Covering electricity price increases from our data centers One of the sub-threads of the AI energy usage discourse has been the impact new data centers have on the cost of electricity to nearby residents. Here’s detailed analysis from Bloomberg in September reporting “Wholesale electricity costs as much as 267% more than it did five years ago in areas near data centers”. Anthropic appear to be taking on this aspect of the problem directly, promising to cover 100% of […]

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The Pentagon has officially designated Anthropic a supply-chain risk after the two failed to agree on how much control the military should have over its AI models, including its use in autonomous weapons and mass domestic surveillance. As Anthropic’s $200 million contract fell apart, the DoD turned to OpenAI instead, which accepted and then watched ChatGPT uninstalls surge 295%. As the stakes keep rising, the question remains: how much unrestricted […]

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arXiv:2601.12145v1 Announce Type: new Abstract: Softmax attention struggles with long contexts due to structural limitations: the strict sum-to-one constraint forces attention sinks on irrelevant tokens, and probability mass disperses as sequence lengths increase. We tackle these problems with Threshold Differential Attention (TDA), a sink-free attention mechanism that achieves ultra-sparsity and improved robustness at longer sequence lengths without the computational overhead of projection methods or the performance degradation caused by noise accumulation of standard rectified attention. TDA applies row-wise […]

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Most automated pricing systems are built to optimize. n Very few are built to survive failure. When a system manages 1,000,000+ SKUs and processes 500,000 daily price updates across multiple marketplaces, pricing stops being a growth feature. It becomes financial infrastructure. At that scale: A single incorrect price can erase 30% margin instantly A misinterpreted API response can affect thousands of SKUs within minutes A faulty rollout can create $15,000–$20,000 daily exposure per seller The problem is not […]

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Tabular foundation models aim to make machine learning accessible for enterprise data without task-specific training. This paper presents the first independent evaluation of SAP’s Retrieval Pretrained Transformer (RPT-1) from a practitioner perspective. RPT-1 is a compact 64.6 MB model pretrained on 1.34 TB of structured data across 3.1 million tables. We benchmark it against tuned gradient-boosted decision trees (XGBoost, LightGBM, CatBoost) on three SAP business scenarios: demand forecasting across SD/MM/PP modules, predictive data integrity in BC/MM/QM, and financial […]

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For the last 20 years, the dogma of software engineering was simple: “Good code documents itself.” We were taught that comments are a “code smell” (Uncle Bob told us so). We were taught that if you need to explain it, you failed to name it properly. We were taught to DRY (Don’t Repeat Yourself) everything into oblivion. But in 2024, the game changed. You aren’t just writing code for your future self or your colleagues anymore. You are […]

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arXiv:2602.17843v1 Announce Type: new Abstract: In this work, we design and analyze a novel, provably conditionally stable, weakly coupled partitioned scheme to solve the conjugate heat transfer (CHT) problem. We consider a model CHT problem consisting of linear advection-diffusion and heat equations, coupled at an interface through continuity of temperature and heat flux. We employ high-order summation-by-parts finite-difference operators in conjunction with simultaneous-approximation-terms (SATs) in curvilinear coordinates for spatial derivatives, combined with first- and second-order time discretizations and […]

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arXiv:2601.13272v1 Announce Type: cross Abstract: We develop a multilevel Monte Carlo (MLMC) framework for uncertainty quantification with Monte Carlo dropout. Treating dropout masks as a source of epistemic randomness, we define a fidelity hierarchy by the number of stochastic forward passes used to estimate predictive moments. We construct coupled coarse–fine estimators by reusing dropout masks across fidelities, yielding telescoping MLMC estimators for both predictive means and predictive variances that remain unbiased for the corresponding dropout-induced quantities while reducing […]

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