arXiv:2604.13077v1 Announce Type: new Abstract: Coronary angiography (CAG) reports contain clinically relevant physiological measurements, yet this information is typically in the form of unstructured natural language, limiting its use in research. We investigate the use of Large Language Models (LLMs) to automatically extract these values, along with their anatomical locations, from Portuguese CAG reports. To our knowledge, this study is the first addressing physiology indexes extraction from a large (1342 reports) corpus of CAG reports, and one of […]

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Crypto staking has emerged as a popular method for cryptocurrency holders to earn passive income by participating in the network’s operations. By locking up their digital assets, users contribute to the security and efficiency of a blockchain, receiving rewards in return. Below is a comparison of popular staking options, with a detailed look at EMCD Staking. Understanding Crypto Staking Staking involves holding cryptocurrencies in a wallet to support the operations of a Proof-of-Stake (PoS) blockchain network. In exchange […]

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arXiv:2602.13280v1 Announce Type: new Abstract: Simulating student learning behaviors in open-ended problem-solving environments holds potential for education research, from training adaptive tutoring systems to stress-testing pedagogical interventions. However, collecting authentic data is challenging due to privacy concerns and the high cost of longitudinal studies. While Large Language Models (LLMs) offer a promising path to student simulation, they suffer from competency bias, optimizing for efficient correctness rather than the erratic, iterative struggle characteristic of novice learners. We present BEAGLE, […]

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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:2411.12502v4 Announce Type: replace-cross Abstract: Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic processes. Originally developed as a scalable alternative to Gaussian Processes (GPs), which are limited by $O(n^3)$ runtime complexity, the most accurate modern NPs can often rival GPs but still suffer from an $O(n^2)$ bottleneck due to their attention mechanism. We introduce the Transformer Neural Process – Kernel Regression (TNP-KR), a scalable NP featuring: […]

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People think that becoming a writer is easy. In some ways, it is. All you need is a pen and paper or a computer, an idea, and the determination to write. Voila, you’re a writer now. Writing is easy; becoming serious about your writing is a whole lot harder and more complex. However, why should it be? Writers should be helping each other navigate this crazy world whenever possible. So, if you’re reading this right now, then that […]

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arXiv:2603.20526v1 Announce Type: cross Abstract: Policy gradient computes a backward pass for every sample, even though the backward pass is expensive and most samples carry little learning value. The Delightful Policy Gradient (DG) provides a forward-pass signal of learning value: emph{delight}, the product of advantage and surprisal (negative log-probability). We introduce the emph{Kondo gate}, which compares delight against a compute price and pays for a backward pass only when the sample is worth it, thereby tracing a quality–cost […]

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arXiv:2508.14936v2 Announce Type: replace-cross Abstract: Synthetic data holds substantial potential to address practical challenges in epidemiology due to restricted data access and privacy concerns. However, many current methods suffer from limited quality, high computational demands, and complexity for non-experts. Furthermore, common evaluation strategies for synthetic data often fail to directly reflect statistical utility and measure privacy risks sufficiently. Against this background, a critical underexplored question is whether synthetic data can reliably reproduce key findings from epidemiological research while […]

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arXiv:2602.02499v1 Announce Type: new Abstract: Long-context capability and computational efficiency are among the central challenges facing today’s large language models. Existing efficient attention methods reduce computational complexity, but they typically suffer from a limited coverage of the model state. This paper proposes ROSA-Tuning, a retrieval-and-recall mechanism for enhancing the long-context modeling ability of pretrained models. Beyond the standard attention mechanism, ROSA-Tuning introduces in parallel a CPU-based ROSA (RWKV Online Suffix Automaton) retrieval module, which efficiently locates historical positions […]

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Hello! I am looking for research that has successfully applied AlphaZero/MuZero-style learning to sequential, perfect information, non-zero sum board games, e.g. Terra Mystica where the winning player is decided by a numerical score (associated with each player) at the end of the game, rather than the zero sum outcomes of games such as Chess, Shogi, Go, etc. I figure there must exist an approach that works for multi-agent (> 2 player) games. Any suggestions? Thank you submitted by […]

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