PageGuide gives a concrete browser extension for visually grounding LLM answers on the page via DOM overlays, but the N=94 study gains cannot be assessed without any methodology details. the 1 major comments →

arxiv 2604.23772 v3 pith:BYTMOF6F submitted 2026-04-26 cs.HC

PageGuide: Browser extension to assist users in navigating a webpage and locating information

Tin Nguyen , Thang T. Truong , Runtao Zhou , Trung Bui , Chirag Agarwal , Anh Totti Nguyen This is my paper
classification cs.HC
keywords browser extensionweb navigationLLM groundingvisual overlaysuser studyinformation findingtask guidancecontent hiding
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper introduces PageGuide, a browser extension that connects AI-generated answers directly to the source content on a webpage through visual overlays instead of leaving users to search or trust results blindly. It tackles three needs with specific modes: Find highlights relevant evidence in place for quick verification, Guide delivers one instruction at a time so users can follow along themselves, and Hide lets people remove distracting sections after deciding. In a study with 94 participants, these features produced measurable gains over regular browsing, such as higher success rates and shorter completion times across the modes. The approach keeps the user in the loop while leveraging AI, addressing the common problem of cluttered pages and ungrounded assistance.

Core claim

PageGuide is a browser extension that grounds LLM answers directly in the HTML DOM via visual overlays. It supports three modes: Find for locating and highlighting relevant evidence in-situ, Guide for presenting step-by-step instructions one at a time, and Hide for allowing users to decide whether to remove distracting elements. In a user study with 94 participants, PageGuide outperformed unaided browsing with Hide accuracy improving by 26 percentage points and task time dropping by 70 percent, Guide completion rate increasing by 30 percentage points, and Find reducing Ctrl+F usage by 80 percent along with 19 percent less task time.

What carries the argument

Visual overlays that map LLM outputs to specific HTML DOM elements for in-place highlighting, guidance, and hiding.

Load-bearing premise

The performance gains measured in the N=94 user study are caused by the PageGuide features rather than by task selection, participant assignment, or other aspects of the study design.

What would settle it

A follow-up study using different tasks, a larger or more diverse participant pool, and tighter controls that finds no significant difference in accuracy or time between PageGuide and unaided browsing would show the reported benefits do not hold.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Users can verify AI answers on the actual page without separate manual searches.
  • Step-by-step guidance enables users to complete multi-step tasks themselves rather than relying on full automation.
  • Selective hiding of content improves focus and accuracy when locating information on cluttered pages.
  • Reduced use of shortcuts like Ctrl+F indicates lower manual search effort across tasks.

Where Pith is reading between the lines

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

  • The grounding method could be adapted to non-browser interfaces such as document viewers or mobile apps to provide similar source-linked assistance.
  • Repeated interactions might let the system learn and suggest personalized hiding rules based on past user choices.
  • Pairing the overlay approach with more advanced browser agents could create systems that offer guidance alongside partial automation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 1 minor

Summary. The paper presents PageGuide, a browser extension that grounds LLM outputs via visual DOM overlays to support three modes: Find (locating and highlighting evidence in-situ), Guide (step-by-step instructions for multi-step tasks), and Hide (selectively hiding distracting content). It claims that in a user study (N=94), PageGuide outperforms unaided browsing with a 26 percentage point accuracy gain (86.7% relative) and 70% time reduction in Hide, a 30 percentage point completion rate increase in Guide, and an 80% drop in Ctrl+F usage plus 19% time reduction in Find.

Significance. If the empirical claims hold after methodological details are supplied, the work would be significant for HCI by showing how visual grounding can address verification and trust problems with ungrounded LLM assistants and browser agents. The public release of code and demo at pageguide.github.io is a clear strength for reproducibility and extension by others.

major comments (1)
  1. [Abstract] Abstract (user study paragraph): The headline quantitative claims (Hide +26pp accuracy, Guide +30pp completion, Find -80% Ctrl+F) rest entirely on the N=94 study, yet the manuscript supplies no information on design (between- vs. within-subjects, counterbalancing, task selection criteria), controls (prior familiarity, blinding), or analysis (statistical tests, effect sizes, confidence intervals). This is load-bearing for the central claim that gains are caused by the visual-grounding, step-by-step, and hiding mechanisms rather than confounds.
minor comments (1)
  1. [Abstract] Abstract: grammatical and agreement errors ('PageGuide outperform', 'Hide accuracy improve', 'Code and demo is at') should be corrected for clarity.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting the need for greater methodological transparency in the user study. We agree that the reported performance gains require detailed justification of the experimental design to rule out confounds, and we will revise the manuscript to supply this information.

