Relational AI increasingly serves as an emotional shelter for humans, and its impact is mixed. Prior research has focused on either positive or negative impacts, leaving unclear how they are configured within individuals and relate to psychological functioning. To address these gaps, this study used a sequential mixed-...
Lu Chen, Feng-Hua Tang, Jia-Yu Zhao et al.· 0 citations
CATVis is presented, a Collaborative multi-agent workflow system that bridges the gap by transforming NL intents into structured middle representation for visualization, and significantly improves complex workflow generation correctness while reducing prompt complexity.
Zhe Wang, Zehao Lou, Guang-Hui Zhao et al.· 0 citations
We introduce a three-wave, in-the-wild multimodal dataset for affect sensing that integrates smartphone sensing, wearable sensing, and dense experience-sampling-method (ESM) labels collected annually from 2020 to 2022. The dataset supports moment-level affect modeling through a shared dimensional label core across all...
Pan-Yu Zhang, Minseo Park, Soowon Kang et al.· 0 citations
A central challenge in exploratory data analysis (EDA) is keeping track of what has already been examined in order to decide what to analyze next. In practice, analysts often run dozens of analyses while building an understanding of a dataset. However, most tools provide little support for maintaining an overview of th...
A wrist-worn pressure-sensing wristband that recovers continuous full-hand pose and distributed contact force on a single wearable is introduced, and the key insight is that muscle contraction and tendon displacement produce pressure patterns, which correlate strongly with hand pose and interaction force.
S. Kolev, Ling-Ni Ma, Michael Goesele et al.· 0 citations
This work designed two scaffolded interfaces around the same negotiation scaffold: one presented a completed AI analysis, while the other supported user-directed, incremental development, which elicited a broader repertoire of analytic requests and lower subjective effort.
Zi-Lin Ma, Suzi Jazmati, Marco Chimenton et al.· 0 citations
Conversations with large language models (LLMs) can substantially shift beliefs and attitudes, raising concerns about manipulation using AI persuasion. Here we test whether a light-touch AI literacy intervention - a brief warning that LLMs can be prompted to persuade and may present information selectively - helps prot...
Voice-assistant interruptions tend to be intrusive because existing systems fail to consider the affective state, cognitive load and situational context of the user when deciding when and how to interrupt.Voice-assistant interruptions tend to be intrusive, since existing systems do not consider the affective state, cog...
Peer stories have been shown to boost self-efficacy in older adults'health behavior change. Despite their effectiveness, peer stories are difficult to deploy in health promotion at scale given the difficulty of matching the diverse health concerns and coping styles of heterogeneous older populations. Large language mod...
Kexin Quan, Precious Olalere, Smit Desai et al.· 0 citations
PAIR, a theory-based emotion-regulation companion, was deployed with 19 participants for 14 days and linked memory updates and retained corrections to cross-session personalization, informing future emotional support tools that adapt to evolving needs, learn from prior outcomes, and preserve user control over memory.
Kexin Quan, Zi-Jian Ding, Jia-Ye Yong et al.· 0 citations
This paper argues for making the IxTs themselves more intelligent, so users can freely mix modalities, even within the same interaction, and also into the infrastructure that will enable these intelligent IxTs (IIxTs) to be built.
The results demonstrate the feasibility of a stateful contactless sensing-to-action architecture for long-term home health monitoring and integrates sensing, temporal state, reasoning, and action into a unified and auditable loop.
Xu-Wen Zhang, Zi-Jian Lu, Yi-Cheng Lei 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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