arXiv:2601.19312v1 Announce Type: cross Abstract: The Schrodinger Bridge and Bass (SBB) formulation, which jointly controls drift and volatility, is an established extension of the classical Schrodinger Bridge (SB). Building on this framework, we introduce LightSBB-M, an algorithm that computes the optimal SBB transport plan in only a few iterations. The method exploits a dual representation of the SBB objective to obtain analytic expressions for the optimal drift and volatility, and it incorporates a tunable parameter beta greater than […]

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Enterprise AI has moved from isolated prototypes to systems that shape real decisions: drafting customer responses, summarising internal knowledge, generating code, accelerating research, and powering agent workflows that can trigger actions in business systems. That creates a new security surface, one that sits between people, proprietary data, and automated execution. AI security tools exist to make those questions operational. Some focus on governance and discovery. Others harden AI applications and agents at runtime. Some emphasise testing and red […]

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Despite the ratification of regional integration frameworks such as the Lisbon Recognition Convention (1997), cross-border academic mobility in Central Asia remains constrained by fragmented credential verification systems. This paper examines the institutional barriers to educational integration in Kazakhstan and Kyrgyzstan. This inefficiency stems from two structural factors: (1) the absence of interoperable verification infrastructure, and (2) asymmetric institutional capacity across national systems (Fukuyama, 2013). This paper studies how Kazakhstan and Kyrgyzstan manage credential recognition differently. We use a […]

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Kolmogorov-Arnold Networks (KANs) have shown strong potential for efficiently approximating complex nonlinear functions. However, the original KAN formulation relies on B-spline basis functions, which incur substantial computational overhead due to De Boor’s algorithm. To address this limitation, recent work has explored alternative basis functions such as radial basis functions (RBFs) that can improve computational efficiency and flexibility. Yet, standard RBF-KANs often sacrifice accuracy relative to the original KAN design. In this work, we propose Free-RBF-KAN, a RBF-based KAN […]

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Creating and sharing knowledge are defining traits of humankind, yet copyright law has grown so restrictive that it can require acts of civil disobedience to ensure that students and scholars have the books they need and to preserve swaths of culture from being lost forever. Reputable research generally follows a familiar pattern: Scientific articles are written by scholars based on their research—often with public funding. Those articles are then peer-reviewed by other scholars in their fields and revisions […]

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Can AI shopping agents move beyond sending product links and actually complete trusted purchases end to end inside a chat? Universal Commerce Protocol, or UCP, is Google’s new open standard for agentic commerce. It gives AI agents and merchant systems a shared language so that a shopping query can move from product discovery to an authenticated order without custom integrations for every retailer and every surface. https://developers.googleblog.com/under-the-hood-universal-commerce-protocol-ucp/ What problem is UCP solving? Today, most AI shopping experiences stop […]

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Electroencephalogram (EEG) decoding is a critical component of medical diagnostics, rehabilitation engineering, and brain-computer interfaces. However, contemporary decoding methodologies remain heavily dependent on task-specific datasets to train specialized neural network architectures. Consequently, limited data availability impedes the development of generalizable large brain decoding models. In this work, we propose a paradigm shift from conventional signal-based decoding by leveraging large-scale vision-language models (VLMs) to analyze EEG waveform plots. By converting multivariate EEG signals into stacked waveform images and integrating […]

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arXiv:2601.14287v1 Announce Type: new Abstract: External memory systems are pivotal for enabling Large Language Model (LLM) agents to maintain persistent knowledge and perform long-horizon decision-making. Existing paradigms typically follow a two-stage process: computationally expensive memory construction (e.g., structuring data into graphs) followed by naive retrieval-augmented generation. However, our empirical analysis reveals two fundamental limitations: complex construction incurs high costs with marginal performance gains, and simple context concatenation fails to bridge the gap between retrieval recall and reasoning accuracy. […]

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