read point-by-point responses

  1. Referee: [Abstract] Abstract (user study paragraph): The headline quantitative claims (Hide +26pp accuracy, Guide +30pp completion, Find -80% Ctrl+F) rest entirely on the N=94 study, yet the manuscript supplies no information on design (between- vs. within-subjects, counterbalancing, task selection criteria), controls (prior familiarity, blinding), or analysis (statistical tests, effect sizes, confidence intervals). This is load-bearing for the central claim that gains are caused by the visual-grounding, step-by-step, and hiding mechanisms rather than confounds.

    Authors: We acknowledge that the manuscript currently provides only high-level results without the requested methodological details on study design, controls, or analysis. This omission weakens the ability to attribute the gains specifically to PageGuide's mechanisms. In the revision we will expand the User Study section with a full description of the experimental protocol, including design type and counterbalancing, task selection criteria, controls for prior familiarity and blinding, and the statistical tests, effect sizes, and confidence intervals used. These additions will directly address the concern that the claims rest on unverified assumptions. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical user study with no derivations or self-referential claims

full rationale

The paper describes a browser extension (PageGuide) and reports results from an N=94 user study comparing it to unaided browsing. No equations, fitted parameters, predictions derived from inputs, or self-citations appear in the abstract or described claims. The central performance deltas (e.g., +26pp accuracy, -70% time) are presented as direct empirical outcomes rather than reductions of any prior result by construction. This matches the default case of a self-contained empirical system paper with no load-bearing derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an applied HCI systems paper. No free parameters, mathematical axioms, or invented entities are involved; claims rest on the unreviewed user study whose design details are absent from the abstract.

pith-pipeline@v0.9.1-grok · 5808 in / 1080 out tokens · 25503 ms · 2026-07-01T09:06:24.917968+00:00 · methodology

0 comments
read the original abstract

Users browsing the web daily struggle to quickly locate relevant information in cluttered pages, complete unfamiliar multi-step tasks, and stay focused amid distracting content. State-of-the-art AI assistants (e.g., ChatGPT, Gemini, Claude) and browser agents (e.g., OpenAI Operator, Browser Use) can answer questions and automate actions, yet they return answers without showing where the information comes from on the page, forcing users to manually verify results and blindly trust every automated steps. We present PageGuide, a browser extension that grounds LLM answers directly in the HTML DOM via visual overlays, addressing three core user needs: (a) Find-locating and highlighting relevant evidence in-situ so users can instantly verify answers on the page; (b) Guide-showing step-by-step instructions (e.g. how to change password) one at a time so users can follow and perform actions by themselves; and (c) Hide-hiding distracting content-giving users a chance to decide to hide an element or not. In a user study (N=94), PageGuide outperform unaided browsing across all modes: Hide accuracy improve by 26 percentage points (86.7% relative gain) and task completion time drops by 70%; Guide completion rate increases by 30 percentage points; and Find reduces manual search effort, with Ctrl+F usage falling by 80% and task time decreasing by 19%. Code and demo is at: pageguide.github.io.

Figures

Figures reproduced from arXiv: 2604.23772 by Anh Totti Nguyen, Chirag Agarwal, Runtao Zhou, Thang T. Truong, Tin Nguyen, Trung Bui.

