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arXiv:2606.18323v1 Announce Type: cross Abstract: Open autoregressive neural-codec text-to-speech (TTS) models sound excellent on typical inputs yet suffer stochastic catastrophic failures: on a meaningful fraction of utterances they emit silence, terminate early, or collapse into repetitive or hallucinated content. We show this failure mode is cheap to remove. Under a single format-robust metric (a catastrophic-failure rate via an ASR round-trip), best-of-N ASR self-verification drives failures

arXiv:2606.18354v1 Announce Type: cross Abstract: Recent advances in generative machine learning models have significantly improved medical imaging, offering promising solutions for data augmentation, privacy preservation, and improved model generalization. However, synthesizing high-quality structural MRI data for Alzheimer's Disease (AD) remains challenging due to the subtle, region-specific, and progressive anatomical changes associated with neurodegeneration. In this paper, we extend the Med

arXiv:2606.18436v2 Announce Type: cross Abstract: Sparse point observations are increasingly available for precipitation nowcasting, but it is unclear how much they improve dense radar-field forecasts. We partially address this question with a multimodal graph neural network nowcasting system over the Nordic radar domain. The model predicts rain rate every five minutes up to two hours ahead and is trained with different combinations of radar history, MEPS numerical weather prediction, Netatmo su

arXiv:2606.18464v1 Announce Type: cross Abstract: Detecting the tiny Doppler shifts induced by Earth-mass planets in stellar radial-velocity measurements remains extremely challenging due to stellar activity. Many deep-learning methods performing well on simulated data remain difficult to apply reliably on real stellar spectra. The aim of this work is to develop a deep-learning framework that generalizes to real, unseen spectra and improves the detectability of Earth-mass planets in radial-veloc

arXiv:2606.18467v1 Announce Type: cross Abstract: Modern AI agents retrieve documents, call tools, check intermediate information, and then produce a final answer or action. This creates a risk-control problem that is not visible from the final answer alone. A final response may look acceptable even when the retrieval was weak, a tool output was wrong, or an earlier step was unsupported. We propose ToolChain-CRC, a conformal risk-control method for retrieval-augmented and tool-using agents under

arXiv:2606.18514v1 Announce Type: cross Abstract: Neural combinatorial optimization (NCO) offers a promising alternative to traditional heuristic-based methods for solving complex graph optimization problems by proposing to learn heuristics through data. This class of problems frequently arises in automation, as it can be used to model a variety of applications. While NCO has been extensively studied for deterministic combinatorial optimization problems, there are only a few works that aim to so

arXiv:2606.18527v1 Announce Type: cross Abstract: U-calibration studies online forecasting algorithms whose predictions can be consumed by any unknown downstream agent, guaranteeing sublinear regret simultaneously for all proper loss functions. Existing U-calibration algorithms achieve worst-case optimal $O(\sqrt{T})$ regret for every bounded proper loss, but they fail to adapt to easier losses: as we show, even for smooth losses such as squared loss, they incur $\Omega(\sqrt{T})$ regret instead

arXiv:2606.18679v2 Announce Type: cross Abstract: We study the problem of fair online resource allocation, motivated by applications such as refugee resettlement and airline scheduling, where agents arrive sequentially and must be assigned to facilities with limited capacities. We introduce a model that maximizes the overall welfare subject to resource constraints and a Lipschitz fairness requirement, which ensures that similar agents arriving in the same batch receive similar expected outcomes.

arXiv:2606.18759v1 Announce Type: cross Abstract: The concept of geodesic-like curves was introduced by Chen in 2010 as a method for estimating shortest paths (geodesics) on parametric surfaces, with its convergence established theoretically. However, an efficient numerical computational framework has not yet been developed. In this paper, we propose an elegant and efficient approach for computing geodesic-like curves by leveraging deep learning and Physics-Informed Neural Networks (PINNs). Unde

arXiv:2606.18807v1 Announce Type: cross Abstract: The field of learning-augmented algorithms has demonstrated that machine-learned predictions can bypass worst-case lower bounds across a wide range of problems. So far, however, the focus has been almost exclusively on polynomial-time algorithms, where predictions improve competitive ratios, approximation guarantees, or running times. In this paper, we raise the question of whether predictions can push the frontier of exact exponential-time algor

arXiv:2606.19023v1 Announce Type: cross Abstract: The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces. Recent vulnerabilities demonstrate that malicious behavior can be embedded within model artifacts, often bypassing existing defenses. Current model-scanning solutions primarily rely on static, format-specific rules or known attack signatures, which limit their ability to generalize across frameworks and to detect novel exploitation paths. In contr

