arXiv:2502.18709v3 Announce Type: replace-cross Abstract: Online structured prediction is a task of sequentially predicting outputs with complex structures based on inputs and past observations, encompassing online classification. Recent studies showed that in the full-information setting, we can achieve finite bounds on the textit{surrogate regret}, textit{i.e.,}~the extra target loss relative to the best possible surrogate loss. In practice, however, full-information feedback is often unrealistic as it requires immediate access to the whole structure of complex outputs. Motivated by this, […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## The State of The Noonion: Blogging Our Way Through the AI Boom By @noonion [ 13 Min read ] HackerNoon’s 2016–2026 evolution: $727k Q4 revenue, 62% Business Blogging CAGR, 4.4M monthly pageviews, and resilient, AI-aware publishing. Read More. Navigating Crypto’s 2026 Reset: Why Retail-First […]

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arXiv:2603.06688v1 Announce Type: new Abstract: We present “Narrative Weaver”, a novel framework that addresses a fundamental challenge in generative AI: achieving multi-modal controllable, long-range, and consistent visual content generation. While existing models excel at generating high-fidelity short-form visual content, they struggle to maintain narrative coherence and visual consistency across extended sequences – a critical limitation for real-world applications such as filmmaking and e-commerce advertising. Narrative Weaver introduces the first holistic solution that seamlessly integrates three essential capabilities: fine-grained […]

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arXiv:2602.11179v1 Announce Type: new Abstract: Prompt engineering has emerged as an integral technique for extending the strengths and abilities of Large Language Models (LLMs) to gain significant performance gains in various Natural Language Processing (NLP) tasks. This approach, which requires instructions to be composed in natural language to bring out the knowledge from LLMs in a structured way, has driven breakthroughs in various NLP tasks. Yet there is still no structured framework or coherent understanding of the varied […]

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arXiv:2411.09686v3 Announce Type: replace Abstract: Regressing a function $F$ on $mathbb{R}^d$ without the statistical and computational curse of dimensionality requires special statistical models, for example that impose geometric assumptions on the distribution of the data (e.g., that its support is low-dimensional), or strong smoothness assumptions on $F$, or a special structure $F$. Among the latter, compositional models $F=fcirc g$ with $g$ mapping to $mathbb{R}^r$ with $rll d$ include classical single- and multi-index models, as well as neural networks. […]

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Tokens are discrete representations that allow modern deep learning to scale by transforming high-dimensional data into sequences that can be efficiently learned, generated, and generalized to new tasks. These have become foundational for image and video generation and, more recently, physical simulation. As existing tokenizers are designed for the explicit requirements of realistic visual perception of images, it is necessary to ask whether these approaches are optimal for scientific images, which exhibit a large dynamic range and require […]

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arXiv:2603.00716v1 Announce Type: cross Abstract: We study computationally and statistically efficient reinforcement learning under the linear $Q^{pi}$ realizability assumption, where any policy’s $Q$-function is linear in a given state-action feature representation. Prior methods in this setting are either computationally intractable, or require (local) access to a simulator. In this paper, we propose a computationally efficient online RL algorithm, named Frozen Policy Iteration, under the linear $Q^{pi}$ realizability setting that works for Markov Decision Processes (MDPs) with stochastic initial […]

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arXiv:2601.21070v1 Announce Type: new Abstract: Large language models for code are advancing fast, yet our ability to evaluate them lags behind. Current benchmarks focus on narrow tasks and single metrics, which hide critical gaps in robustness, interpretability, fairness, efficiency, and real-world usability. They also suffer from inconsistent data engineering practices, limited software engineering context, and widespread contamination issues. To understand these problems and chart a path forward, we combined an in-depth survey of existing benchmarks with insights gathered […]

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arXiv:2601.16348v1 Announce Type: new Abstract: Art technological investigations of historical panel paintings rely on acquiring multi-modal image data, including visual light photography, infrared reflectography, ultraviolet fluorescence photography, x-radiography, and macro photography. For a comprehensive analysis, the multi-modal images require pixel-wise alignment, which is still often performed manually. Multi-modal image registration can reduce this laborious manual work, is substantially faster, and enables higher precision. Due to varying image resolutions, huge image sizes, non-rigid distortions, and modality-dependent image content, registration […]

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