{"id":"a50010c4-b092-47b1-83cd-be3eca8e5c67","arxiv_id":"2606.09848","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Presents a mid-level framework with coordination zones, input taxonomy, and coordination curves for human-AI interactions based on salience, involvement, and activity dimensions.","lead":"This paper introduces a framework for human-AI coordination in agentic AI systems derived from analysis of 60 commercial applications. A smart generalist might read it to learn structured tools for designing interfaces that balance AI autonomy with user involvement in everyday products.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"The reader's assessment already isolates the exact point at which the claim's support is weakest. Without access to the full text, no additional internal inconsistency or unsupported assumption can be identified beyond the methodological gap already noted.","tokens_in":1715,"tokens_out":272,"duration_ms":18313,"concrete_test":"Locate the methods or analysis section and verify whether it reports app-selection criteria, coding procedure, number of coders, and any reliability metric; if the section is absent or contains only high-level description, rerun the analysis on a fresh sample of 20 apps using the published dimensions to test reproducibility.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim is that artifact analysis of 60 commercial applications yields a generalizable three-dimensional framework (salience, involvement, activity) plus mid-level tools such as coordination zones and input taxonomies. The reader's weakest assumption correctly flags the missing methodological detail on how the dimensions and zones were extracted. However, because the full manuscript is not supplied in the provided context and the abstract alone does not contain contradictory or internally inconsistent statements, no load-bearing flaw in the argument itself can be isolated at this stage. The framework could still be a useful descriptive synthesis even if its derivation process remains opaque.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper claims that landscape and artifact analysis of 60 commercial AI applications yields a three-dimensional framework for human-AI coordination (salience: how prominently AI is presented; involvement: what users can do to engage AI; activity: what AI actually does). It contributes mid-level design tools including coordination zones (done-for-me, done-under-me, done-with-me, done-without-me), an input taxonomy (prompted, sparked, inferred, layered), coordination curves for mapping user journeys, and design patterns. The framework is intended for generative design, analytical evaluation, and cross-stakeholder communication of human-in-the-loop experiences with agentic AI.","tokens_in":1861,"tokens_out":579,"duration_ms":22135,"significance":"If the derivation and generalizability hold, the work supplies needed mid-level design knowledge that sits between high-level principles and low-level UI patterns, offering structured vocabulary and visual tools that could improve usability, trust, and safety in everyday agentic AI products. The explicit coordination zones and input taxonomy provide concrete, reusable artifacts for practitioners.","major_comments":[{"comment":"Methods section: The manuscript provides no information on selection criteria for the 60 applications, the coding scheme or process used to surface the three dimensions and zones, inter-rater reliability, or any validation against external data. This absence directly undermines the claim that the framework constitutes generalizable mid-level design knowledge rather than an interpretive synthesis.","section":"Methods"},{"comment":"§4 (Framework): The mapping from observed app features to the specific coordination zones and input taxonomy is presented as emergent from the analysis, yet no trace of the analytic steps, counter-examples, or saturation criteria is supplied. Without this, it is impossible to evaluate whether the zones are load-bearing constructs or post-hoc categorizations.","section":"§4"},{"comment":"§5 (Design patterns): The generative capacity of the framework is illustrated with examples, but these examples are not cross-checked against the original 60-app corpus or tested for predictive utility; the section therefore does not demonstrate that the framework adds explanatory power beyond existing high-level guidelines.","section":"§5"}],"minor_comments":[{"comment":"Figure 2 (coordination curves): Axis labels and legend are too small for print; add a caption that explicitly ties each curve segment to the three dimensions.","section":"Figure 2"},{"comment":"The abstract states the framework is derived from 'landscape and artifact analysis' but the introduction does not cite prior HCI landscape studies; adding 2-3 references would clarify the methodological lineage.","section":"Introduction"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the thoughtful and constructive comments, which highlight important opportunities to strengthen the methodological transparency of our landscape analysis. We address each major comment below and will revise the manuscript accordingly.","responses":[{"response":"We agree that the current manuscript lacks a dedicated Methods section with these details. In the revision, we will add a Methods section describing: (1) selection criteria (apps were chosen from major platforms for diversity in domains such as productivity, creative tools, and consumer services, prioritizing those with agentic features released or updated in 2023-2024); (2) the artifact analysis process (systematic review of interfaces, onboarding flows, and feature sets by the author team through iterative discussion); and (3) the emergent coding that surfaced the dimensions of salience, involvement, and activity. We will explicitly note that this was an interpretive synthesis without formal inter-rater reliability metrics or external validation, and we will add a limitations subsection discussing implications for generalizability.","revision_made":"yes","referee_comment":"[Methods] Methods section: The manuscript provides no information on selection criteria for the 60 applications, the coding scheme or process used to surface the three dimensions and zones, inter-rater reliability, or any validation against external data. This absence directly undermines the claim that the framework constitutes generalizable mid-level design knowledge rather than an interpretive synthesis."},{"response":"We acknowledge the absence of traceable analytic steps in the current draft. The revision will expand §4 with a new subsection that outlines the derivation process, including representative examples from the 60-app corpus that informed each coordination zone (done-for-me, done-under-me, done-with-me, done-without-me) and input type, as well as counter-examples considered during refinement. This will make the grounding of the constructs more explicit while preserving the mid-level, design-oriented nature of the contribution.","revision_made":"yes","referee_comment":"[§4] §4 (Framework): The mapping from observed app features to the specific coordination zones and input taxonomy is presented as emergent from the analysis, yet no trace of the analytic steps, counter-examples, or saturation criteria is supplied. Without this, it is impossible to evaluate whether the zones are load-bearing constructs or post-hoc categorizations."