Figure 1
Figure 1. Figure 1: (a) Existing web agents can fail in three ways: by providing answers that cannot be verified from the page, failing view at source ↗
Figure 2
Figure 2. Figure 2: Given the query “How to add ABC to this GitHub project?”, PageGuide (powered by Gemini-3-Flash ) generates a step-by-step plan and delivers it one step at a time. The target UI element is highlighted directly on the page (e.g., Settings , Collaborators )), while the sidebar panel shows the current instruction, the outcome hint (), and Next / Stop controls. The user always drives the pace: each step only a… view at source ↗
Figure 3
Figure 3. Figure 3: On social platforms such as X.com, users often encounter repetitive or distracting content. Given the query view at source ↗
Figure 4
Figure 4. Figure 4: Given a user query, the Router assigns it to one of view at source ↗
Figure 5
Figure 5. Figure 5: Task performance comparing control and extension conditions across all three features. view at source ↗
Figure 6
Figure 6. Figure 6: Task completion time (seconds) for the control and extension conditions, restricted to correctly completed tasks. view at source ↗
Figure 7
Figure 7. Figure 7: Behavioral signals (mean ± SE) comparing the control and extension conditions. Each bar shows the average count or distance per task for five metrics: Ctrl+F presses, text selections, mouse clicks, scroll count, and mouse movement distance. All five metrics decrease substantially with PageGuide, indicating that users rely less on manual search and perform fewer interactions to complete the same tasks. unai… view at source ↗
Figure 12
Figure 12. Figure 12: Using these responses, we address RQ3: do users per￾ceive completing tasks with PageGuide as easier and less effortful compared to completing the same tasks without it? RQ3) Users rate PageGuide as easier to use and effective in helping them locate answers. Participants’ subjective ratings view at source ↗
Figure 8
Figure 8. Figure 8: Behavioral metrics (mean ± SE) broken down by task type (Find, Guide, Hide) and condition. While Find and Hide show consistent reductions across all signals, Guide shows a different pattern: page visits and mouse movement distance increase with PageGuide, reflecting that the extension actively guides users to navigate to new pages as part of the task. F1: PageGuide can correctly locate the information need… view at source ↗
Figure 9
Figure 9. Figure 9: Post-study Likert ratings (1 = Strongly Disagree, 7 = Strongly Agree) for each interaction mode. Each mode includes view at source ↗
Figure 10
Figure 10. Figure 10: Task outcome distributions for Guide and Hide tasks, based on participants’ response to “Did you com￾plete the task?” after each trial (completed / partial / failed), shown as the percentage of participants per category. With PageGuide, completed outcomes increase and failures de￾crease in both features. Notably, Guide shows a rise in partial completions under the extension condition, indicating that user… view at source ↗
Figure 11
Figure 11. Figure 11: Task interfaces displayed to participants during the user study, covering each of view at source ↗
Figure 12
Figure 12. Figure 12: Post-Study Questionnaire view at source ↗
Figure 13
Figure 13. Figure 13: Routing prompt used to classify user queries into appropriate handlers ( view at source ↗
Figure 14
Figure 14. Figure 14: Prompt used to answer user queries grounded in webpage content, producing responses with interleaved references view at source ↗
Figure 15
Figure 15. Figure 15: Hide mode prompt. Given the page element index and the user’s natural language description of content to suppress, the LLM returns a ranked list of up to 15 matching element indices, each paired with a one-sentence hiding reason. The result is used to apply display:none to the identified elements and populate the inline placeholder shown to the user view at source ↗
Figure 16
Figure 16. Figure 16: Guide mode prompt. The LLM receives the current page element index, the user query, the current step number, and any previously completed steps. It returns one action at a time as a JSON object specifying the instruction text, the SoM index and text of the target element, the action type (click, input, scroll), and a next-step hint view at source ↗
Figure 17
Figure 17. Figure 17: Data generation pipelines for the three subsets of PageGuide. (A) The Find pipeline collects webpages, filters for view at source ↗
Figure 18
Figure 18. Figure 18: Users can upload a PDF and ask questions about its contents through the PDF upload feature. In this example, the view at source ↗
Figure 19
Figure 19. Figure 19: PageGuide allows users to upload an image and ask questions about it, while grounding its textual response in evidence from the webpage and specific areas of the user’s image. In this example, the user asks whether the tiger in the uploaded image is the same species as the tiger described in the webpage, and the agent responds with evidence-backed information. Note: The yellow bounding box highlighting th… view at source ↗
Figure 20
Figure 20. Figure 20: With the Page Off feature enabled, users can ask questions that are not related to the currently active tab. The view at source ↗
Figure 21
Figure 21. Figure 21: Example of Gemini Chatbot on the query “How many female casts in Stranger Things?”. The chatbot provides an answer with partial references to text spans, such as “Stranger Things,” but does not link evidence to individual cast names, making verification difficult. In addition, the relevant evidence is embedded within dense text sections, and the highlights include unrelated information, such as male cast … view at source ↗
Figure 22
Figure 22. Figure 22: Example of Browser Use on https://en.wikipedia.org/wiki/Stranger_Things. The agent generates a response that view at source ↗
Figure 23
Figure 23. Figure 23: Example of MolmoWeb [14] on https://en.wikipedia.org/wiki/Elon_Musk. The agent generates a response that correctly answers the query is unable to highlight corresponding evidence, such as through bounding boxes, which makes it difficult for the users to verify the response view at source ↗
Figure 24
Figure 24. Figure 24: Example of Gemini Agent on https://us.megabus.com/. The agent generates a response that correctly answers the view at source ↗
Figure 25
Figure 25. Figure 25: Given the query “How to find the time frame for finding a lost item?” on https://us.megabus.com/, PageGuide (powered by Gemini-3-Flash ) not only generates a step-by-step plan but also highlights the relevant text spans on the webpage, making it easier for users to locate and verify the information view at source ↗

