Identifying physical laws from noisy observational data is a central challenge in scientific machine learning. We present Minimum-Action Learning (MAL), a framework that selects symbolic force laws from a pre-specified basis library by minimizing a Triple-Action functional combining trajectory reconstruction, architectural sparsity, and energy-conservation enforcement. A wide-stencil acceleration-matching technique reduces noise variance by 10,000x, transforming an intractable problem (SNR ~0.02) into a learnable one (SNR ~1.6); this preprocessing is the critical enabler shared by all methods tested, including […]

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Combination pharmacotherapy offers substantial therapeutic advantages but also poses substantial risks of adverse drug reactions (ADRs). The accurate prediction of ADRs with interpretable computational methods is crucial for clinical safety management, drug development, and precision medicine. However, managing ADRs remains a challenge due to the vast search space of drug combinations and the complexity of physiological responses. Current graph-based architectures often struggle to effectively integrate multi-scale biological information and frequently rely on fixed association matrices, which limits their […]

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Victoria, Seychelles, February 5, 2026 MEXC, the world’s fastest-growing digital asset exchange and a pioneer of true zero-fee trading, announced the official launch of the Commodity Zero-Fee Gala. It offers zero-fee trading on commodity assets including gold and silver, high-yield staking opportunities, and $1 million in trading rewards. Amid recent activity in global commodity markets, particularly in gold and silver, traders’ demand for diversified assets and strategies is rising. MEXC is expanding user access to tokenized gold, silver, […]

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With an overflowing war chest from its recent $5 billion raise, Databricks is buying startups and looking for more. It acquired Antimatter and SiftD.ai.

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arXiv:2602.17729v1 Announce Type: new Abstract: Across academia, industry, and government, “AI” has become central in research and development, regulatory debates, and promises of ever faster and more capable decision-making and action. In numerous domains, especially safety-critical ones, there are significant concerns over how “AI” may affect decision-making, responsibility, or the likelihood of mistakes (to name only a few categories of critique). However, for most critiques, the target is generally “AI”, a broad term admitting many (types of) systems […]

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Despite the state-of-the-art performance of large language models (LLMs) across diverse tasks, their susceptibility to adversarial attacks and unsafe content generation remains a major obstacle to deployment, particularly in high-stakes settings. Addressing this challenge requires safety mechanisms that are both practically effective and supported by rigorous theory. We introduce BarrierSteer, a novel framework that formalizes response safety by embedding learned non-linear safety constraints directly into the model’s latent representation space. BarrierSteer employs a steering mechanism based on Control […]

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arXiv:2601.04211v1 Announce Type: new Abstract: We present Qwerty AI, an end-to-end system for automated age-rating and content-safety assessment of Russian-language screenplays according to Federal Law No. 436-FZ. The system processes full-length scripts (up to 700 pages in under 2 minutes), segments them into narrative units, detects content violations across five categories (violence, sexual content, profanity, substances, frightening elements), and assigns age ratings (0+, 6+, 12+, 16+, 18+) with explainable justifications. Our implementation leverages a fine-tuned Phi-3-mini model with […]

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We study the canonical string-based Assembly Index (ASI), defined as the minimum number of binary concatenations needed to construct a target word under full reuse. NP-completeness of ASI-DEC over general finite alphabets and an equivalence between ASI plans and straight-line programs (SLPs) under the same size convention has been established. We emphasize that all transfers between decision variants are effected by explicit polynomial-time mappings and (where needed) an explicit reparameterization of the threshold by an absolute constant or […]

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arXiv:2410.06378v2 Announce Type: replace Abstract: Covering numbers of (deep) ReLU networks have been used to characterize approximation-theoretic performance, to upper-bound prediction error in nonparametric regression, and to quantify classification capacity. These results rely on covering number upper bounds obtained via explicit constructions of coverings. Lower bounds on covering numbers do not appear to be available in the literature. The present paper fills this gap by deriving tight (up to multiplicative constants) lower and upper bounds on the metric […]

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