arXiv:2601.15360v1 Announce Type: new Abstract: Estimating Heterogeneous Treatment Effects (HTE) in industrial applications such as AdTech and healthcare presents a dual challenge: extreme class imbalance and heavy-tailed outcome distributions. While the X-Learner framework effectively addresses imbalance through cross-imputation, we demonstrate that it is fundamentally vulnerable to “Outlier Smearing” when reliant on Mean Squared Error (MSE) minimization. In this failure mode, the bias from a few extreme observations (“whales”) in the minority group is propagated to the entire majority […]

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Linear systems with nonnegative or positive coefficients play a central role in the analysis of dynamical processes where admissible states are required to be positive. This paper studies a linear system governed by a positive matrix and interprets it as a spec-tral problem motivated by a subsidy allocation model. The analysis is carried out within the framework of the Perron–Frobenius theory and relies on classical results of linear algebra, in particular Perron’s theorem and Wielandt’s lemma. Using purely […]

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arXiv:2602.04932v1 Announce Type: new Abstract: Hyperbolic representation learning has been widely used to extract implicit hierarchies within data, and recently it has found its way to the open-world classification task of Generalized Category Discovery (GCD). However, prior hyperbolic GCD methods only use hyperbolic geometry for representation learning and transform back to Euclidean geometry when clustering. We hypothesize this is suboptimal. Therefore, we present Hyperbolic Clustered GCD (HC-GCD), which learns embeddings in the Lorentz Hyperboloid model of hyperbolic geometry, […]

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Introducing GPT-5.3-Codex-Spark—our first real-time coding model. 15x faster generation, 128k context, now in research preview for ChatGPT Pro users.

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In Part 3, we have reviewed models in the R-CNN family. All of them are region-based object detection algorithms. They can achieve high accuracy but could be too slow for certain applications such as autonomous driving. In Part 4, we only focus on fast object detection models, including SSD, RetinaNet, and models in the YOLO family.

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Executive Summary The era of the passive ‘recommendation engine’ is over. We are entering the age of Agentic  Commerce—where AI doesn’t just suggest, but acts with autonomy. Retail is entering a new  phase—Agentic Commerce—where artificial intelligence no longer merely suggests products,  but acts autonomously on behalf of consumers and enterprises. In this model, intelligent agents understand intent, evaluate constraints, reason across  alternatives, and execute end-to-end commerce workflows. For consumers, this means delegating  shopping decisions to trusted digital agents. […]

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Combinatorial optimization augmented machine learning (COAML) has recently emerged as a powerful paradigm for integrating predictive models with combinatorial decision-making. By embedding combinatorial optimization oracles into learning pipelines, COAML enables the construction of policies that are both data-driven and feasibility-preserving, bridging the traditions of machine learning, operations research, and stochastic optimization. This paper provides a comprehensive overview of the state of the art in COAML. We introduce a unifying framework for COAML pipelines, describe their methodological building blocks, […]

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Although the bill’s prospects are uncertain, New York is at least the sixth state to consider pausing construction of new data centers.

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As neural theorem provers become increasingly agentic, the ability to interpret and act on compiler feedback is critical. However, existing Lean datasets consist almost exclusively of correct proofs, offering little supervision for understanding and repairing failures. We study Lean proof repair as a supervised learning problem: given an erroneous proof and compiler feedback, predict both a corrected proof and a natural-language diagnosis grounded in the same feedback. We introduce APRIL (Automated Proof Repair in Lean), a dataset of […]

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