{"id":"eb9649f2-7166-43d1-ad3b-1a3ae85ceb31","arxiv_id":"2605.24325","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"PAIRED is a process-anchored dual-facing framework for transparent reporting of AI contributions that uses decision-point granularity and artifact-triggered logging to capture cognitive dynamics in AI-enabled discovery.","lead":"The paper proposes PAIRED, a new framework for reporting AI use in scientific research that logs decision points during the process instead of only documenting final outputs. Smart generalists might read it to see a structured way to increase transparency about how AI shapes research directions and evaluations.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Framework viability rests on untested claim that prospective decision-point logging yields publisher disclosure without double work or selective omission","rationale":"The identified weakest assumption directly matches the load-bearing point for the central claim; the abstract-only limitation noted by the reader reinforces that practicality evidence is absent, so no adjustment to UNVERDICTED is warranted.","tokens_in":1772,"tokens_out":299,"duration_ms":20405,"concrete_test":"Implement the PAIRED logging protocol in one complete research project (e.g., a 4-week AI-assisted study); record total researcher time on logging versus normal documentation, then generate the publisher disclosure directly from the log and have an independent reviewer check for omissions or added drafting effort; if logging exceeds 10% extra time or requires post-hoc editing, the no-double-work claim does not hold.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The strongest claim requires that PAIRED's dual-facing output and artifact-triggered logging actually deliver accurate process records from a single author log. The paper asserts this via design principles but supplies only conceptual description and worked examples; no pilot data, integration details with research tools, or measurement of added cognitive/load burden appear. If researchers must still perform separate disclosure drafting or if decision points remain subjectively logged, the process-orientation advantage over output-oriented frameworks collapses. This assumption is least secure because the adoption pathway is described at the level of proposal rather than demonstrated mechanism.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript proposes PAIRED (Process-Anchored Interaction Reporting for AI-Enabled Discovery), a dual-facing framework for reporting AI contributions in scientific research. It argues that existing frameworks are uniformly output-oriented and therefore cannot distinguish cognitive dynamics such as origination of research direction versus adoption of AI-proposed directions or critical evaluation versus uncritical acceptance. PAIRED is defined by four design principles—process orientation (decision point as unit), dual-facing output (structured publisher disclosure derived from a prospective author log), decision-point granularity, and artifact-triggered logging—and is illustrated via worked examples, with discussion of limitations and a model-assisted adoption pathway embedded in research platforms.","tokens_in":1883,"tokens_out":482,"duration_ms":28317,"significance":"If the untested adoption assumptions hold, the framework could improve transparency by shifting documentation from outputs to the research process, enabling finer attribution of intellectual contribution in AI-enabled work. The proposal is internally consistent, introduces a novel process-oriented lens, and explicitly discusses limitations while outlining an integration pathway; these are genuine strengths for a conceptual contribution in the cs.CY area.","major_comments":[{"comment":"Abstract and the section describing dual-facing output: the claim that dual-facing output and artifact-triggered logging together produce accurate publisher disclosure from a single prospective author log without double work or selective omission is load-bearing for the asserted advantage over output-oriented frameworks, yet the manuscript supplies only design principles and worked examples with no pilot data, tool-integration details, or measurement of added cognitive or logging burden.","section":"Abstract"},{"comment":"The section on demonstration through worked examples: the examples illustrate the four principles conceptually but contain no test or simulation of whether decision-point logging can be automatically or easily transformed into structured disclosure, leaving the central practical-viability claim unsupported.","section":"Demonstration through worked examples"}],"minor_comments":[{"comment":"Ensure that every reference to the four design principles is accompanied by a short parenthetical reminder of its definition on first use in each major section to aid readers unfamiliar with the framework.","section":"Throughout"}],"recommendation":"major_revision","confidential_remarks":"The manuscript is a timely conceptual proposal well-aligned with the scope of a Computers and Society venue; no issues with citation patterns or undisclosed prior work."