{"total":13,"items":[{"citing_arxiv_id":"2606.25489","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"When LLM Rationales Become User-Facing: Effects on Trust Perception, Decision-Making, and Gaze Behaviors","primary_cat":"cs.HC","submitted_at":"2026-06-24T07:18:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Two linked user studies find that LLM rationale correctness and certainty framing affect trust and decision confidence while presentation format does not, and incorrect rationales increase gaze attention and pupil size.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.23840","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Embodied Explainability and Ontological Obstacles: Why We Struggle to Explain the Answers of Large Language Models (LLMs)","primary_cat":"cs.HC","submitted_at":"2026-06-22T18:22:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"An argument paper reframes LLM explainability as an embodied, situated practice based on Dourish and enactivist cognition, identifying ontological obstacles in internal explanations and advocating affordance-based designs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.01189","ref_index":194,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Case for Model Science: Verify, Explore, Steer, Refine","primary_cat":"cs.AI","submitted_at":"2026-05-31T12:11:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Position paper proposing Model Science as a discipline to systematically analyze AI model behavior beyond benchmarks, drawing analogies from cognitive science, neuroscience, medicine, and agriculture.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.25856","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition","primary_cat":"cs.HC","submitted_at":"2026-05-25T13:46:04+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Summary reasoning traces from LLMs maintain task performance and increase trust and appeal relative to answer-only or full-trace conditions, but none of the formats improve users' metacognitive calibration on reasoning tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21635","ref_index":18,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Addressing the Synergy Gap: The Six Elements of the Design Space","primary_cat":"cs.HC","submitted_at":"2026-05-20T18:46:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"The paper proposes six interconnected elements of a design space to close the synergy gap in human-AI decision-making.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16197","ref_index":86,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Position: AI as Part of Self -- Extending the Mind Requires Cognitive Co-Regulation","primary_cat":"cs.HC","submitted_at":"2026-05-15T17:11:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"The paper claims that alignment requires treating AI as part of the self through cognitive co-regulation, identifying risks like deskilling and automation bias while drawing on System 0 cognition theory.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[84] Ziyang Guo, Yifan Wu, Jason Hartline, and Jessica Hullman. A decision theoretic framework for measuring AI reliance. InProceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT '24), page 26, 2024. doi: 10.1145/3630106.3658901. [85] Gary Marchionini. Exploratory search: From finding to understanding.Communications of the ACM, 49(4):41-46, 2006. doi: 10.1145/1121949.1121979. [86] Meredith Ringel Morris, Jascha Sohl-Dickstein, Noah Fiedel, et al. Levels of AGI for operationalizing progress on the path to AGI.Proceedings of the International Conference on Machine Learning, 2024. 14 [87] Alexander Skulmowski. The cognitive architecture of digital externalization.Educational Psychology Review, 35(4):101, 2023. doi: 10.1007/s10648-023-09818-1."},{"citing_arxiv_id":"2605.15322","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Overreliance in Writing Tasks: Exploring Similarity-Based Measures of AI Influence on Writing and Proposing a Reflective Writing Interface Intervention","primary_cat":"cs.HC","submitted_at":"2026-05-14T18:36:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Mixed-methods study finds AI assistance linked to higher textual overlap with suggestions in writing tasks, and a reflective interface prototype increases user awareness of AI incorporation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14207","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"What Should Explanations Contain? A Human-Centered Explanation Content Model for Local, Post-Hoc Explanations","primary_cat":"cs.HC","submitted_at":"2026-05-14T00:05:00+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[11] Bannon, L.J., 1991. From human factors to human actors: The role of psychology and human-computer interaction studies in system design, in: Greenbaum, J., Kyng, M. (Eds.), Design at Work: Cooperative Design of Computer Systems. Lawrence Erlbaum Associates, Hillsdale, NJ, pp. 25-44. URL:https://doi.org/10.1201/9781315800349-4, doi:10.1201/9781315800349-4. [12] Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M.T., Weld, D., 2021. Does the Whole Exceed Its Parts? The Effect of AI Explanations on Complementary Team Performance, in: Pro- ceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New York, NY, USA. pp. 81:1-81:16. doi:10."},{"citing_arxiv_id":"2605.10930","ref_index":36,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Evaluating the False Trust Engendered by LLM Explanations","primary_cat":"cs.HC","submitted_at":"2026-05-11T17:58:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LLM reasoning traces and post-hoc explanations increase false trust in incorrect predictions, whereas contrastive dual explanations enhance users' ability to distinguish correct from incorrect AI outputs.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"On human predictions with explanations and predictions of machine learning models: A case study on deception detection. InProceedings of the Conference on Fairness, Accountability, and Transparency, FAT* '19, page 29-38, New York, NY , USA, 2019. Association for Computing Machinery. ISBN 9781450361255. doi: 10.1145/3287560. 3287590. URLhttps://doi.org/10.1145/3287560.3287590. [36] Amina Adadi and Mohammed Berrada. Peeking inside the black-box: A survey on explainable artificial intelligence (xai).IEEE Access, PP:1-1, 09 2018. doi: 10.1109/ACCESS.2018. 2870052. [37] OpenAI. GPT-4 technical report, 2023. URLhttps://arxiv.org/abs/2303.08774. [38] DeepSeek-AI. DeepSeek-V3 technical report, 2024. URL https://arxiv.org/abs/2412."},{"citing_arxiv_id":"2605.09273","ref_index":53,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Instance-Adaptive Online Multicalibration","primary_cat":"cs.LG","submitted_at":"2026-05-10T02:45:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A single algorithm for online multicalibration achieves instance-adaptive rates by dynamically refining a dyadic prediction grid, recovering the worst-case Õ(T^{2/3}) bound and improving to Õ(√T) in marginal stochastic settings and Õ(√(JT)) for J-piecewise stationary means.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.05166","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistants","primary_cat":"cs.HC","submitted_at":"2026-04-06T20:50:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Structural mental models of AI writing assistants improve system understanding and usability but result in more grammatical errors in user writing compared to functional models.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[4] Gagan Bansal, Besmira Nushi, Ece Kamar, Walter S Lasecki, Daniel S Weld, and Eric Horvitz. 2019. Beyond accuracy: The role of mental models in human-AI team performance. InProceedings of the AAAI Conference on Human Computation and Crowdsourcing, Vol. 7. Association for the Advancement of Artificial Intelligence (AAAI), Honolulu, Hawaii, USA, 2-11. doi:10.1609/hcomp.v7i1.5285 [5] Gagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel Weld. 2021. Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan)(CHI '21, Article 81). Association for Computing Machinery,"},{"citing_arxiv_id":"2512.13061","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Modeling Collaborative Problem Solving Dynamics from Group Discourse: A Text-Mining Approach with Synergy Degree Model","primary_cat":"cs.CY","submitted_at":"2025-12-15T07:43:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Automated classification of CPS discourse combined with the Synergy Degree Model produces group-level synergy degrees that distinguish collaborative quality and reveal task-type differences in MOOC groups.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2507.22163","ref_index":75,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"IdeaBlocks: Expressing and Reusing Divergent Intents for Graphic Design Exploration using Generative AI","primary_cat":"cs.HC","submitted_at":"2025-07-29T18:53:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"IdeaBlocks modularizes divergent intents into Exploration Blocks with multi-level reuse options, enabling 2.13 times more images explored and 12.5% greater visual diversity than baseline in a comparative user study.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}