We introduce Unsupervised Partner Design (UPD), a population-free multi-agent reinforcement learning method for robust ad-hoc teamwork. UPD generates training partners on-the-fly and selects them adaptively based on a learnability criterion, removing the need for pre-trained partner populations or manual parameter tuni...
Constantin Ruhdorfer, Matteo Bortoletto, Victor Oei et al.· arXiv.org· 3 citations
These findings show that participants configured proactive support through prospective planning, used suggestions for both idea generation and self-monitoring, and valued lightweight visual representations alongside non-directive rhetorical framing for non-intrusive interventions.
Chao Zhang, Abe Davis, Chih-Wei Chen et al.· 1 citation
The results show strong performance on colonoscopy images and competitive performance on prostate MRI, supporting gaze-guided prototype refinement as a promising approach for segmentation-label-efficient, human-in-the-loop medical image segmentation.
M. Benyahia, Marouane Tliba, M. A. Kerkouri et al.· 0 citations
A disclosure gate conditioning information release on the companion agent's behaviour is answered, with a disclosure gate conditioning information release on the companion agent's behaviour: its state is a ladder of five ordered gates, merged onto three observable depth layers.
Yaozhong Liu, Yu He· 0 citations
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The manual labor trap is introduced as an empirically grounded concept that explains why monitoring CSOs tend to remain locked into labor-intensive, low-capacity arrangements.
Elisabeth Steffen, Helena Mihaljevic· Proceedings of the ACM on Hu...· 0 citations
A three-week diary study with 8 blind users using OLLA, a screen-reader-accessible CUA prototype, collecting 1,258 commands across 12 applications with screenshots, UI trees, model responses, and action traces reveals grounding, planning, constraint-tracking, and termination failures, while interviews reveal beyond-aut...
Satwik Ram Kodandaram, Monalika Padma Reddy, Xiaojun Bi et al.· 0 citations
RecalibrateGPT is a system introducing five cross-turn operators that each target a distinct fatigue type, recalibrating LLM responses through a structured panel by acting on the full conversation history with a single click, suggesting AI fatigue is not just a model-quality issue but an interaction-flow cost that inte...
The emergence of the theorems from sustained human-AI collaboration suggests that involving AI throughout a systematic research program may incubate autonomous scientific breakthroughs.
The cluster analysis of chatbot usage and discussion patterns by users showed that most participants in the unrestricted-mode adopted a direct answer-retrieval strategy, while participants in the Socratic-mode initially attempted to reason through the hints before progressively disengaging, suggesting that the success...
AI Morbidity and Mortality (AI M&M) is intended to complement, rather than replace, model monitoring, patient safety reporting, and regulatory oversight by converting individual AI-in-workflow failures into actionable institutional learning.
Paulius Mui, Dean F. Sittig, Steven Labkoff et al.· 0 citations
A taxonomy of bot species on the news aggregation and social media platform Reddit based on temporal, community, linguistic, and semantic features suggests that online bot populations form evolving digital ecosystems.
Qiusi Sun, Thomas Gaskin, B. Blagojević et al.· arXiv.org· 0 citations
With the emergence of Physical AI, artificial intelligence is extending beyond screen-based applications to embodied systems that perceive, interact with, and act in the physical world. Unlike traditional AI, Physical AI operates under real-time safety constraints, continuously interacts with dynamic environments, and...
Wang Yang, Shaojuan Wang, Hong-Xuan Liu 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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