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive and detailed report. The comments correctly identify that the manuscript is a conceptual proposal relying on design principles and worked examples rather than empirical testing. We respond to each major comment below, maintaining that the contribution is appropriately scoped for a framework paper in cs.CY while acknowledging the absence of pilot data.","responses":[{"response":"We agree that the manuscript provides no pilot data or quantitative measurement of cognitive or logging burden, as it is a conceptual framework proposal rather than an empirical evaluation. The dual-facing output and artifact-triggered logging are presented as logical consequences of the four design principles, with the worked examples illustrating how a single prospective log can generate the publisher disclosure without requiring separate authoring. The model-assisted adoption pathway section outlines a route for platform integration that would address burden and tool details in implementation. The paper already states its limitations regarding untested adoption assumptions. No empirical data can be added without changing the paper's scope from proposal to evaluation study.","revision_made":"no","referee_comment":"[Abstract] Abstract and the section describing dual-facing output: the claim that dual-facing output and artifact-triggered logging together produce accurate publisher disclosure from a single prospective author log without double work or selective omission is load-bearing for the asserted advantage over output-oriented frameworks, yet the manuscript supplies only design principles and worked examples with no pilot data, tool-integration details, or measurement of added cognitive or logging burden."},{"response":"The worked examples are designed to show the application of decision-point granularity and artifact-triggered logging in concrete research scenarios, demonstrating the conceptual transformation from log entries to structured disclosure. No automated simulation or test is included because the manuscript does not claim to have implemented or evaluated a logging tool; instead, it proposes such integration via the model-assisted adoption pathway. The practical viability is argued on the basis of the design principles themselves, particularly artifact-triggered logging as an auditable mechanism against omission. We accept that a full simulation would provide stronger support but lies outside the current conceptual contribution.","revision_made":"no","referee_comment":"[Demonstration through worked examples] The section on demonstration through worked examples: the examples illustrate the four principles conceptually but contain no test or simulation of whether decision-point logging can be automatically or easily transformed into structured disclosure, leaving the central practical-viability claim unsupported."}],"tokens_in":1426,"tokens_out":538,"duration_ms":29347,"standing_objections":["Absence of any pilot data, tool prototypes, or empirical measurements of logging burden or transformation feasibility, which cannot be supplied without conducting new studies outside the manuscript's conceptual scope."]},"desk_editor":{"model":"grok-4.3","letter":"This paper introduces PAIRED as a process-anchored framework for reporting AI contributions in research. It argues that existing methods only record what AI produced and miss the cognitive steps that shape actual intellectual input, such as whether a researcher originated an idea or merely accepted an AI suggestion.\n\nThe contribution is the set of four design principles: anchoring at decision points, generating dual-facing outputs from a single log, using intermediate granularity, and triggering logs on artifacts to limit omission. These are presented as a way to produce both author records and publisher disclosures without separate drafting. Worked examples are included to show the structure.\n\nThe proposal is internally consistent and names a real shortcoming in current practice. The discussion of limitations and the suggestion to embed logging in research platforms are straightforward.\n\nThe central weakness is that the practicality claims rest on untested assumptions. Nothing in the paper shows that prospective decision-point logging can be maintained accurately by researchers or transformed into disclosures without added burden or room for selective reporting. No pilot data, tool integration details, or measurements of cognitive load appear. If those steps still require extra effort, the process advantage over output-oriented frameworks does not hold.