arXiv:2601.14479v1 Announce Type: new Abstract: This paper investigates the ability of large language models (LLMs) to solve statistical tasks, as well as their capacity to assess the quality of reasoning. While state-of-the-art LLMs have demonstrated remarkable performance in a range of NLP tasks, their competence in addressing even moderately complex statistical challenges is not well understood. We have fine-tuned selected open-source LLMs on a specially developed dataset to enhance their statistical reasoning capabilities, and compared their performance with […]

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arXiv:2603.05542v1 Announce Type: new Abstract: The rapid advancement of AI is transforming human-centered systems, with profound implications for human-AI interaction, human-data interaction, and visual analytics. In the AI era, data analysis increasingly involves large-scale, heterogeneous, and multimodal data that is predominantly unstructured, as well as foundation models such as LLMs and VLMs, which introduce additional uncertainty into analytical processes. These shifts expose persistent challenges for human-data interactive systems, including perceptually misaligned latency, scalability constraints, limitations of existing interaction […]

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It’s hard to find much to write about Google I/O this year because I have a policy of not writing about anything that I can’t try out myself, and a lot of the big announcements are “coming soon”. I actually prefer to write about things that are in general availability, because I’ve had instances in the past where the previews didn’t match what was released to the general public later on. Aside from Gemini 3.5 Flash the most […]

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arXiv:2604.01337v1 Announce Type: new Abstract: While deep learning has significantly advanced accident anticipation, the robustness of these safety-critical systems against real-world perturbations remains a major challenge. We reveal that state-of-the-art models like CRASH, despite their high performance, exhibit significant instability in predictions and latent representations when faced with minor input perturbations, posing serious reliability risks. To address this, we introduce SECURE – Stable Early Collision Understanding Robust Embeddings, a framework that formally defines and enforces model robustness. SECURE […]

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I’ve been diving into the deep end of Reinforcement Learning and Generative Models lately, specifically trying to see if I could train a simple diffusion model from scratch using nothing but a reward signal. On paper, it sounded like a fun weekend experiment, but in reality, it was a 72-hour masterclass in frustration. By Sunday night, I was staring at a screen of pure static; every time I adjusted the hyperparameters, the model would either collapse into a […]

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Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to catastrophic forgetting of previous knowledge. Existing methods typically treat parameters uniformly, failing to distinguish between specific task knowledge and shared capabilities. We introduce Mixture of Sparse Experts for Task-Agnostic Continual Learning, referred to as SETA, a framework that resolves the plasticity-stability conflict by decomposing the model into modular subspaces. Unlike standard updates, where tasks compete for the same […]

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arXiv:2603.08823v1 Announce Type: new Abstract: We introduce Fish Audio S2, an open-sourced text-to-speech system featuring multi-speaker, multi-turn generation, and, most importantly, instruction-following control via natural-language descriptions. To scale training, we develop a multi-stage training recipe together with a staged data pipeline covering video captioning and speech captioning, voice-quality assessment, and reward modeling. To push the frontier of open-source TTS, we release our model weights, fine-tuning code, and an SGLang-based inference engine. The inference engine is production-ready for streaming, […]

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Accurate prediction of crop states (e.g., phenology stages and cold hardiness) is essential for timely farm management decisions such as irrigation, fertilization, and canopy management to optimize crop yield and quality. While traditional biophysical models can be used for season-long predictions, they lack the precision required for site-specific management. Deep learning methods are a compelling alternative, but can produce biologically unrealistic predictions and require large-scale data. We propose a emph{hybrid modeling} approach that uses a neural network to […]

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Partial differential equations (PDEs) are a central tool for modeling the dynamics of physical, engineering, and materials systems, but high-fidelity simulations are often computationally expensive. At the same time, many scientific applications can be viewed as the evolution of spatially distributed fields, making data-driven forecasting of such fields a core task in scientific machine learning. In this work we study autoregressive deep-learning surrogates for two-dimensional PDE dynamics on periodic domains, focusing on generalization to out-of-distribution initial conditions within […]

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Let’s start with the basics. Cryptocurrency mining is the process where computers compete and “work” to solve mathematical puzzles in order to add new transactions to a distributed ledger. In return for helping secure the network and confirm payments, miners earn newly created coins and transaction fees. The mechanism behind this is called Proof-of-Work (PoW), and many coins use it, including very popular ones like Bitcoin, Monero, and Litecoin. We would say anyone can be a miner, but […]

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