AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.24940v1 Announce Type: new Abstract: Partial differential equations (PDEs) often have high-frequency and multi-scale features that neural networks struggle to approximate. Physics-Informed Neural Networks (PINNs) build the governing equations directly into training, but suffer from spectral bias: they learn low-frequency components faster than high-frequency ones. Techniques such as Fourier feature embeddings and sinusoidal activations address this, but most studies assume they help across the board without checking which spectral regimes actually benefit. We introduce a dual-branch, spectrally-gated architecture (DBSG-PINN) that splits low- and high-frequency components into separate subnetworks joined by an adaptive gate, and use it to run a partially controlled ablation of frequency decomposition and spectral routing. We test this on five one-dimensional benchmark PDEs, ranging from smooth, single-scale problems to oscillatory, multi-scale ones. Frequency decomposition helps most on the spectrally complex benchmarks, cutting relative $L_2$ error by up to $59.2\%$ on a multimodal wave problem, but gives little benefit on smoother PDEs. On one benchmark (1D Wave), it performs substantially worse than a simpler fixed-combination variant. The gate's benefit scales with how spectrally rich the target solution is: the full model's advantage over the ablations is largest on multi-scale benchmarks and smallest (or negative) on single-scale ones, consistent with the gate exploiting frequency structure rather than acting as noise,though we do not directly visualize or quantify its spatial activations in this study. All results come from a single training seed across five 1D benchmarks, so we present this as an exploratory study meant to raise questions rather than answer them, and outline the additional seeds and benchmarks needed to test whether the pattern holds.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.24938v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models scale capacity for strong quality while keeping per-token compute bounded through sparse expert activation. Yet low-latency MoE serving is increasingly challenging, because it spans two inference phases with fundamentally different bottlenecks: prefill is dominated by token-wise expert computation, whereas decode is constrained by memory traffic from the batch-wise activated expert set. However, existing training-free acceleration methods optimize only a single resource proxy, either the experts each token executes or the experts a batch activates, and either discard the excluded experts' contribution or leave it only implicitly approximated. In this paper, we propose ExFold, a unified training-free expert-folding framework for jointly accelerating MoE prefill and decode. ExFold casts both prefill and decode as one budgeted output-approximation problem: execute only a phase-specific constrained expert set while projecting the contribution of budget-excluded experts onto retained experts using calibrated scalar projectors. Motivated by the observation that many expert outputs are directionally aligned but differ in magnitude, ExFold calibrates a pairwise scalar-projector matrix on unlabeled data and uses it at inference time to fold excluded expert contributions into retained experts. Under this view, prefill acceleration becomes token-level Top-K folding, and decode acceleration becomes batch-level expert-pool folding. The two phases differ only in how retained experts are selected, while excluded contributions are recovered by one shared folding mechanism. We implement ExFold as a plug-and-play plugin in vLLM, with a lightweight expert-folding CUDA kernel, delivering up to 1.41x TTFT and 2.45x TPOT speedups while retaining about 99% of the original average quality.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.24937v1 Announce Type: new Abstract: Multi-Modal Anomaly Detection (MMAD) detects rare abnormal events from heterogeneous data sources and is increasingly used in safety- and reliability-critical applications such as industrial inspection and cybersecurity. Yet the literature is fragmented across domains and modality combinations, and existing surveys usually group methods by architecture rather than by how abnormality is defined and separated in multi-modal settings. We survey MMAD from an assumption-driven perspective. We formalize the problem, identify five intrinsic characteristics underlying its core challenges, and organize prior work into two complementary paradigms. The first, normality-assumption methods, models regularity via representation learning, cross-modal alignment, and knowledge enhancement. The second, anomaly-assumption methods, sharpens decision boundaries through coarse-grained, structural, and semantic anomaly injection. We also investigate how foundation models are reshaping MMAD through scalable pretraining, flexible cross-modal transfer, and emerging reasoning capabilities. Finally, we compile representative benchmarks and evaluation protocols across domains and highlight open problems and future directions for robust, adaptive, and interpretable MMAD systems.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.24936v1 Announce Type: new Abstract: We present GreenLeaf Law Embed Tiny, a 0.6B parameter embedding model for legal domain retrieval. GreenLeaf-Tiny achieves 75.11% on the Massive Legal Embedding Benchmark (MLEB) and 64.38% on MTEB(Law, v1),demonstrating competitive performance among models under 1B parameters. Our approach combines a two-stage training pipeline that first distills knowledge from a larger teacher model into a compact student architecture, then applies domain-specific fine-tuning with hard negative mining; a carefully curated dataset of 3.4 million query-passage pairs, including 150,000 human-curated samples across diverse legal jurisdictions; and an efficient inference architecture supporting multiple quantization levels (BF16, INT8, binary) enabling deployment in resource-constrained environments. We provide detailed analysis of our training methodology, architectural choices, and comprehensive evaluation across legal retrieval tasks. Our results demonstrate that domain-specific training with high-quality data can improve performance for specialized domain applications

