arXiv:2602.22216v1 Announce Type: new Abstract: Accurate and efficient access to laboratory protocols is essential in Anatomical Pathology (AP), where up to 70% of medical decisions depend on laboratory diagnoses. However, static documentation such as printed manuals or PDFs is often outdated, fragmented, and difficult to search, creating risks of workflow errors and diagnostic delays. This study proposes and evaluates a Retrieval-Augmented Generation (RAG) assistant tailored to AP laboratories, designed to provide technicians with context-grounded answers to protocol-related queries. […]

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AI is the biggest user of the internet. Most emails are read first by AI. Most websites are crawled by AI before humans. Most API calls are software-to-software. Most content is consumed, parsed, and summarized by LLMs, then served back as a short answer. Within five years, we will go from 70% to 99% all decisions made by AI. We set the goals. AI will figure things out to achieve the goal. AI share of internet traffic 99% […]

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arXiv:2601.08856v1 Announce Type: new Abstract: Unit tests are critical in the hardware design lifecycle to ensure that component design modules are functionally correct and conform to the specification before they are integrated at the system level. Thus developing unit tests targeting various design features requires deep understanding of the design functionality and creativity. When one or more unit tests expose a design failure, the debugging engineer needs to diagnose, localize, and debug the failure to ensure design correctness, […]

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In-context reinforcement learning (ICRL) promises fast adaptation to unseen environments without parameter updates, but current methods either cannot improve beyond the training distribution or require near-optimal data, limiting practical adoption. We introduce SPICE, a Bayesian ICRL method that learns a prior over Q-values via deep ensemble and updates this prior at test-time using in-context information through Bayesian updates. To recover from poor priors resulting from training on sub-optimal data, our online inference follows an Upper-Confidence Bound rule that […]

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Suppose I give you a big number F and claim that F is a Fibonacci number. How could you confirm this? Before I go further, let me say what this post is really about. It’s not about Fibonacci numbers so much as it is about proofs and certificates. There’s no market for large Fibonacci numbers, and certainly no need to quickly verify that a number is a Fibonacci number. You could write a program to generate Fibonacci numbers, and run […]

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Building Harmless AI With Self-Critique and AI Feedback :::info Authors: Yuntao Bai Saurav Kadavath Sandipan Kundu Amanda Askell Jackson Kernion Andy Jones Anna Chen Anna Goldie Azalia Mirhoseini Cameron McKinnon Carol Chen Catherine Olsson Christopher Olah Danny Hernandez Dawn Drain Deep Ganguli Dustin Li Eli Tran-Johnson Ethan Perez Jamie Kerr Jared Mueller Jeffrey Ladish Joshua Landau Kamal Ndousse Kamile Lukosuite Liane Lovitt Michael Sellitto Nelson Elhage Nicholas Schiefer Noemi Mercado Nova DasSarma Robert Lasenby Robin Larson Sam Ringer […]

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arXiv:2602.21928v1 Announce Type: cross Abstract: Root cause analysis (RCA) in networked industrial systems, such as supply chains and power networks, is notoriously difficult due to unknown and dynamically evolving interdependencies among geographically distributed clients. These clients represent heterogeneous physical processes and industrial assets equipped with sensors that generate large volumes of nonlinear, high-dimensional, and heterogeneous IoT data. Classical RCA methods require partial or full knowledge of the system’s dependency graph, which is rarely available in these complex networks. […]

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arXiv:2601.20072v1 Announce Type: new Abstract: We address the challenge of training Vision Transformers (ViTs) when labeled data is scarce but unlabeled data is abundant. We propose Semi-Supervised Masked Autoencoder (SSMAE), a framework that jointly optimizes masked image reconstruction and classification using both unlabeled and labeled samples with dynamically selected pseudo-labels. SSMAE introduces a validation-driven gating mechanism that activates pseudo-labeling only after the model achieves reliable, high-confidence predictions that are consistent across both weakly and strongly augmented views of […]

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Anthropic’s chatbot Claude seems to have benefited from the attention around the company’s fraught negotiations with the Pentagon.

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