LifeSync-Games (LSG), a framework that operationalizes player signals as a Player Experience Integration Layer acting transversally across the three existing pervasive dimensions through verified real-world signals: physical activity, sleep quality, memory, and decision speed, is presented.
J. Macías-Cáceres, F. Gutiérrez-Vela, P. Paderewski-Rodriguez et al.· 0 citations
The findings prove that cBCI synergy is heavily contingent on the temporal dynamics of trust, providing a critical framework for designing dynamically gated Human-AI systems.
Christopher Baker, Stephen Hinton, Akashdeep Nijjar et al.· arXiv.org· 0 citations
Overall it can be concluded that promising UQ methods are available, but that research is needed on how people and systems may interact with an uncertainty-model in a (clinical) environment.
Ivo Pascal de Jong, A. Sburlea, M. Valdenegro-Toro· Journal of Healthcare Inform...· 8 citations
This work delivers an interpretable, privacy-by-design, edge-ready system that can provide real-time feedback during case submission to filter ungradable photo sets before review, and formalizes epidermal micro-relief as a computable biomarker of image quality.
Robert Engel· IEEE/ACM International Confe...· 0 citations
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The need for more user-centered studies to measure LLMs'ability to help users while preserving privacy, and for improving alignment between LLMs and users in estimating perceived privacy and utility, is discussed.
Xiaoyuan Wu, R. Kaushik, Wenkai Li et al.· Annual Meeting of the Associ...· 2 citations
It is shown that translating simulation traces to a sparse representation of "high-level"structural patterns leads to more effective interpretation by LLMs, and an unsupervised learning scheme to perform this translation, or annotation, via program synthesis is proposed.
GazeFS improves Focus target centering and empirical residual contraction while leaving temporal smoothness as a separate objective and introduces GazeFS, which maps a variable-length gaze-head history to the next target-center direction and a short-horizon Search/Focus estimate without target information at inference.
Yao-Zheng Xia, Zaiping Zhu, Bo Pang et al.· 0 citations
This article presents a structured framework of behavioral indicators that may signal progression toward potentially catastrophic threats from artificial intelligence systems. We adopt a pragmatic approach, inspired by established methodologies in cybersecurity and national security. By establishing clear metrics, indi...
T. Bauer, W. Kegelmeyer, Edmon Begoli et al.· 0 citations
It is argued that federation is not in itself a remedy for extractive AI, because outcomes depend on who governs the data and the model and who has agency over the practices that shape them, and four design principles for a creative data commons are proposed.
Phoenix Perry, George Simms, Elizabeth Wilson et al.· 0 citations
The TRANSparent Framework for Interactive, Verifiable, Editable Representation (Transfiver) is introduced, an architecture for human-AI co-inference through a shared editable state that separates shared parameters from the persistent state, which evolves during deployment without parameter retraining.
DECODE (Developer Edits of Code Dataset), a dataset of 53.6K real-world in-IDE code edits of AI-generated code in Python, TypeScript, and JavaScript, is introduced, and finetuning on DECODE enables open-source 3B models to perform code edit prediction tasks significantly better than frontier LLMs.
Jenny T Liang, M. Bairathi, Wayne Chi et al.· arXiv.org· 1 citation
In a controlled study, 20 university students completed five common daily tasks using OpenClaw, a general-purpose AI agent, across tasks chosen to vary in privacy, stakes, and reversibility, and found delegation regret appeared consistently when the agent executed actions without preview, even when the output was rated...
Shiva Pochampally, Shengwei An, Yan Chen· arXiv.org· 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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