Cross-modal associations are systematic pairings of features across modalities, such as the association of'bouba'with round shapes and'kiki'with sharp shapes. Prior work has compared humans and vision-language models (VLMs) on such associations, but often using different stimuli or tasks between humans and models. Here...
Su-Min Hong, Katsumi Ibaraki, Renee Shi et al.· 0 citations
Masked diffusion language models (MDLMs) can generate text efficiently by predicting multiple masked tokens in parallel, but predictions from the same forward pass are not necessarily reliable when committed together. We study when parallel commitment is reliable. Our diagnostics show that confidence alone does not det...
Zheng-Hao He, Bo-Han Liu, Guang-Zhi Xiong et al.· 0 citations
Decision-only language models return a probability for every answer option instead of generating text, which makes them attractive as survey respondents and as judges. We audit the cultural values of one such model, TypeSafe's JEV, with the Values Survey Module 2013. We asked it the 24 items as 12 matched Saudi and 12...
Verbalization training is introduced, a method for making LLMs less reticent about verbalizing evaluation awareness while avoiding to supervise the latent belief itself, and it is shown that VT-induced verbalizations reflect the richer knowledge acquired by the model.
Usman Anwar, Sahar Abdelnabi, David Krueger· 0 citations
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This work proves that rank logarithmic in $M$ suffices for routing expressivity when the number of active experts is fixed, and is necessary up to precision factors, and introduces MoRE (Mixture of Rank-reduced-routed Experts), which factorizes the router weight matrix at rank r and reduces the routing cost to $O((h +...
A three-stage symbolic reasoning circuit (abstraction, induction, retrieval) is traced across shot counts in three model families and finds it is detectable and functional well before the model achieves high accuracy.
This work introduces Population Fidelity, an evaluation framework that distinguishes key conditions required for a set of LLM-generated responses to represent a population and organizes these features and provides reusable code, data, and trained models for evaluating population fidelity across substantive domains and...
Neemias B. da Silva, Martin Lukk, A. Sutani et al.· 0 citations
Large language models are increasingly evaluated in specialized domains such as law, medicine, software engineering, and cybersecurity, yet film remains comparatively underexplored despite requiring long-form narrative integration, multilingual interpretation, and culturally situated audience judgments. We introduce Ci...
Mir Tafseer Nayeem, Susmoy Chakraborty, Davood Rafiei· 0 citations
A preliminary extension to coding tasks shows that SCOUT can support safety verification beyond computer-use, andlations and analysis show that tool-free rubric generation in SCOUT elicits substantially more reasoning and is crucial for safety detection across verifier backbones, especially non-frontier ones.
Jian-Xing Chen, Xiao Yu, Shipra Agrawal et al.· 0 citations
ProVer, a framework that targets potentially pivotal decisions for fine-grained credit assignment in agentic reinforcement learning, is introduced, highlighting the effectiveness and efficiency of selectively targeting pivotal decisions for fine-grained credit assignment in agentic reinforcement learning.
Dongwon Jung, H. Ramesh, Yi-Fan Wang et al.· 0 citations
The results suggest that LLMs tend to present narratives of success by default, and that steering models toward honesty makes their reports substantially more transparent.
Jenny Y. Huang, Jia-Meng Fan, Ahmed Imtiaz Humayun et al.· 0 citations
Language models are frequently employed to evaluate other language models. An LM evaluator scoring agentic behaviors across multiple criteria is valuable, provided that its decisions align with human judgment. We call the problem of evaluating this alignment Meta-Evaluation. Tackling it directly is difficult: collectin...
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 24, 2026
