The AI for Science Forum: A new era of discovery
Google DeepMind and the Royal Society are co-hosting the AI for Science Forum to explore how AI is rapidly accelerating science.
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Propositional Abduction via Only-Knowing: A Non-Monotonic Approach
arXiv:2601.04272v1 Announce Type: new Abstract: The paper introduces a basic logic of knowledge and abduction by extending Levesque logic of only-knowing with an abduction modal operator defined via the combination of basic epistemic concepts. The upshot is an alternative approach to abduction that employs a modal vocabulary and explores the relation between abductive reasoning and epistemic states of only knowing. Furthermore, by incorporating a preferential relation into modal frames, we provide a non-monotonic extension of our basic framework […]
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Sparse Bayesian Message Passing under Structural Uncertainty
arXiv:2601.01207v1 Announce Type: cross Abstract: Semi-supervised learning on real-world graphs is frequently challenged by heterophily, where the observed graph is unreliable or label-disassortative. Many existing graph neural networks either rely on a fixed adjacency structure or attempt to handle structural noise through regularization. In this work, we explicitly capture structural uncertainty by modeling a posterior distribution over signed adjacency matrices, allowing each edge to be positive, negative, or absent. We propose a sparse signed message passing network that […]
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NVIDIA Unveils Nemotron 3 Family of Open Models for Agentic AI Development
Key Highlights: NVIDIA has been among the top AI companies that is helping other AI giants with its infrastructure and GPU building capabilities that can run datacenters efficiently. Starting today, NVIDIA is also doubling down on open, agentic AI with the launch of Nemotron 3. It’s a new family of open models, datasets, and tools designed to power large-scale multi-agent systems across different industries. NVIDIA Nemotron 3 lineup targets efficiency and scale across AI workloads According to NVIDIA, […]
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What Makes Looped Transformers Perform Better Than Non-Recursive Ones
arXiv:2510.10089v3 Announce Type: replace-cross Abstract: While looped transformers (termed as Looped-Attn) often outperform standard transformers (termed as Single-Attn) on complex reasoning tasks, the mechanism for this advantage remains underexplored. In this paper, we explain this phenomenon through the lens of loss landscape geometry, inspired by empirical observations of their distinct dynamics at both sample and Hessian levels. To formalize this, we extend the River-Valley landscape model by distinguishing between U-shaped valleys (flat) and V-shaped valleys (steep). Based on […]
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5 Fun Docker Projects for Absolute Beginners
Learn Docker by doing with five beginner-friendly projects covering hosting, multi-container apps, CI, and monitoring.
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Most Replayed Moment: Confidence Can Be Taught! Use These Body Language Cues To Your Advantage!
Joe Navarro is a former FBI agent and one of the world’s leading experts in body language and nonverbal communication. In this Moment, Joe reveals the hidden signals behind body language and how to use nonverbal cues, such as posture and eye contact, to your advantage in business, relationships, and beyond. Listen to the full episode with Joe Navarro on The Diary of a CEO below: Spotify: https://g2ul0.app.link/01Qhc2kbPYb Apple: https://g2ul0.app.link/NwkCj5obPYb Watch the Episodes On YouTube:https://www.youtube.com/c/%20TheDiaryOfACEO/videos Joe Navarro: https://www.jnforensics.com/
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Aligned explanations in neural networks
arXiv:2601.04378v1 Announce Type: cross Abstract: Feature attribution is the dominant paradigm for explaining deep neural networks. However, most existing methods only loosely reflect the model’s prediction-making process, thereby merely white-painting the black box. We argue that explanatory alignment is a key aspect of trustworthiness in prediction tasks: explanations must be directly linked to predictions, rather than serving as post-hoc rationalizations. We present model readability as a design principle enabling alignment, and PiNets as a modeling framework to pursue […]
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Avoiding the Price of Adaptivity: Inference in Linear Contextual Bandits via Stability
arXiv:2512.20368v2 Announce Type: replace Abstract: Statistical inference in contextual bandits is challenging due to the adaptive, non-i.i.d. nature of the data. A growing body of work shows that classical least-squares inference can fail under adaptive sampling, and that valid confidence intervals for linear functionals typically require an inflation of order $sqrt{d log T}$. This phenomenon — often termed the price of adaptivity — reflects the intrinsic difficulty of reliable inference under general contextual bandit policies. A key structural […]
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