Cooperative AI agents are evaluated against other AIs, yet human cooperation relies on implicit conventions---shared protocols for reading meaning beyond the literal message---which AI-AI benchmarks may not capture. We propose the \emph{convention gap}, the difference between the failure probability predicted from the...
The development of autonomous driving demands comprehensive testing in mixed-traffic scenarios involving vulnerable road users (VRUs), where purely artificial agents often fail to capture authentic human social negotiations. While human-in-the-loop (HITL) simulators enable safe investigation of these interactions, exis...
Patrick Rebling, Philipp Nenninger, Reiner Kriesten· 0 citations
Clinical education must prepare medical students to conduct safe and coherent patient interviews under conditions of uncertainty. Traditional standardized patient (SP) training is resource-intensive and difficult to scale. We developed a scaffolding-oriented multi-agent Large Language Model (LLM) AI Standardized Patien...
Luming Yang, Haoxian Liu, Siqing Li et al.· 0 citations
A conceptual framework is proposed composed of three core components which are drawn on the synthesis of selected empirical studies on technologies embedded in built environments for social wellbeing that could help researchers, practitioners and policymakers to design and guide digital technology interventions that ta...
Gul Sher Ali, M. Giannakos, M. Lillefjell et al.· 0 citations
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Five deployment dilemmas involving persistence, attention, role boundaries, state disclosure, and escalation that require stakeholder specification are derived, further informing technical implications for learning, situated evaluation, and embodied adaptation.
The pulling illusion induced by asymmetric vibration stimuli has attracted attention for its potential applications in rehabilitation and sensory assessment. However, the underlying mechanism of the pulling illusion remains unclear. This study addressed the central question of whether peripheral vibrotactile sensitivit...
Takeshi Tanabe, Satoshi Yamamoto, Toru Yamada et al.· 0 citations
WiFi gesture recognition is accurate in fixed deployments but often degrades when user orientation, available links, or transceiver placement changes. Unlike ordinary domain shifts, these changes alter the wireless observation operator, so the same motion is expected to produce different measurements. Existing methods...
Xiang Zhang, Huan Yan, Geying Yang et al.· 0 citations
Maritime Autonomous Surface Ships (MASS) and AI- supported decision assistants are expected to transform maritime operations, but their safe integration depends on how maritime professionals perceive and trust such systems. This paper presents a survey study on maritime stakeholders' attitudes toward an AI-supported as...
Doreen Jirak, Armeen Saroukanoff, Dirk van Rooy· 0 citations
Extended Reality (XR) technologies have received growing attention in accessibility research involving Deaf and Hard of Hearing (DHH) communities. Yet less attention has been given to the assumptions shaping this work. We present a theory-grounded review of XR research involving DHH users. Drawing on Disability Studies...
Shuxu Huffman, Michaela Okosi, J. Merino et al.· 0 citations
Ethics Training Agents is proposed, a group discussion system that leverages multiple LLM participants embodying distinct ethical orientations, along with a moderator agent, to enable structured human-AI group ethical discussions for collaborative reflection.
Youngseok Seo, Sueun Jang, Hyesoo Park et al.· 0 citations
This study examines data visualization design evolution over 12.5 years, reflecting on the impact of Large Language Models over the last 3.75 years and identifies how LLMs have aided design-space exploration: reducing coding effort, enabling new design opportunities, shock, excitement, accomplishments, and shifts to th...
Live captions on TV often contain errors and timing issues, making it hard for deaf and hard-of-hearing (DHH) viewers to follow dialog. It is essential that caption quality metrics reflect the lived DHH TV viewing experience. To this end, we describe a U.S.-based large-scale online survey with 216 validated participant...
Bernard Thompson, James Waller, Luz Fanny Calderon Torres et al.· 0 citations
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 30, 2026
Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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