A gesture elicitation study was conducted in which users suggested interactions for 16 geospatial data visualization tasks, presented as a realistic collaborative workflow co-designed with geography and migration researchers, producing a taxonomy of user-defined gestures using tangibles and styluses for engaging with g...
K. P. Valdivia, Nujaimah Ahmed, Aswah Butt et al.· 0 citations
This paper introduces InSight, a benchmark for agentic claim verification over interactive visualizations and evaluates state-of-the-art models, revealing that interactive verification remains a non-trivial challenge.
Maeve Hutchinson, Syed Mahbubul Huq, Mohammad Albinhassan et al.· 0 citations
This paper describes the collection design, participants, recording setup, transcription format, and corpus statistics, and provides preliminary analyses to illustrate how TEIDAN can support research on turn-taking, addressee recognition, and multimodal grounding in human-human and human-agent interaction.
Taiga Mori, K. Inoue, Mikey Elmers et al.· Companion Publication of the...· 2 citations
These findings provide a representation-level analysis of LLM user simulators, confirming that assistant bias is structurally identifiable and that user behavior can be directionally analyzed.
Daeheon Jeong, Yoonjoo Lee, Eugene Choi et al.· 0 citations
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MemeBridge, a curated dataset centered on U.S.-originated memes, is introduced, designed to capture two complementary perspectives: how Chinese participants interpret these memes, and how U.S. participants anticipate how people from other cultures might misunderstand them.
Hangxiao Zhu, Ming Jiang, Yu Zhang· Knowledge Discovery and Data...· 1 citation
A two-level hierarchical reinforcement learning (HRL) framework for conversational agents that bridges the gap between existing token-level and utterance-level RL methods and introduces a dual-granularity reward mechanism that combines the utterance-level satisfaction score with token-level intrinsic self-consistency a...
Xiaoyu Wang, Qing-Qing Gu, Yue Zhao et al.· 0 citations
It is shown that commonly used EEG features can reveal participant identity and demographic attributes in addition to supporting the intended cognitive task, motivating purpose-limited representations and explicit privacy auditing in wearable neurohealth systems.
Sarmistha Sarna Gomasta, Bhawana Chhaglani, Prashant J. Shenoy· 0 citations
Anamnesis produces opinion distributions that more closely match real-world survey data than standard persona-prompting baselines, offering a transparent, reproducible, and open-source alternative to proprietary simulation services.
Song Yu, Joseph Suh, Serina Chang et al.· arXiv.org· 0 citations
DigitalCoach, a multimodal dataset of 72 human expert-novice computer use coaching sessions consisting of 22,752 dialogue turns grounded in 28.1 hours of screen and input event recordings across five software applications, evaluates whether state-of-the-art models can teach humans how to use computers.
Meng Chen, An-Ya Ji, Tsung-Han Wu et al.· arXiv.org· 0 citations
Generativism is proposed as a learning theory for the generative AI age and offers a foundation for rethinking instructional design, learning, assessment, and expertise development in contexts where generative AI plays an integral role in cognition.
An emotion-planning framework that determines the emotion prior to the textual generation, grounding the downstream emotional TTS in a streaming manner, and finally implements an entire streaming pipeline for real-time deployment.
Yue Zhao, Hongyang Li, Yong Chen et al.· Annual Meeting of the Associ...· 0 citations
This experiment examined how algorithmic advice affects human behavior in a classic economic game with a unique, non-collusive, and analytically traceable equilibrium, finding that algorithmic advice can function as a strategic signal, shaping coordination even without explicit communication.
T. Rebholz, Maxwell Uphoff, Christian H. R. Bernges 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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