cs.HC · 2026-09-03 · No. 104

Human-Computer Interaction, 2026-09-03.

3 new papers in cs.HC. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    Towards a Foundational Ontology for Identifying and Resolving Contradictions in Dialogue-based Human-Robot Interactions

    Maitreyee Tewari, Michele Persiani

    cs.HC · cs.AI

    Existing Human-Robot Interaction (HRI) literature has focused on identifying and structuring errors, failures, conflicts, and knowledge issues (called in this work as contradictions) in domain-specific dialogue-based interactions. However, there is still lack of a formal computational framework to represent and define these contradictions, interoperable and usable across HRI and human-agent interaction (HAI) domains. Thus, this research...

    arxiv.org/abs/2609.02364 · PDF

  2. 02

    OmegaUse-SOP: SOP Engineering for Professional Computer Use from Human Demonstrations

    Yixiong Xiao, Lang An, Hucheng Yang, Pinxue Ma, Yongquan Chen, Jingjia Cao, Yusai Zhao, Ting Wang, Ting Liu, Siqi...

    cs.HC · cs.AI

    Large language models (LLMs) are increasingly evolving from conversational assistants into agents capable of operating external digital environments. Graphical user interface (GUI) agents play an important role in this transition, as many real-world workflows remain accessible only through user-facing software interfaces. However, despite recent progress on general computer-use benchmarks, domain-specific professional standard operating...

    arxiv.org/abs/2609.02149 · PDF

  3. 03

    Knowing Is Not Enough: Information Retrievability as a Precondition to Effective LLM Oversight

    Xinyu Fu, Narayan Ramasubbu, Dennis Galletta

    cs.HC · cs.AI

    Large language models (LLMs) are increasingly embedded in organizational work, yet their errors often pass human review. Prior research locates such failures in users' capability to review LLM output or their engagement in doing so. We develop an alternative, retrieval-based account of human oversight and posit that error detection is more effective when oversight-relevant information is accessible to users at the moment of review. Across two...

    arxiv.org/abs/2609.01976 · PDF

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