Average-reward reinforcement learning offers a principled framework for long-term decision-making by maximizing the mean reward per time step. Although Q-learning is a widely used model-free algorithm with established sample complexity in discounted and finite-horizon Markov decision processes (MDPs), its theoretical guarantees for average-reward settings remain limited. This work studies a simple but effective Q-learning algorithm for average-reward MDPs with finite state and action spaces under the weakly communicating assumption, covering both single-agent and federated scenarios. For the single-agent […]

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arXiv:2602.03730v1 Announce Type: new Abstract: Generative models trained using self-supervision of tokenized electronic health record (EHR) timelines show promise for clinical outcome prediction. This is typically done using Monte Carlo simulation for future patient trajectories. However, existing approaches suffer from three key limitations: sparse estimate distributions that poorly differentiate patient risk levels, extreme computational costs, and high sampling variance. We propose two new estimators: the Sum of Conditional Outcome Probability Estimator (SCOPE) and Risk Estimation from Anticipated Conditional […]

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When daily urgencies and immediate results take priority over long-term goals, when quick fixes are the default mode of execution instead of big-picture thinking or when you spend most of your time firefighting issues—putting out fires, responding to crises and reacting to challenges that surface unexpectedly—it may feel like you’re accomplishing a lot. Problems are being solved, challenges are being resolved and things are getting done. You’re highly engaged, super involved and extremely busy in trying to keep […]

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arXiv:2601.02377v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into robotics has revolutionized their ability to interpret complex human commands and execute sophisticated tasks. However, such paradigm shift introduces critical security vulnerabilities stemming from the ”embodiment gap”, a discord between the LLM’s abstract reasoning and the physical, context-dependent nature of robotics. While security for text-based LLMs is an active area of research, existing solutions are often insufficient to address the unique threats for the embodied […]

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We study the problem of learning Transformer-based sequence models with black-box access to their outputs. In this setting, a learner may adaptively query the oracle with any sequence of vectors and observe the corresponding real-valued output. We begin with the simplest case, a single-head softmax-attention regressor. We show that for a model with width $d$, there is an elementary algorithm to learn the parameters of single-head attention exactly with $O(d^2)$ queries. Further, we show that if there exists […]

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After a years-long battle, the European Commission’s “Chat Control” plan, which would mandate mass scanning and other encryption-breaking measures, at last codifies agreement on a position within the Council of the EU, representing EU States. The good news is that the most controversial part, the forced requirement to scan encrypted messages, is out. The bad news is there’s more to it than that. Chat Control has gone through several iterations since it was first introduced, with the EU […]

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I used to think that driving big impact came from shipping really big projects.  I was wrong. The big projects are certainly what everyone talks about. These are what get added to your resume and mentioned in company all hands decks. If you look at the following projects, you’ll probably reach the same conclusion. Big project – takes 3 months to build and produces 5 “points” of value Small project – takes 1 month to build and produces 1 […]

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Large pretrained language models are trained over a sizable collection of online data. They unavoidably acquire certain toxic behavior and biases from the Internet. Pretrained language models are very powerful and have shown great success in many NLP tasks. However, to safely deploy them for practical real-world applications demands a strong safety control over the model generation process.

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Primary bottleneck for Enterprise AI is not the availability of tools or the identification of a tech stack, it is getting the data landscape in order. Success in 2026 is predicated on having total clarity of the underlying data infrastructure and establishing a foundation that is petabyte-scale, secure, and high-performing. Without a reliable data layer, AI initiatives remain experimental rather than transformational. Foundation (Scalable and Maintainable Data Acquisition) A useful litmus test for the engineering foundation is time […]

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