arXiv:2606.19039v1 Announce Type: cross Abstract: The mismatch between continuous acoustic signals and discrete event-driven processing remains a fundamental bottleneck for neuromorphic speech processing. Current systems typically rely on fixed spike encoders, forcing downstream Spiking Neural Networks (SNNs) to compensate for non-adaptive input representations. To address this, we present a learnable residual speech-to-spike encoder jointly trained end-to-end with a Recurrent Leaky Integrate-an

arXiv:2606.19057v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used as judges for scalable evaluation, yet such LLM--as--a--Judge systems exhibit systematic biases that are decoupled from semantic quality, most notably verbosity bias. Meanwhile, human supervision is costly and typically selective, yielding reliable positive judgments but leaving most outputs unlabelled and potentially mixed in quality. We formulate LLM evaluation under selective human supervision

arXiv:2510.09905v2 Announce Type: replace-cross Abstract: When an AI assistant remembers that Sarah is a single mother working two jobs, does it interpret her stress differently than if she were a wealthy executive? As personalized AI systems increasingly incorporate long-term user memory, understanding how this memory shapes emotional reasoning is critical. We investigate how user memory affects emotional intelligence in large language models (LLMs) by evaluating 15 models on human-validated em

arXiv:2511.00802v2 Announce Type: replace-cross Abstract: With data-driven development now widely adopted, online A/B testing is an established method for measuring the effects of new technologies. However, deploying online experiments demands resources for design, implementation, and deployment, and may negatively impact users (e.g., unsafe or unethical outcomes) while requiring weeks of data collection. To address this, the growing research area of off-policy evaluation (OPE), or offline A/B t

arXiv:2601.12805v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown growing promise in biomedical research, particularly for knowledge-driven interpretation tasks. However, their ability to reliably reason from gene-level knowledge to functional understanding, a core requirement for knowledge-enhanced cell atlas interpretation, remains largely underexplored. To address this gap, we introduce SciHorizon-GENE, a large-scale gene-centric benchmark constructed from auth

arXiv:2602.00161v2 Announce Type: replace-cross Abstract: In this paper, we formulate the compression of large language models (LLMs) by optimally deleting transformer blocks (``block removal'') as a constrained binary optimization (CBO) problem that can be mapped to a physical system (Ising glass), whose energies are a strong proxy for downstream model performance. This formulation enables an efficient ranking of a large number of candidate block-removal configurations yielding many high-qualit

arXiv:2603.06310v2 Announce Type: replace-cross Abstract: Speech foundation models struggle with low-resource Pacific Indigenous languages because of severe data scarcity. Furthermore, full fine-tuning risks catastrophic forgetting. To address this gap, we present an empirical study adapting models to real-world Pacific datasets. We investigate the impact of data volume, adaptation strategies, and representational drift on speech foundation models for various Pacific languages. Additionally, we

arXiv:2605.16385v3 Announce Type: replace-cross Abstract: Geometric problem solving, as a typical multimodal reasoning problem, has attracted much attention and made great progress recently, however most of works focus on plane geometry while usually fail in solid geometry due to 3D spatial diagrams and complex reasoning. To bridge this gap, we introduce Hilbert-Geo, the first unified formal language framework for solid geometry, including an extensive predicate library and a dedicated theorem b

arXiv:2605.29676v2 Announce Type: replace-cross Abstract: Large language models in Agentic AI systems consume tool schemas and execution results and emit tool invocations as structured data. The default language for that exchange, JSON, was designed for application-to-application interchange rather than token efficiency, so its structural elements impose substantial token overhead. Recent work proposes token-optimized alternatives such as TOON (Token-Oriented Object Notation) and TRON (Token Red

arXiv:2606.01249v3 Announce Type: replace-cross Abstract: On-Policy Distillation (OPD) is a fundamental technique for efficient post-training of large language models (LLMs), with broad applications in agent learning, multi-task enhancement, and model compression. However, OPD training becomes unstable when the teacher and student distributions differ substantially, as teacher supervision on student-generated tokens may yield unreliable policy gradients and even cause optimization failure. This

arXiv:2606.07591v3 Announce Type: replace-cross Abstract: AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capability remains difficult to verify. We present ResearchClawBench, a benchmark for evaluating autonomous scientific research across 40 tasks from 10 scientific domains. Each task is grounded in a real published paper, provides related literature and raw data, and hides the target paper during evaluation. Expert-curated multimodal rubric

