The study investigates how users switch roles in a human‑AI chess collaboration, using multimodal behavioral signals such as gaze and task‑specific features. Participants mostly retained their roles, but when they switched they showed more exploratory gaze and poorer move quality. A classifier trained on these signals achieved a PR‑AUC of 0.56, indicating that behavioral cues can predict role switches.
arXiv:2609.18011v1 Announce Type: new Abstract: In collaborative tasks with asymmetric information, participants coordinate their understanding through interaction. We ask whether gaze provides evide...
The study examines how the speed and accuracy of an AI teammate—Fast/Less-Accurate (FLA-AI) versus Slow/Accurate (SA-AI)—affect performance in a collaborative Brain‑Computer Interface (cBCI) team during a virtual reality drone search task. Fast AI leads to instant, blind compliance and a sharp drop in human accuracy, while Slow AI induces delayed cognitive conflict that ultimately allows teams to recover and achieve perfect accuracy. A 2D Adaptive Riemannian Oracle and Hybrid Fusion techniques were used to adaptively capture and integrate these timing-dependent signals, improving team performance in both scenarios.
arXiv:2604.02578v2 Announce Type: replace-cross Abstract: Humans exhibit remarkable abilities to coordinate in groups. As large language models (LLMs) become more capable, it remains an open question...
The study compares human and vision‑language model (VLM) responses to cross‑modal association tasks, using identical stimuli (a pseudo‑word and two images) and recording both choices and eye movements. While larger VLMs show some alignment with human choices, their attention patterns correlate poorly with human gaze, performing no better than a simple center‑bias baseline. Fine‑tuning VLMs on human choices improves choice alignment but not attention alignment, and training on human gaze improves attention correlation without affecting choice accuracy.
The paper investigates how visuomotor imitation policies fail when visually similar distractors are introduced, framing the issue as conditional visual grounding where the target changes with manipulation phase and task state. Using Action Chunking with Transformers (ACT), the authors systematically vary color and shape similarity of distractors, pinpointing failures to picking and placement stages. They then test distractor augmentation, phase‑dependent attention regularization, and appearance‑based visual prompting, which together significantly improve robustness in simulation and on a physical UR3e robot, and demonstrate similar improvements on a pretrained vision‑language‑action policy for instrument handling.
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:2607. 12823v1 Announce Type: new Abstract: Interaction with AI agents has become one of the most frequent activities of everyday digital life.
arXiv:2606. 00182v1 Announce Type: cross Abstract: Human-AI collaboration is considered the most promising way to incorporate AI in the workplace.
arXiv:2504. 20903v4 Announce Type: replace-cross Abstract: How should organizations divide and sequence decision tasks between human and artificial agents?
arXiv:2605.06524v3 Announce Type: replace Abstract: Reliable human-machine discrimination is becoming increasingly important as Large Language Models and autonomous agents are deployed in online sett...
arXiv:2608. 16514v1 Announce Type: cross Abstract: Human visual search is serial: the fovea must land on a candidate to confirm it, and those landings form a scanpath.
arXiv:2609.39727v1 Announce Type: new Abstract: Cooperative language-model agents must coordinate over long horizons and adapt to changing environments and to partners with unfamiliar conventions, ye...