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.24904v1 Announce Type: new Abstract: Inertial sensors at multiple body locations can improve activity recognition, but requiring every sensor at inference increases the deployment burden. We study whether four synchronized IMUs available during training can improve a student that uses only the right-arm IMU during fitting and inference. A frozen four-IMU teacher provides logit and feature targets. Fixed-weight knowledge distillation applies each target with the same strength to every fitting sample, although the student may not benefit equally from them. We introduce dynamic influence weighting (DIW), which tests a one-step candidate update on separate fold-internal training participants. DIW then assigns separate sample-wise gates to the logit and feature losses. On WEAR, we evaluate 19 labels and 68,298 complete windows from 22 participants using subject-disjoint five-fold cross-validation. Pooled out-of-fold macro-F1 is 0.561820 for Supervised and 0.571623 for Fixed-weight KD. DIW reaches 0.638451, gains of 7.66 and 6.68 percentage points, respectively. It exceeds Supervised for 18 of 19 labels and 21 of 22 held-out participants. All three routes retain the same 80,915-parameter right-arm student at inference. Under this protocol, DIW converts training-only multi-position information into a stronger single-IMU model without changing deployed sensing or the student forward graph.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：World's first patient to undergo live AI-assisted brain surgery has tumour removed 4 hours ago Smitha MundasadHealth reporter BBC Rhys Hibbert's tumour could have led to blindness The world's first patient to have brain…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Discussion | Link

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：OpenAI is expanding its presence in Brazil, deepening engagement with developers, businesses, and communities to support AI adoption across the country.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Earlier this year, Meta created a “plan” to reduce some of its teams by as much as 60 percent to make the company “AI native,” Reuters reported today, citing two people familiar with Meta’s internal affairs. Reuters’ re…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：--> [Submitted on 25 Aug 2026] Title:Reading Is Not Using: Retrieval, Judgment, and the Design of AI Financial Research Workflows View a PDF of the paper titled Reading Is Not Using: Retrieval, Judgment, and the Design…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Revisited: Guardian journalist Michael Safi looks into the world of artificial intelligence, exploring the dangers and promises it holds for society Today in Focus is on a summer break and will be back with new episodes from 1 September. In the meantime, we are bringing you season one of Black Box, before the launch of season two in early September. This episode was first broadcast on 14 March 2024. Two stories about the way artificial intelligence could make the world better – and is already doing so. In Montana, when Lee Johnson discovered his wife, Yokie, had cancer, he turned to AI – and was surprised by the answers he got. Meanwhile, in Massachusetts, Prof Regina Barzilay’s experience with cancer has led her to build an AI system that can detect the disease years before a human can. Continue reading...

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Much of the autonomous vehicle (AV) space is uncharted territory. However, at Waymo, with more than 200 million miles driven fully autonomously, we’re one of very few companies that can look to our past to illuminate ou…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Image and video models have gotten a lot better over the last few years, even though the internals of these models haven't changed much since Stable Diffusion 3.Of course, there have been small variants like the auto-re…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Open Source wins!

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Discussion | Link

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Microsoft Corp. co-founder Bill Gates sounded the alarm recently regarding how artificial intelligence will cause profound disruption in the labor market, warning that millions of jobs are at risk and there’s no plan yet to help those who will lose out. Gates, now a philanthropist, said in an almost-6,000-word essay that the transition to the […] The post Bill Gates issues stark warning about AI and the future of humanity appeared first on SiliconANGLE.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The conference with hot chips and even hotter companies

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：--> [Submitted on 21 Aug 2026] Title:Assessing the Impact of High-Resolution Imaging on Statistical Validation of TESS Planet Candidates View a PDF of the paper titled Assessing the Impact of High-Resolution Imaging on…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：0:00 / 0:00 Introducing S1: In-Context Learning for Robotics Unseen tasks10-minute horizonsOne video promptNo post-training 13-minute read Introduction The evolution of language modeling provides a blueprint for turning…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Discussion | Link