\n\nThis is for readers working on research integrity and AI transparency standards. It deserves peer review so that the feasibility questions can be examined directly rather than left at the conceptual level.","headline":"PAIRED identifies a genuine gap in output-focused AI disclosure but supplies no evidence that its logging approach avoids extra work or selective omission.","tokens_in":2350,"tokens_out":341,"would_cite":false,"duration_ms":28708,"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":"Existing frameworks for AI use in research only record outputs and miss how decisions unfold; PAIRED logs the process at decision points to capture actual intellectual contributions.","keywords":["AI disclosure","research transparency","generative AI","academic reporting","process-oriented documentation","decision-point logging","scientific integrity","dual-facing output"],"falsifier":"A controlled trial in which teams record their AI-assisted work both with and without PAIRED, then independent assessors compare the resulting disclosures against full session transcripts to check whether cognitive dynamics and omissions are accurately reflected.","tokens_in":2652,"feed_emoji":"📋","tokens_out":678,"duration_ms":25783,"temperature":0.7,"pith_summary":"The paper identifies that current disclosure methods document only what AI produced, without revealing whether the researcher originated ideas, critically evaluated options, or simply accepted AI suggestions. This leaves the nature of the human intellectual contribution unclear. PAIRED counters this by shifting documentation to the decision point as the basic unit, using prospective author logs that automatically generate publisher disclosures. The framework adds rules for artifact-triggered entry to block selective omission and operates at a granularity between whole sessions and individual messages. If adopted, it would let readers and reviewers distinguish different kinds of AI collaboration without extra reporting burden on authors.","feed_headline":"Framework logs AI research decisions to reveal true contributions","feed_subtitle":"By recording at each decision point rather than final outputs, it distinguishes origination and evaluation from simple acceptance of AI sugg","key_machinery":"Decision-point granularity combined with artifact-triggered logging, which anchors documentation in the research process and automatically produces dual-facing output from a single prospective log.","core_discovery":"PAIRED is a dual-facing framework built on four principles: process orientation that treats the decision point rather than the research product as the documentation unit; dual-facing output that turns one prospective author log into both an internal record and a structured publisher disclosure; decision-point granularity that sits between session-level and message-level recording; and artifact-triggered logging that supplies an auditable rule against selective omission. The framework is demonstrated through worked examples and paired with a model-assisted adoption pathway that embeds the logging discipline into AI research platforms.","pith_inferences":["If widely used, the framework could shift authorship norms by making the degree of human steering visible in every paper.","Publishers might need new review protocols to interpret the decision-point disclosures rather than treating them as simple checklists.","Integration with existing research tools could make the prospective logging automatic, but would also raise questions about data ownership of the logs themselves."],"forward_implications":["Disclosures would distinguish origination of ideas from adoption of AI-proposed directions.","Reviewers could evaluate whether critical assessment of AI alternatives occurred.","A single log would satisfy both internal record-keeping and external reporting requirements.","Artifact-triggered rules would reduce the scope for selective omission.","Embedding the logging rules in AI platforms would lower the adoption barrier for researchers."],"fun_headline_variants":["PAIRED logs research decisions for transparent AI reporting","Framework records AI decisions at key research points","Decision-point logging distinguishes AI contributions in science","Process-anchored tool for reporting AI roles in research"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Researchers will keep accurate prospective logs at each decision point and that one author log can be turned into a structured publisher disclosure without extra work or selective omission.","fun_headline_variants_meta":{"raw":{"variants":["PAIRED logs research decisions for transparent AI reporting","Framework records AI decisions at key research points","Decision-point logging distinguishes AI contributions in science","Process-anchored tool for reporting AI roles in research"]},"model":"grok-4.3","cost_usd":0.003194,"raw_usage":{"total_tokens":1748,"prompt_tokens":726,"num_sources_used":0,"completion_tokens":57,"cost_in_usd_ticks":31937000,"prompt_tokens_details":{"text_tokens":726,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":965,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":726,"tokens_out":57,"duration_ms":10683,"temperature":1.0,"reasoning_tokens":965,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-30T12:49:48.760163+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A controlled trial in which teams record their AI-assisted work both with and without PAIRED, then independent assessors compare the resulting disclosures against full session transcripts to check whether cognitive dynamics and omissions are accurately reflected.","supporting_citations":[],"review_version":1}