Every agentic system I see has the same hidden tax: the model keeps rereading the same context. Tickets, Slack threads, docs, customer history, database notes, runbooks, logs, prior decisions. You can cache static prefixes, route to cheaper models, or set team budgets, but none of those fixes the underlying behavior: agents start most tasks trying to re-explore everything. We built Parcle as a shared memory layer for AI agents. It ingests operational context, indexes what happened, and lets agen

Article URL: https://www.bbc.com/news/articles/c3wyxvqdx1zo Comments URL: https://news.ycombinator.com/item?id=48580466 Points: 35 # Comments: 19

Article URL: https://dolfyn.ai/ Comments URL: https://news.ycombinator.com/item?id=48580402 Points: 1 # Comments: 1

Article URL: https://www.artificialstudio.ai/blog/ai-design-report-2026 Comments URL: https://news.ycombinator.com/item?id=48580378 Points: 1 # Comments: 2

<p> AI that gets you *and* the people in your life, together </p> <p> <a href="https://www.producthunt.com/products/genie-mentions?utm_campaign=producthunt-atom-posts-feed&utm_medium=rss-feed&utm_source=producthunt-atom-posts-feed">Discussion</a> | <a href="https://www.producthunt.com/r/p/1174820?app_id=339">Link</a> </p>

<p> A unified control plane for Magento with Claude Code web </p> <p> <a href="https://www.producthunt.com/products/ai-native-ecommerce-infrastructure?utm_campaign=producthunt-atom-posts-feed&utm_medium=rss-feed&utm_source=producthunt-atom-posts-feed">Discussion</a> | <a href="https://www.producthunt.com/r/p/1174814?app_id=339">Link</a> </p>

Midjourney CEO David Holz just showed off the company's first hardware product and plans to build a San Francisco spa, which he admitted is a bit different from the "cat pictures" produced by its AI image generator. Dubbed The Midjourney Scanner, it's an ultrasound-based full-body scanner that uses a ring of sensors to capture vertical […]

Article URL: https://huggingface.co/WhissleAI/STT-meta-ZH-150m Comments URL: https://news.ycombinator.com/item?id=48580224 Points: 1 # Comments: 0

Article URL: https://blog.alexellis.io/local-ai-is-not-opus/ Comments URL: https://news.ycombinator.com/item?id=48580209 Points: 111 # Comments: 36

Article URL: https://www.businessinsider.com/rainbow-shops-fashion-models-ai-use-jobs-impact-2026-6 Comments URL: https://news.ycombinator.com/item?id=48580182 Points: 1 # Comments: 0

Comments URL: https://news.ycombinator.com/item?id=48580171 Points: 2 # Comments: 0

Article URL: https://elvalabs.ai Comments URL: https://news.ycombinator.com/item?id=48580110 Points: 1 # Comments: 2

Article URL: https://www.reuters.com/world/meta-head-product-ai-work-transformation-is-leaving-company-2026-06-17/ Comments URL: https://news.ycombinator.com/item?id=48580103 Points: 21 # Comments: 2

Article URL: https://solidlao.github.io/GenDB/ Comments URL: https://news.ycombinator.com/item?id=48579954 Points: 1 # Comments: 0

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OpenAI's LifeSciBench evaluates whether frontier AI can handle real life-science research across 750 expert-authored tasks, seven workflows, and seven biological domains. Built by 173 PhD scientists with 19,020 rubric criteria, it grades reasoning and decisions, not just recall. The best model, GPT-Rosalind, passes 36.1%, leaving large headroom on artifacts, exact outputs, and operational calls. The post OpenAI Releases LifeSciBench, a 750-Task Benchmark Grading AI Models on Real Life-Science Re

Article URL: https://amitlimaye1.substack.com/p/six-numbers-from-running-1500-ai Comments URL: https://news.ycombinator.com/item?id=48579822 Points: 2 # Comments: 0

<!-- SC_OFF --><div class="md"><h1>🏇 Open-Source Hong Kong Horse Racing ML Pipeline — Feedback Welcome</h1> <p>Hi everyone,</p> <p>I've been working on an open-source horse racing prediction project focused on <strong>Hong Kong Jockey Club (HKJC)</strong> data.</p> <ul> <li>📦 <strong>Repo:</strong> <a href="https://github.com/catowabisabi/horse-racing-model-training">catowabisabi/horse-racing-model-training</a></li> <li>🌐 <strong>Live Dashboard:</strong> <a href="https://catowabisabi.github.i