What happens when you stop throwing money at AI infrastructure and start thinking about architecture instead A third-party cost analysis priced the platform at $32,000. The prototype went from concept to production deployment in six hours. Total compute cost for the first full cognitive evaluation session: less than one cent. Those numbers are not a rounding error. They are the result of a fundamental architectural decision that most AI developers never consider: what if the intelligence isn’t in […]

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In quick-service restaurants, the drive-thru is no longer a supporting channel; it is the core revenue engine, accounting for as much as 70% of sales in many brands. Over the past decade, operators have invested heavily in optimizing throughput at the speaker box and efficiency in the kitchen, but the system has remained fundamentally fragmented. Each stage of the experience has been measured independently, leaving a critical gap in understanding what actually happens in the moments that determine […]

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The rapid growth of chemical literature has generated vast amounts of unstructured data, where reaction information is particularly valuable for applications such as reaction predictions and drug design. However, the prohibitive cost of expert annotation has led to a scarcity of training data, severely hindering the performance of automatic reaction extraction. In this work, we conduct a systematic study of active learning for chemical reaction extraction. We integrate six uncertainty- and diversity-based strategies with pretrained transformer-CRF architectures, and […]

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arXiv:2602.07059v1 Announce Type: new Abstract: Reproducibility is an important requirement in evolutionary computation, where results largely depend on computational experiments. In practice, reproducibility relies on how algorithms, experimental protocols, and artifacts are documented and shared. Despite growing awareness, there is still limited empirical evidence on the actual reproducibility levels of published work in the field. In this paper, we study the reproducibility practices in papers published in the Evolutionary Combinatorial Optimization and Metaheuristics track of the Genetic and […]

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Neural reasoners such as Tiny Recursive Models (TRMs) solve complex problems by combining neural backbones with specialized inference schemes. Such inference schemes have been a central component of stochastic reasoning systems, where inference rules are applied to a stochastic model to derive answers to complex queries. In this work, we bridge these two paradigms by introducing Recursive Inference Machines (RIMs), a neural reasoning framework that explicitly incorporates recursive inference mechanisms inspired by classical inference engines. We show that […]

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Word embeddings, attention, and positional encodings and the specific problem each one was built to solve. Blogging my understanding as I go through the lecture series of “CS 639: Introduction to Foundation Models” at University of Wisconsin-Madison. Photo by Gaelle Marcel on Unsplash How Do Machines Even Understand Words? Before we get into the fancy stuff, it’s worth pausing on this simple question. Text is just characters to a machine, so how do we get it to understand meaning? A […]

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ONLYOFFICE upgraded its API, introducing significant improvements across the Docs API, the Plugins and Macros API, and the Office JavaScript API. The release is designed to help developers build more capable integrations, automate complex document workflows, and reduce the friction involved in embedding the editors into third-party applications. One of enhancements to the ONLYOFFICE Docs API is memory leak fix in the destroyEditor method. It improves stability for applications that frequently create and remove editor instances, such as single-page […]

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The disintegration of liquid sheets into ligaments and droplets involves highly transient, multi-scale dynamics that are difficult to quantify from high-speed shadowgraphy images. Identifying droplets, ligaments, and blobs formed during breakup, along with tracking across frames, is essential for spray analysis. However, conventional multi-object tracking frameworks impose strict one-to-one temporal associations and cannot represent one-to-many fragmentation events. In this study, we present a two-stage deep learning framework for object detection and temporal relationship modeling across frames. The framework […]

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