This work reframing proposal generation as a time-sliced scientific forecasting problem, given a research question and inspiring papers available before a cutoff time, and operationalizes this objective with the Future Alignment Score (FAS), computed via retrieval and LLM-based semantic scoring against a held-out futur...
Heng Wang, Pengcheng Jiang, Jiashuo Sun et al.· arXiv.org· 5 citations
This work introduces a multi-agent framework designed to perform problem evolution while validating the solvability and increased difficulty of the generated problems, and provides empirical evidence that code-driven agents can serve as a viable mechanism for synthesizing high-difficulty mathematical reasoning problems...
Dadi Guo, Yue-Jin Xie, Qing-Yu Liu et al.· 0 citations
Presenting task-relevant information clearly is associated with better immediate revision performance and is favorably evaluated across learner profiles, while descriptive differences in evaluation patterns are observed, particularly for informational novelty and affective framing.
Momoka Furuhashi, Kouta Nakayama, N. Kawai et al.· 1 citation
This work leverage Mixture of Factor Analyzers (MFA) as a scalable, unsupervised alternative that models the activation space as a collection of Gaussian regions with their local covariance structure, and positions local geometry, expressed through subspaces, as a promising unit of analysis for scalable concept discove...
Or Shafran, S. Ronen, Omri Fahn et al.· arXiv.org· 10 citations· ⚡2
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On-policy Verbal Distillation is introduced, a framework that uses verbal scores from black-box teachers to rank student-generated sub-trajectories, retaining high-scoring ones and replacing low-scoring ones with teacher-generated continuations and suggests that retaining student-generated prefixes helps preserve explo...
Jing Xiong, Hui Shen, Shansan Gong et al.· arXiv.org· 8 citations
David-GRPO addresses the challenge of small language model agents under the budget constraint of commodity GPUs by placing a handful of off-policy expert trajectories into the GRPO groups of the early updates, and rewarding evidence coverage and resumes the most promising partial trajectory.
Hojae Han, Hee-Yeon Jung, Jongyoon Kim et al.· arXiv.org· 1 citation
Hallucination in large language models (LLMs) remains an acute concern, contributing to the spread of misinformation and diminished public trust, particularly in high-risk domains. Among hallucination types, factuality is crucial, as it concerns a model's alignment with established world knowledge. Adversarial factuali...
Adam Szelestey, Sofie van Engelen, Tianhao Huang et al.· 0 citations
Noise-Aware Verbal Confidence CAlibration Rules are proposed to provide a principled foundation for resolving overconfidence under noise and NOVA, a noise-aware calibration framework that synthesizes supervision from ~2K HotpotQA examples guided by these rules.
Judge Decoding accelerates LLM inference by relaxing the strict verification of Speculative Decoding, yet it typically relies on expensive and noisy supervision. In this work, we revisit this paradigm from first principles, revealing that the ``criticality'' scores learned via costly supervision are intrinsically encod...
Shengyin Sun, Yiming Li, Renxi Liu et al.· 0 citations
In this work, we examine hateful memes from three complementary angles - how to detect them, how to explain their content and how to intervene them before being posted - by applying a range of strategies built on top of generative AI models. To the best of our knowledge, explanation and intervention have typically been...
Naquee Rizwan, Subhankar Swain, Paramananda Bhaskar et al.· 4 citations
This work formalizes polarity-aware evidence grounding: linking each response sentence to its contextual evidence with a supporting, contradicting, or irrelevant label, and proposes eTracer, a post-hoc framework whose context-aware signed scorer recovers the full evidence map in a single forward pass per response.
Bo-Hao Chu, Qianli Wang, Hendrik Damm et al.· 0 citations
A human-annotated benchmark that grounds each summarized claim in both cited sentences and contributory phrases within them and introduces a decoupled evaluation framework that separately scores content, traceability, and locatability, together with a proxy for the amount of source text a clinician must inspect to veri...
Bo-Hao Chu, Hendrik Damm, T. Pakull et al.· 0 citations
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
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
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