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    "guide" - For step-by-step "how to" questions that need interactive guidance

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    "hide" - For requests to hide, remove, or suppress distracting/annoying content (ads, banners, popups, cookie notices, sidebars, recommendations, etc.)

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    image_find

    "image_find" - For questions about an UPLOADED IMAGE (finding similar items, comparing with page content)

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    pdf_find

    "pdf_find" - For questions about PDF documents (summarize, find specific content, extract info from PDFs)

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    find" - For questions, information lookup, finding content, highlighting elements (DEFAULT) ROUTING RULES: -

    "find" - For questions, information lookup, finding content, highlighting elements (DEFAULT) ROUTING RULES: - "guide": User wants to LEARN how to do something in steps (e.g.,"how do I report this video?", "where can I find settings?", "help me delete my account") - "hide": User wants to hide or remove something on the page (e.g.,"hide the ads", "remove th...

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    Answer the question based on the page content if possible

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    text"] citations inline to reference specific elements from the PAGE INDEX - N is the index number from PAGE INDEX -

    If the page content has the answer, use [N:"text"] citations inline to reference specific elements from the PAGE INDEX - N is the index number from PAGE INDEX - "text" is the EXACT text snippet to highlight (copy from the page content)

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    The "text" should be a short, specific phrase (not the entire element text)

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    Consider conversation history for context, but always answer based on CURRENT page content

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    Wikipedia’s [1], [2], [3]) — only use [N:"text"] format where N comes from the PAGE INDEX above

    NEVER reproduce existing footnote markers from the webpage itself (e.g. Wikipedia’s [1], [2], [3]) — only use [N:"text"] format where N comes from the PAGE INDEX above

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    The information is not provided on this page

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    PAGE INDEX - Visible elements on the page

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    USER QUESTION - What the user wants to do

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    STEP NUMBER - Current step (1 = first step)

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    " or

    PREVIOUS STEPS - What was done before (if any) Your job: Guide the user ONE STEP at a time. IMPORTANT CONCEPTS: - Some buttons/options are HIDDEN in menus (like "... " or " ..." three-dot menus) - If the target isn’t visible, guide user to open the menu FIRST - Common hidden locations: dropdown menus, "More" buttons, three-dot menus, right-click menus, se...

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    ONE step at a time - don’t overwhelm the user

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    If target is likely hidden in a menu, first step should open that menu

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    waitFor":

    Use "waitFor": "click" when user needs to click something

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    isLastStep

    Set "isLastStep": true only when the goal is achieved

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    Make instructions clear and specific

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    Highlight the element user needs to interact with EXAMPLES: PAGE INDEX:

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    How do I report this video?

    (button) Save Q: "How do I report this video?" (Step 1) → { "step":1, "instruction":"Click the three-dot menu ( ...) to see more options", "highlight":"index":5, "text":" ...", "wait- For":"click", "isLastStep":false, "nextStepHint":"The menu will open with Report option" } Q: "How do I report this video?" (Step 2, after menu opened) PAGE INDEX now shows:...

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