The paper introduces a computational framework that detects dynamic team‑process phases in collaborative virtual reality (VR) by analyzing timestamped dialogue. It uses late chunking, penalized Gaussian‑kernel change‑point detection, TF‑IDF, NMF, and a locally deployed large language model to identify semantic transitions and generate interpretable phase descriptions. The detected phases are aligned with interaction logs, demonstrating that transcript‑derived phases correspond to task‑action patterns and thus provide a transparent, transferable method for studying temporal changes in teamwork.
arXiv:2606. 14784v1 Announce Type: cross Abstract: Understanding human states and interaction dynamics is a core goal of human-computer interaction (HCI).
PUBG Ally is an embodied, voice‑enabled AI teammate for PUBG: BATTLEGROUNDS that can perceive the game world, interpret player speech, and autonomously decide actions while keeping speech synchronized with gameplay. It combines a language‑model agent that uses a controlled interface to gather game information and a faster control layer for movement, combat, and recovery. The system was trained on nearly 39,000 real‑player sessions and evaluated through player feedback and preference comparisons, with live deployment requiring low‑latency on‑device execution and safety safeguards.
arXiv:2608. 14016v1 Announce Type: cross Abstract: Live game commentary is scarce: it exists for professional esports broadcasts and almost nowhere else.
arXiv:2606.08081v2 Announce Type: replace-cross Abstract: Repeated reference games test whether interlocutors replace their initially long descriptions with shorter, partner-specific expressions grou...
arXiv:2609.16396v1 Announce Type: new Abstract: Negation is typically modeled through its linguistic realization, although spoken interaction is accompanied by tightly coordinated nonverbal behavior....
arXiv:2606. 08081v1 Announce Type: cross Abstract: Repeated reference games test whether interlocutors replace their initially long descriptions with shorter, partner-specific conventions grounded in shared interaction history.
arXiv:2607. 29181v1 Announce Type: cross Abstract: Agentic assistants capable of proactive, personalized interactions require structured models of user intent and workflow.
arXiv:2606. 09831v1 Announce Type: cross Abstract: As classroom cohorts expand, team teaching is increasingly used to integrate the expertise and pedagogical perspectives of multiple teachers.
arXiv:2606. 31719v1 Announce Type: cross Abstract: In collaborative dialogue, shared perception does not guarantee shared interpretation.
arXiv:2608. 06975v1 Announce Type: cross Abstract: Long-horizon role-playing demands that characters remain recognizable as they evolve with the narrative.
The paper introduces CoCoEval, a framework for evaluating large language model (LLM)–simulated conversations by detecting 10 types of inconsistent and uncollaborative behaviors at the turn level. Using CoCoEval, the authors compare human conversations with those generated by GPT‑4.1, GPT‑5.1, and Claude Opus 4, finding that LLMs produce far fewer such behaviors under vanilla prompting and that prompt engineering or fine‑tuning often over‑produces specific behaviors. The study highlights gaps between human and LLM‑simulated interactions that conventional Likert‑scale evaluations miss, raising concerns about using LLMs as proxies for human social interaction.
arXiv:2606. 11835v1 Announce Type: cross Abstract: Collecting participants' lived experiences is central to design research.