January 2026

arXiv:2601.20021v1 Announce Type: new Abstract: Natural-language planning often involves vague predicates (e.g., suitable substitute, stable enough) whose satisfaction is inherently graded. Existing category-theoretic planners provide compositional structure and pullback-based hard-constraint verification, but treat applicability as crisp, forcing thresholding that collapses meaningful distinctions and cannot track quality degradation across multi-step plans. We propose Fuzzy Category-theoretic Planning (FCP), which annotates each action (morphism) with a degree in [0,1], composes plan quality via a t-norm Lukasiewicz, and retains crisp executability checks […]

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arXiv:2601.20016v1 Announce Type: new Abstract: Wikipedia administrators are vital to the platform’s success, performing over a million administrative actions annually. This multi-method study systematically analyzes adminship across 284 Wikipedia languages since 2018, revealing a critical two-sided trend: while over half of all Wikipedias show a net increase in administrators, almost two-thirds of highly active Wikipedias face decline. Our analysis, drawing from large-scale adminship log analysis, over 3000 surveys, and 12 interviews, reveals this decline is primarily driven by […]

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arXiv:2601.20015v1 Announce Type: new Abstract: DBTuneSuite is a suite of experiments on four widely deployed free database systems to test their performance under various query/upsert loads and under various tuning options. The suite provides: (i) scripts to generate data and to install and run tests, making it expandable to other tests and systems; (ii) suggestions of which systems work best for which query types; and (iii) quantitative evidence that tuning options widely used in practice can behave very […]

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arXiv:2601.20014v1 Announce Type: new Abstract: Inference-time planning with large language models frequently breaks under partial observability: when task-critical preconditions are not specified at query time, models tend to hallucinate missing facts or produce plans that violate hard constraints. We introduce textbf{Self-Querying Bidirectional Categorical Planning (SQ-BCP)}, which explicitly represents precondition status (texttt{Sat}/texttt{Viol}/texttt{Unk}) and resolves unknowns via (i) targeted self-queries to an oracle/user or (ii) emph{bridging} hypotheses that establish the missing condition through an additional action. SQ-BCP performs bidirectional search […]

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arXiv:2601.20013v1 Announce Type: new Abstract: This chapter offers a comprehensive introduction to the least-squares neural network (LSNN) method introduced in [14,16], for solving scalar first-order hyperbolic partial differential equations, specifically linear advection-reaction equations and nonlinear hyperbolic conservation laws. The LSNN method is built on an equivalent least-squares formulation of the underlying problem on an admissible solution set that accommodates discontinuous solutions. It employs ReLU neural networks (in place of finite elements) as the approximating functions, uses a carefully […]

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arXiv:2601.20010v1 Announce Type: new Abstract: When deciding how to solve complex problems, it seems important not only to know whether an intervention is helpful but also to understand why. Therefore, the present study investigated whether explicit information about causal mechanisms enables people to distinguish between multiple interventions. It was hypothesised that mechanism information helps them appreciate indirect interventions that treat the root causes of a problem instead of just fixing its symptoms. This was investigated in an experimental […]

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arXiv:2601.20009v1 Announce Type: new Abstract: Despite multilingual pretraining, large language models often struggle with non-English tasks, particularly in language control, the ability to respond in the intended language. We identify and characterize two key failure modes: the multilingual transfer bottleneck (correct language, incorrect task response) and the language consistency bottleneck (correct task response, wrong language). To systematically surface these issues, we design a four-scenario evaluation protocol spanning MMLU, MGSM, and XQuAD benchmarks. To probe these issues with interpretability, […]

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arXiv:2601.20006v1 Announce Type: new Abstract: The rapid progress of large language models has enabled the generation of text that closely resembles human writing, creating challenges for authenticity verification in education, publishing, and digital security. Detecting AI-generated text has therefore become a crucial technical and ethical issue. This paper presents a comprehensive study of AI-generated text detection based on large-scale corpora and novel training strategies. We introduce a 1-billion-token corpus of human-authored texts spanning multiple genres and a 1.9-billion-token […]

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arXiv:2601.20005v1 Announce Type: new Abstract: The urgent need for building decarbonization calls for a paradigm shift in future autonomous building energy operation, from human-intensive engineering workflows toward intelligent agents that interact with physics-grounded digital environments. This study proposes an end-to-end agentic AI-enabled Physics-Informed Machine Learning (PIML) environment for scalable building energy modeling, simulation, control, and automation. The framework consists of (1) a modular and physics-consistent PIML digital environment spanning building thermal dynamics, Heating, Ventilation, and Air Conditioning (HVAC), […]

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arXiv:2601.20000v1 Announce Type: new Abstract: In this paper, we propose an adaptive high-order method for hyperbolic systems of conservation laws. The proposed method is based on a dual formulation approach: Two numerical solutions, corresponding to conservative and nonconservative formulations of the same system, are evolved simultaneously. Since nonconservative schemes are known to produce nonphysical weak solutions near discontinuities, we exploit the difference between these two solutions to construct a smoothness indicator (SI). In smooth regions, the difference between […]

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