Collaborative Brain-Computer Interfaces (cBCIs) offer a promising mechanism to augment team decision-making, but existing approaches rely exclusively on evidence available only after a decision has been made and reported, such as reaction time or stated confidence. This limits their use to explaining or discounting a d...
Christopher Baker, Stephen Hinton, Tom Reed et al.· 0 citations
Mastering musical performance requires precise multisensory coordination, yet learners encounter a kinesthetic mismatch, which is a discrepancy between the internal perception of an action and the actual physiological state of the body. While multisensory Body Transformation Experiences (BTE) provide tools to bridge th...
Ziyue Piao, Isabelle Cossette, Marcelo M. Wanderley· 0 citations
While breathing is essential to living and for sound production in some instruments, for pianists, it is often a hidden and automatic process, making it difficult to analyze or refine. A critical gap exists between data and awareness: while sensors record precise physical metrics, they fail to capture the performer's s...
Ziyue Piao, Yohei Wada, Isabelle Cossette et al.· 0 citations
This work introduces PROS (Proactive Refinement Of Scientific Posters), which separates epistemic initiative from behavioral authority: the system can surface source-grounded candidate problems, while users decide which become repair goals and whether resulting changes are committed.
Xingda Lyu, Hong-Ling Lu, Xin-ye Luo et al.· 0 citations
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This work introduces VirSqueezer, a framework designed to generate both localized deformations and complex squeezing dynamics, such as rupture and overflow, and enables the generation of physically realistic visual effects directly from continuous, fine-grained squeezing control signals.
Qian Zhang, Xiao-Ming Chen, Xiao-Rui Ma et al.· 0 citations
We study body-only, 12-class acted-emotion classification from skeleton motion under leave-performer-out (LPO) evaluation, a hard, underdetermined setting: chance is 8.3%, and a protocol-matched reproduced STGCN++ baseline reaches only 25.73 ± 4.03% Macro-F1. We show that reliable gains come not from a new architecture...
Naoto Nishida, Yoshio Ishiguro· 2026 14th International Conf...· 0 citations
A spectrum-placement task inspired by the social party game Wavelength is developed, which suggests that TUX provides a measurable behavioral signal of human--LLM tacit understanding, while revealing the limits of profile-based conditioning for capturing deeper representational alignment.
Yue-Shen Li, Han-Yi Min, Vedant Das Swain et al.· arXiv.org· 0 citations
Despite the growing utility of Large Language Models (LLMs) for simulating human behavior, the extent to which these synthetic personas accurately reflect world and moral value systems across different cultural conditionings remains uncertain. This paper investigates the alignment of synthetic, culturally-grounded pers...
C. M. Greco, Lucio La Cava, Andrea Tagarelli· arXiv.org· 3 citations
Comprehensive experiments show that MHL achieves predictive performance comparable to that of state-of-the-art methods, performs favorably in small-sample and highly imbalanced settings, and supports the transfer and adaptive revision of validated rules under feature evolution.
Wei Xu, Ke Yang, Gang Luo et al.· arXiv.org· 0 citations
As generative AI capabilities expand, AI-driven virtual worlds face a growing architectural challenge. Users interact through in-world interfaces in multimodal ways, yet their requests demand fundamentally different AI backend models and computational resources. Embedding these capabilities directly into virtual world...
AI copilots represent a new generation of AI-powered systems designed to assist users, particularly knowledge workers and developers, in complex, context-rich tasks. As these systems become more embedded in daily workflows, personalization has emerged as a critical factor for improving usability, effectiveness, and use...
Saleh Afzoon, Ali Shahsavandi, Phuong Thao Huynh et al.· 0 citations
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...
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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