Human-like cognition does not select past experience by topical similarity alone: affective significance and unresolved conflict also shape what becomes accessible. We present PsychoAgent, a cognitive architecture for LLM agents that separates factual and affective memory and integrates both through a conflict-aware ex...
Mohammad Amanlou, Parham Abed Azad, F. Davoodi et al.· 0 citations
The AppraiSal benchmark is introduced, containing 996 emotional support conversations with human-annotated mental states, including salient cognitive appraisal dimensions, and PRISM, a multi-agent probabilistic framework grounded in Bayesian Inverse Planning, designed to improve LLMs'ability to identify context-specifi...
Hainiu Xu, ZHAOYUE SUN, Hanqi Yan et al.· arXiv.org· 0 citations
It is argued that public tutoring benchmarks can better support positive-impact evaluation by reporting solving-oriented and pedagogy-oriented scores separately and by making disclosure-sensitive, student-agency-preserving criteria more explicit.
LALS (Latent Association Leaning Score) is introduced, a zero-shot metric that projects visual-token activations into the model's text-embedding space to measure concept associations per token and layer and shows that culturally loaded visual cues further modulate these internal associations.
Arnau Marin-Llobet, Simon Henniger, M. Banaji· arXiv.org· 1 citation
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It is found that AI assistance increases accuracy by 3-7% when participants viewed the relevant video segment, and by 27-35% when they did not, while efficiency increases by 10% for short videos and 25% for longer ones, and self-reported confidence in answers remains stable across all three conditions.
Anders Giovanni Møller, Elisa Bassignana, Francesco Pierri et al.· arXiv.org· 2 citations
TxSum is formulated, a new domain-grounded NLP task for DeFi transaction explanation, and MATEX, a grounded multi-agent framework for high-stakes transaction explanation is introduced, which achieves the strongest overall explanation quality.
Zifan Peng, Jingyi Zheng, Yule Liu et al.· 1 citation
D2Snap, an algorithm to downsample the DOM, premised on preserving actionability and actionability-discriminating features, is proposed and evaluated using a snapshot-variant web agent (GPT-4o) on a dataset sampled from Online-Mind2Web.
Thassilo M. Schiepanski, Nicholas Piël· arXiv.org· 4 citations
After interaction, users are still rating the pitch, not the product: user-elicited LLM evaluations, including the preference data driving public leaderboards, measure expectation management at least as much as the model itself.
Robert D. Morabito, Tyler McDonald, C. Viswanath et al.· arXiv.org· 0 citations
Pearmut is introduced, a lightweight yet feature-rich platform that makes end-to-end human evaluation as easy to run as automatic evaluation and enables reliable human evaluation to become a practical, routine component of model development and diagnosis rather than an occasional effort.
Vilém Zouhar, Tom Kocmi· arXiv.org· 11 citations· ⚡2
A clinician-support framework in which post-session interviewer ratings are combined with automatic language-based predictions to estimate patient-reported interaction quality in free clinical interviews is evaluated, suggesting that automatic language analysis and interviewer judgment capture complementary aspects of...
Ao-Wen Shi, Michal Balazia, Danilo Postin et al.· 0 citations
People share mental health diagnoses on social media, yet how such language becomes visible around their self-disclosure, and whether community engagement tracks it, remain unexamined across conditions. We analyze 89,605 Reddit posts from 739 users across eight conditions, removing each user's diagnosis disclosure and...
Renkai Ma, Lingyao Li, Shan-Ting Chen et al.· 0 citations
This work proposes the first taxonomy of LLM refusals that is grounded in pragmatic theory and finds that although models differ in how they refuse, their refusals are overall explicit and strongly morally evaluative, with interactional repair occurring mainly through offering or providing safer alternatives instead of...
Ruo-Xuan Li, Pin-Qiao Wang, Sheng Li et al.· 1 citation
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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