Traditional machine learning systems are typically designed for static data distributions, which suffer from catastrophic forgetting when learning from evolving data streams. Class-Incremental Learning (CIL) addresses this challenge by enabling learning systems to continuously learn new classes while preserving prior knowledge. With the rise of pre-trained models (PTMs) such as CLIP, leveraging their strong generalization and semantic alignment capabilities has become a promising direction in CIL. However, existing CLIP-based CIL methods are often scattered across disparate codebases, rely […]

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We study off-policy reinforcement learning for controlling continuous-time Markov diffusion processes with discrete-time observations and actions. We consider model-free algorithms with function approximation that learn value and advantage functions directly from data, without unrealistic structural assumptions on the dynamics. Leveraging the ellipticity of the diffusions, we establish a new class of Hilbert-space positive definiteness and boundedness properties for the Bellman operators. Based on these properties, we propose the Sobolev-prox fitted $q$-learning algorithm, which learns value and advantage functions […]

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The nontrivial zeros of the Riemann zeta function are parameterized by the spectral variable ( sinmathbb{C} ), and the isomonodromic deformation parameter t of the Painlevé III equation of type ( D_6 ) is connected to s by ( t=s(1-s) ), which maps the critical line ( Re(s)=frac12 ) to the positive real ray ( tin[frac14,infty) ). Any de Branges realization of the Riemann Hypothesis within this framework requires four explicit conditions: (C1) geometric feasibility —the positive lambda-length […]

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Every enterprise revenue team has seen the demonstration at some point. The AI suggests a price, routes an approval, and flags a risk with apparent confidence, and the room is impressed by the fluency of it. What happens six months after go-live tends to be a different conversation, where the team is managing exceptions, investigating outputs that conflict with internal policy, and reconciling AI-generated recommendations against business rules the system had no reliable way to access in the […]

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arXiv:2603.17047v1 Announce Type: new Abstract: We study $(alpha,beta)$-spanners for weighted graphs. We propose a simple greedy completion procedure which starts from a sparse initial graph, and repeatedly fixes pairs of vertices with a bad stretch, generalizing Kunedsen’s additive completion [SWAT ’14]. As an application, we construct $(k,k-1)$-spanners for weighted graphs of size $tilde{O}(n^{1+1/k})$, which were previously unknown.

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arXiv:2602.03924v1 Announce Type: new Abstract: Deep learning has revolutionized weather and climate modeling, yet the current landscape remains fragmented: highly specialized models are typically trained individually for distinct tasks. To unify this landscape, we introduce WIND, a single pre-trained foundation model capable of replacing specialized baselines across a vast array of tasks. Crucially, in contrast to previous atmospheric foundation models, we achieve this without any task-specific fine-tuning. To learn a robust, task-agnostic prior of the atmosphere, we pre-train […]

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arXiv:2602.20273v1 Announce Type: new Abstract: Large language models (LLMs) have been reported to linearly encode truthfulness, yet recent work questions this finding’s generality. We reconcile these views with the truthfulness spectrum hypothesis: the representational space contains directions ranging from broadly domain-general to narrowly domain-specific. To test this hypothesis, we systematically evaluate probe generalization across five truth types (definitional, empirical, logical, fictional, and ethical), sycophantic and expectation-inverted lying, and existing honesty benchmarks. Linear probes generalize well across most domains […]

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arXiv:2601.11701v1 Announce Type: cross Abstract: Algorithmic stability is a central concept in statistics and learning theory that measures how sensitive an algorithm’s output is to small changes in the training data. Stability plays a crucial role in understanding generalization, robustness, and replicability, and a variety of stability notions have been proposed in different learning settings. However, while stability entails desirable properties, it is typically not sufficient on its own for statistical learning — and indeed, it may be […]

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arXiv:2602.06276v1 Announce Type: cross Abstract: We address the problem of training conversion prediction models in advertising domains under privacy constraints, where direct links between ad clicks and conversions are unavailable. Motivated by privacy-preserving browser APIs and the deprecation of third-party cookies, we study a setting where the learner observes a sequence of clicks and a sequence of conversions, but can only link a conversion to a set of candidate clicks (an attribution set) rather than a unique source. […]

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