arXiv:2602.13323v1 Announce Type: new Abstract: The ability of autonomous systems to provide explanations is important for supporting transparency and aiding the development of (appropriate) trust. Prior work has defined a mechanism for Belief-Desire-Intention (BDI) agents to be able to answer questions of the form “why did you do action $X$?”. However, we know that we ask emph{contrastive} questions (“why did you do $X$ emph{instead of} $F$?”). We therefore extend previous work to be able to answer such questions. […]

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arXiv:2602.06773v1 Announce Type: cross Abstract: Multicalibration gradient boosting has recently emerged as a scalable method that empirically produces approximately multicalibrated predictors and has been deployed at web scale. Despite this empirical success, its convergence properties are not well understood. In this paper, we bridge the gap by providing convergence guarantees for multicalibration gradient boosting in regression with squared-error loss. We show that the magnitude of successive prediction updates decays at $O(1/sqrt{T})$, which implies the same convergence rate bound […]

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Service tags in Symfony are often misunderstood as merely a mechanism for Event Listeners or Twig Extensions. While they excel at those tasks, their true power lies in decoupling architecture. When wielded correctly, tags allow you to build systems that are open for extension but closed for modification (Open-Closed Principle) without touching a single line of configuration files. In this article, we will move beyond standard usage. We won’t just “tag a service”; we will build a robust, modular Document […]

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Table of Links Abstract and 1. Introduction Some recent trends in theoretical ML 2.1 Deep Learning via continuous-time controlled dynamical system 2.2 Probabilistic modeling and inference in DL 2.3 Deep Learning in non-Euclidean spaces 2.4 Physics Informed ML Kuramoto model 3.1 Kuramoto models from the geometric point of view 3.2 Hyperbolic geometry of Kuramoto ensembles 3.3 Kuramoto models with several globally coupled sub-ensembles Kuramoto models on higher-dimensional manifolds 4.1 Non-Abelian Kuramoto models on Lie groups 4.2 Kuramoto models […]

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arXiv:2602.12405v1 Announce Type: new Abstract: Reasoning about failures is crucial for building reliable and trustworthy robotic systems. Prior approaches either treat failure reasoning as a closed-set classification problem or assume access to ample human annotations. Failures in the real world are typically subtle, combinatorial, and difficult to enumerate, whereas rich reasoning labels are expensive to acquire. We address this problem by introducing ARMOR: Adaptive Round-based Multi-task mOdel for Robotic failure detection and reasoning. We formulate detection and reasoning […]

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Classical Federated Learning relies on a multi-round iterative process of model exchange and aggregation between server and clients, with high communication costs and privacy risks from repeated model transmissions. In contrast, one-shot federated learning (OFL) alleviates these limitations by reducing communication to a single round, thereby lowering overhead and enhancing practical deployability. Nevertheless, most existing one-shot approaches remain either impractical or constrained, for example, they often depend on the availability of a public dataset, assume homogeneous client models, […]

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arXiv:2601.17093v1 Announce Type: new Abstract: Comparing neural network representations is essential for understanding and validating models in scientific applications. Existing methods, however, often provide a limited view. We propose the Triangle of Similarity, a framework that combines three complementary perspectives: static representational similarity (CKA/Procrustes), functional similarity (Linear Mode Connectivity or Predictive Similarity), and sparsity similarity (robustness under pruning). Analyzing a range of CNNs, Vision Transformers, and Vision-Language Models using both in-distribution (ImageNetV2) and out-of-distribution (CIFAR-10) testbeds, our initial […]

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I wanted to follow up to yesterday’s thread and see if anyone wanted to weigh in on it. This work is far outside of my niche, but it strikes me as an attempt to reframe the issue instead of addressing concerns head on. The part that it bugging me is this: The true novelty of TurboQuant lies in our derivation of the exact distribution followed by the coordinates of rotated vectors, which we use to achieve optimal coordinate-wise […]

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TL;DR: We tested 12 C# Excel libraries against identical tasks: creating workbooks, reading large datasets, formatting cells, and exporting across platforms. This guide covers everything from MIT-licensed open-source options to enterprise-grade commercial suites, with side-by-side code, performance benchmarks, licensing costs, and a decision framework to help you pick the right library for your project. No single C# Excel library wins every scenario — the best choice depends on your budget, scale, and deployment target. We spent three weeks […]

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