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Z.ai Co. today released the code for GLM-5.3-Flash, a large language model that is ten times more cost-efficient than its predecessor. The algorithm made its original debut last week under the codename Ox Alpha. LLM marketplace operator OpenRouter Inc. launched a free hosted version of Ox Alpha and didn’t disclose its developer, which drew a […] The post Z.ai open-sources ‘Ox Alpha’ model as GLM-5.3-Flash appeared first on SiliconANGLE.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：FAA and EASA order compressor blade replacements on V2500 engines - AvioRadar 1 World News Cessna Citation CJ4 reaches 500 deliveries as Gen3 nears certification 27. August 2026. 2 World News Lufthansa receives first Bo…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：WhisperBar — Speak messy thoughts into polished text. Speak messy thoughts into polished text. A Mac menu bar app for writing and skimming. Dictate from any app and get business-casual text ready to paste, or hear a sho…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Designing effective reward signals for open-domain question answering is challenging because high-quality responses must simultaneously satisfy multiple aspects of answer quality that are difficult to capture with a holistic scalar objective. We introduce a rubric-based reward framework that generates query-specific rubrics grounded in retrieved evidence and decomposed into multiple quality dimensions, providing fine-grained supervision during post-training. Averaged across three evaluation axes (composition, grounding, and instruction-following), our approach improves over the…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Weaviate 1.39 promotes the Boost API and MMR diversity selection to GA, previews 4-bit Rotational Quantization, and ships an experimental Search REST API.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Investigación, análisis y redacción jurídica con IA Ahorrá horas de trabajo con la IA legal más precisa y confiable del mercado argentino. No inventa fallos ni normativa: cada cita con su fuente oficial. Registro simple…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：<p><strong><a href="https://qwen.ai/blog?id=qwen3.8-flash-next">Qwen3.8-Flash-Next</a></strong></p> Another open weights model from Qwen. This one is "a multimodal MoE model that also serves as an early preview of the architecture used in Qwen4".</p> <p>It's pretty big: 125B tokens, but only 6B active which means it gets a pretty big performance boost.</p> <p>I've been trying it out on a DGX Spark using <a href="https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF">these Unsloth quantized models</a>. I'm still exploring the model - so far I've tried the 72.5GB UD-IQ1_S one (producing <a href="https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fgist.github.com%2Fsimonw%2Ff9c69ebdab90d8a45b8de4742cc7b840">these pelicans</a>) and the 78.9GB UD-Q2_K_XL (producing <a href="https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fgist.github.com%2Fsimonw%2F6ba7cbfc1a9336986703b41f7fccd73a">these</a>).</p> <p>My favorite so far was this xhigh reasoning effort one from UD-Q2_K_XL:</p> <p><img alt="Flat vector illustration: a white pelican with an orange beak and orange legs rides a red bicycle along a sandy path, a wicker basket on the handlebars holding a blue fish, with green rolling hills, a small tree and bushes, white clouds and a bright yellow sun in a blue sky behind it" src="https://static.simonwillison.net/static/2026-08-27/IMG_7667.png" /> <p><small></small>Via <a href="https://news.ycombinator.com/item?id=49448210">Hacker News</a></small></p> <p>Tags: <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a>, <a href="https://simonwillison.net/tags/qwen">qwen</a>, <a href="https://simonwillison.net/tags/pelican-riding-a-bicycle">pelican-riding-a-bicycle</a>, <a href="https://simonwillison.net/tags/ai-in-china">ai-in-china</a>, <a href="https://simonwillison.net/tags/nvidia-spark">nvidia-spark</a></p>

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：NASA has successfully tested a system that allows satellites to navigate without GPS by using other spacecraft and debris as landmarks in orbit. In just three days, FALCON also improved the known orbits of more than 200 space objects completely autonomously.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：AI Assistant Instinct Hits $2.5 Billion Valuation In Weeks Amid VC Feeding Frenzy Editors' Pick VCs Are So Obsessed With This AI Assistant That Its Valuation Jumped Fivefold In Weeks A hot new AI agent called Instinct t…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Surgeons aided by real-time analysis of camera footage that identified critical anatomy in brain to be avoided Neurosurgeons in London have performed the world’s first successful AI-assisted operation to remove a brain tumour, saving the sight of a 48-year-old man, health officials have said. The surgery was performed in May at the National Hospital for Neurology and Neurosurgery (NHNN), part of University College London hospitals NHS foundation trust, but details were kept secret until Thursday while the patient, Rhys Hibbert, recovered. Continue reading...