},{"response":"The examples in §5 are primarily illustrative to demonstrate generative use. In revision, we will add explicit cross-references linking each design pattern back to specific applications from the original corpus and clarify how the coordination zones and curves provide structured vocabulary and visual mapping that extend beyond generic principles. We agree that formal predictive testing lies outside the scope of this paper and will add language in the Discussion and Limitations sections to this effect, positioning the work as a mid-level framework rather than a validated predictive model.","revision_made":"partial","referee_comment":"[§5] §5 (Design patterns): The generative capacity of the framework is illustrated with examples, but these examples are not cross-checked against the original 60-app corpus or tested for predictive utility; the section therefore does not demonstrate that the framework adds explanatory power beyond existing high-level guidelines."}],"tokens_in":1432,"tokens_out":672,"duration_ms":29029,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main takeaway is that this work gives HCI designers a practical set of labels and diagrams for thinking about how users and agentic AI share control. The three dimensions (salience, involvement, activity) plus the four coordination zones (done-for-me through done-without-me), the input taxonomy, and the coordination curves are presented as new organizing tools pulled from commercial examples.\n\nWhat the paper does well is turn scattered interface choices into something that can be used both to generate designs and to talk about existing ones. Naming the zones and showing example patterns makes the framework more than another set of high-level guidelines. That kind of mid-level language is genuinely missing in this area.\n\nThe soft spot is the analysis step itself. The abstract says the framework comes from landscape and artifact analysis of 60 apps, yet it gives no information on selection criteria, how the dimensions were identified, or any check on consistency. Without those details it is hard to tell whether the zones reflect a stable pattern across apps or mainly the authors' reading of the ones they chose. That does not make the framework useless, but it does limit how far we can trust it as general design knowledge right now.\n\nThis is for people who design or evaluate agentic AI products and need something more concrete than broad principles. A practitioner could try the zones on a new feature and see if they help surface options. It is worth sending to peer review because the idea is concrete enough that referees can test the categories against more cases and ask for the missing methodological steps. The core synthesis looks worth refining rather than discarding.","headline":"The paper supplies a usable mid-level framework with named zones and taxonomies from 60 apps, but the extraction process is not described enough to judge how solid the categories are.","tokens_in":2339,"tokens_out":398,"would_cite":false,"duration_ms":32908,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Analysis of 60 commercial AI applications produces a three-dimension framework for human-AI coordination zones.","keywords":["human-AI coordination","agentic AI","human-in-the-loop","design framework","coordination zones","salience","involvement","activity"],"falsifier":"A collection of commercial AI applications whose coordination behaviors cannot be described by any combination of the three dimensions or four zones would show the framework does not cover the space of existing products.","tokens_in":2615,"feed_emoji":"🔄","tokens_out":626,"duration_ms":22427,"temperature":0.7,"pith_summary":"The paper establishes a mid-level design framework that treats human-AI coordination as the combined result of how prominently AI appears, what actions users can take with it, and what the AI actually performs. This framework supplies concrete tools such as four coordination zones, an input taxonomy, and journey-mapping curves. A sympathetic reader would care because current AI products lack shared language between high-level guidelines and specific interface choices, leaving designers without reliable ways to support usability, trust, and safety. The work shows the framework can generate new designs, evaluate existing ones, and help teams communicate requirements.","feed_headline":"Three dimensions map human-AI coordination in 60 apps","feed_subtitle":"The resulting zones and curves give designers concrete tools to set salience, involvement, and activity levels.","key_machinery":"The coordination zones framework, which treats human-AI coordination as the interplay of salience (AI prominence), involvement (user actions), and activity (AI actions) to generate, evaluate, and communicate interface designs.","core_discovery":"Through landscape and artifact analysis of 60 commercial AI applications, the paper defines human-AI coordination as the interplay of three dimensions—salience, involvement, and activity—and introduces coordination zones (done-for-me, done-under-me, done-with-me, done-without-me), an input taxonomy (prompted, sparked, inferred, layered), coordination curves, and reusable design patterns.","pith_inferences":["The zones could be applied to non-commercial or research prototypes to check whether the same patterns appear outside the original 60 apps.","Coordination curves might be used in longitudinal studies to track how user journeys change after software updates.","The framework offers a way to compare coordination styles across product categories such as productivity tools versus entertainment apps."],"forward_implications":["Designers can use the zones and curves to create new human-in-the-loop experiences.","Existing AI interfaces can be evaluated by mapping them onto the salience-involvement-activity space.","Cross-functional teams gain a shared vocabulary for specifying coordination requirements.","The input taxonomy supports systematic choices about how users trigger AI actions."],"fun_headline_variants":["AI zones map salience involvement activity","60 apps define human-AI coordination zones","Done-for-me to done-without-me AI framework","Coordination curves for human-AI design journeys"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Landscape and artifact analysis of 60 commercial AI applications yields generalizable mid-level design knowledge that applies beyond the sampled products.","fun_headline_variants_meta":{"raw":{"variants":["AI zones map salience involvement activity","60 apps define human-AI coordination zones","Done-for-me to done-without-me AI framework","Coordination curves for human-AI design journeys"]},"model":"grok-4.3","cost_usd":0.005386,"raw_usage":{"total_tokens":2585,"prompt_tokens":646,"num_sources_used":0,"completion_tokens":53,"cost_in_usd_ticks":53862000,"prompt_tokens_details":{"text_tokens":646,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1886,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":646,"tokens_out":53,"duration_ms":20553,"temperature":1.0,"reasoning_tokens":1886,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-01T08:18:31.163211+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A collection of commercial AI applications whose coordination behaviors cannot be described by any combination of the three dimensions or four zones would show the framework does not cover the space of existing products.","supporting_citations":[],"review_version":1}