arXiv:2503.02108v2 Announce Type: replace-cross Abstract: Generalized Bayesian Inference (GBI) provides a flexible framework for updating prior distributions using various loss functions instead of the traditional likelihoods, thereby enhancing the model robustness to model misspecification. However, GBI often suffers the problem associated with intractable likelihoods. Kernelized Stein Discrepancy (KSD), as utilized in a recent study, addresses this challenge by relying only on the gradient of the log-likelihood. Despite this innovation, KSD-Bayes suffers from critical pathologies, including insensitivity to well-separated […]

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Automated blood morphology analysis can support hematological diagnostics in low- and middle-income countries (LMICs) but remains sensitive to dataset shifts from staining variability, imaging differences, and rare morphologies. Building centralized datasets to capture this diversity is often infeasible due to privacy regulations and data-sharing restrictions. We introduce a federated learning framework for white blood cell morphology analysis that enables collaborative training across institutions without exchanging training data. Using blood films from multiple clinical sites, our federated models learn […]

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{“document”:[{“e”:”par”,”c”:[{“e”:”text”,”t”:”Recent advances in reinforcement learning for code generation have made robust environments essential to prevent reward hacking. As LLMs increasingly serve as evaluators in code-based RL, their ability to detect reward hacking remains understudied. In this paper, we propose a novel taxonomy of reward exploits spanning across 54 categories and introduce TRACE (Testing Reward Anomalies in Code Environments), a synthetically curated and human-verified benchmark containing 517 testing trajectories. Unlike prior work that evaluates reward hack detection in isolated […]

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A new wave of work on covariance cleaning and nonlinear shrinkage has delivered asymptotically optimal analytical solutions for large covariance matrices. Building on this progress, these ideas have been generalized to empirical cross-covariance matrices, whose singular-value shrinkage characterizes comovements between one set of assets and another. Existing analytical cross-covariance cleaners are derived under strong stationarity and large-sample assumptions, and they typically rely on mesoscopic regularity conditions such as bounded spectra; macroscopic common modes (e.g., a global market factor) […]

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We provide a complete theory of optimal universal rates for binary classification in the agnostic setting. This extends the realizable-case theory of Bousquet, Hanneke, Moran, van Handel, and Yehudayoff (2021) by removing the realizability assumption on the distribution. We identify a fundamental tetrachotomy of optimal rates: for every concept class, the optimal universal rate of convergence of the excess error rate is one of $e^{-n}$, $e^{-o(n)}$, $o(n^{-1/2})$, or arbitrarily slow. We further identify simple combinatorial structures which determine […]

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arXiv:2601.05445v1 Announce Type: new Abstract: Large Language Models (LLMs) face a significant threat from multi-turn jailbreak attacks, where adversaries progressively steer conversations to elicit harmful outputs. However, the practical effectiveness of existing attacks is undermined by several critical limitations: they struggle to maintain a coherent progression over long interactions, often losing track of what has been accomplished and what remains to be done; they rely on rigid or pre-defined patterns, and fail to adapt to the LLM’s dynamic […]

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arXiv:2601.12234v1 Announce Type: new Abstract: Generating 3D models has traditionally been a complex task requiring specialized expertise. While recent advances in generative AI have sought to automate this process, existing methods produce non-editable representation, such as meshes or point clouds, limiting their adaptability for iterative design. In this paper, we introduce Proc3D, a system designed to generate editable 3D models while enabling real-time modifications. At its core, Proc3D introduces procedural compact graph (PCG), a graph representation of 3D […]

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arXiv:2601.09739v1 Announce Type: new Abstract: Sabam, a Belgian collective rights management organisation, wanted an internet access provider and a social network site to install a filter system to enforce copyrights. In two recent judgments, the Court of Justice of the European Union decided that the social network site and the internet access provider cannot be required to install the filter system that Sabam asked for. Are these judgments good news for fundamental rights? This article argues that little […]

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In this paper we propose a new classification algorithm designed for application on complex networks motivated by algorithmic similarities between boosting learning and message passing. We consider a network classifier as a logistic regression where the variables define the nodes and the interaction effects define the edges. From this definition we represent the problem as a factor graph of local exponential loss functions. Using the factor graph representation it is possible to interpret the network classifier as an […]

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Image-to-PointCloud place recognition is vital for autonomous systems, yet faces challenges from the inherent modality gap and drastic environmental variations. We propose Cross-Modal Invariant Representation Learning (CMIRL) to learn highly invariant cross-modal global descriptors. CMIRL introduces an Adaptive Cross-Modal Alignment (ACMA) module, which dynamically projects point clouds based on image semantics to generate view-optimized dense depth maps. A Dual-Stream Invariant Feature Encoder, featuring a Transformer-based Cross-Modal Attention Fusion (CMAF) module, then explicitly learns and emphasizes features shared across […]

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