This study aims to prove the Riemann Hypothesis and the Generalized Riemann Hypothesis by ex-tending the Riemann zeta function and Dirichlet L -functions to the elliptic complex domain, based ona newly constructed system of elliptic complex numbers Cλ(λ < 0) . The core challenge addressed is theinherent difficulty in resolving these conjectures within the traditional ”circular complex domain” frame-work (λ = −1); the author posits that a complete proof is unattainable strictly within this conventionalsetting.The primary innovation of […]

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Machine unlearning is rapidly becoming a practical requirement, driven by privacy regulations, data errors, and the need to remove harmful or corrupted training samples. Despite this, most existing methods tackle the problem purely from a post-hoc perspective. They attempt to erase the influence of targeted training samples through parameter updates that typically require access to the full training data. This creates a mismatch with real deployment scenarios where unlearning requests can be anticipated, revealing a fundamental limitation of […]

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arXiv:2602.06221v1 Announce Type: new Abstract: Multiple-choice question answering (MCQA) is standard in NLP, but benchmarks lack rigorous quality control. We present BenchMarker, an education-inspired toolkit using LLM judges to flag three common MCQ flaws: 1) contamination – items appearing exactly online; 2) shortcuts – cues in the choices that enable guessing; and 3) writing errors – structural/grammatical issues based on a 19-rule education rubric. We validate BenchMarker with human annotations, then run the tool to audit 12 benchmarks, […]

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arXiv:2601.02384v1 Announce Type: new Abstract: The development of the digital economy has established e-commerce platforms as the primary space for commercial transactions for the Muslim community. However, innovations in features and business models on these platforms have gave rise to Sharia issues that cannot be fully explained through conventional Fiqh Muamalah contract frameworks. This research aims to examine the compliance of transaction practices in e-commerce with Sharia principles, particularly in the six most frequently used transaction forms, namely […]

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arXiv:2603.22404v1 Announce Type: new Abstract: Consider a market of competing model providers selling query access to models with varying costs and capabilities. Customers submit problem instances and are willing to pay up to a budget for a verifiable solution. An arbitrageur efficiently allocates inference budget across providers to undercut the market, thus creating a competitive offering with no model-development risk. In this work, we initiate the study of arbitrage in AI model markets, empirically demonstrating the viability of […]

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arXiv:2601.15455v1 Announce Type: new Abstract: In their 1991 paper “Algebraic Reconstruction of Types and Effects,” Pierre Jouvelot and David Gifford presented a type-and-effect reconstruction algorithm based on an algebraic structure of effects. Their work is considered a milestone in the development of type-and-effect systems, and has inspired numerous subsequent works in the area of static analysis. However, unlike the later research it spawned, the original algorithm considered a language with higher-rank polymorphism, a feature which is challenging to […]

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arXiv:2602.00982v1 Announce Type: new Abstract: Visual robustness and neural alignment remain critical challenges in developing artificial agents that can match biological vision systems. We present the winning approaches from Team HCMUS_TheFangs for both tracks of the NeurIPS 2025 Mouse vs. AI: Robust Visual Foraging Competition. For Track 1 (Visual Robustness), we demonstrate that architectural simplicity combined with targeted components yields superior generalization, achieving 95.4% final score with a lightweight two-layer CNN enhanced by Gated Linear Units and observation […]

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arXiv:2602.15962v1 Announce Type: new Abstract: Holstein-Friesian detection and re-identification (Re-ID) methods capture individuals well when targets are spatially separate. However, existing approaches, including YOLO-based species detection, break down when cows group closely together. This is particularly prevalent for species which have outline-breaking coat patterns. To boost both effectiveness and transferability in this setting, we propose a new detect-segment-identify pipeline that leverages the Open-Vocabulary Weight-free Localisation and the Segment Anything models as pre-processing stages alongside Re-ID networks. To evaluate […]

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