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:2609.22117v1 Announce Type: new Abstract: Location representations provide mobility models with fundamental information about the spatial position, functional characteristics, and relationships of places. However, existing embeddings are often dependent on mobility observations, unable to represent unseen locations, and weakly constrained to retain geographic distance. This limits their reuse across datasets and mobility tasks. To address these limitations, we propose LE4Mob, an inductive, distance-aware, and geography-derived location embedding framework for mobility modelling. LE4Mob extends contrastive language-location pre-training while introducing a distance-aware regularisation objective that encourages the embedding space to preserve s…

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:2609.22115v1 Announce Type: new Abstract: Zeroth-order optimization (ZOO) estimates updates from function evaluations, making perturbation queries a primary cost. Fixed budgets spend the same number of queries at every step, while adaptive controllers may offset their savings by using additional oracle calls to test estimator reliability. We introduce ZoAQ, an adaptive ZOO method built around query reuse. Rather than discarding past evaluations after each step, ZoAQ makes them useful for both the next update and the decision to query further. This enables adaptive query allocation without extra validation queries. Our analysis characterizes when this agreement identifies an update that supports descent and guides the controller to a sufficient…

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:2609.22113v1 Announce Type: new Abstract: Persistent low retention and completion rates in medications for opioid use disorder (MOUD) have driven the use of machine learning (ML) models to predict retention and identify patients at risk of premature discontinuation. However, the fairness of these models across patient populations remains largely unexplored, raising concerns about their application in treatment decision support. This study systematically assesses algorithmic fairness in ML models for predicting MOUD retention and premature discontinuation and investigates the effectiveness of bias mitigation techniques. Using the cross-sectional Treatment Episode Data Set-Discharges (TEDS-D), which includes treatment episodes for individuals in…

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:2609.22109v1 Announce Type: new Abstract: Selective on-policy distillation trains a student only at the token positions a selector scores highest, and the literature compares selectors under a single shared learning rate--a control chosen to be neutral. We show it is not. Under LoRA on GSM8K (Qwen2.5-1.5B student, 7B teacher), across an 8x learning-rate grid, dense supervision is statistically flat (swing 1.8 pp, p=0.26) while every selective arm moves with the rate: 5.4 pp for a random 5% subset, 6.7 pp for a total-variation selector, up to 17.7 pp for a teachability selector. Consequently the dense-versus-selective verdict reads 10.1 pp at lr=1e-4 but 5.1 pp at 5e-5--a 2.0x difference decided by a parameter the protocol treats as scenery--an…

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:2609.22108v1 Announce Type: new Abstract: Learning-enabled perception is important in many autonomous systems. Unlike traditional sensors, the boundary where ML perception does or does not work is poorly characterized. Incorrect perception can lead to unsafe or overtly conservative downstream control actions. In this paper, we propose a two-step strategy for correcting ML-based state estimation. First, an offline computation is used to characterize the uncertainties resulting from the ML module's state estimation, using preimages of perception contracts. Second, at runtime, a risk heuristic is used to choose particular states from the uncertain estimates to drive the control decisions. We perform extensive simulation-based evaluation of this r…

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:2609.22107v1 Announce Type: new Abstract: Making prediction with multimodal data is widely used in diverse scenarios. Existing multimodal fusion models, once deployed, can only handle predefined modalities (e.g., vision, text and audio) and single tasks, making it difficult to quickly adapt to new downstream applications. Therefore, a natural yet rather aggressive question arises, whether there exists a general multimodal fusion model that can be applied to arbitrary modality combinations and arbitrary prediction tasks. We argue that a unified multimodal fusion model should not depend on specific modalities and instead encode transferable patterns of multimodal correlation. To this end, we propose a simple and effective learning paradigm based…

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:2609.22106v1 Announce Type: new Abstract: Accuracy of Low-bit quantization of linear layers is often dominated by a small number of outliers. Although existing methods, such as smoothing, rotation, or residual-based approaches, may mitigate this problem, they often introduce new accuracy bottlenecks to weights. Besides, most of these techniques are implemented as online approaches, which can result in heavy execution overheads. To address the afore-mentioned issues, We propose PRQuant (Permutation Residual Quantization), a training-free and low-overhead framework that combines channel reorganization with static weight-side residual compensation. After AWQ-style scaling, PRQuant identifies the input channels that contribute most to weight quant…

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：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：Learn how to use Engram effectively, from writing topic descriptions that control extraction to choosing bounded topics and scopes, selecting a retrieval mode, and placing memories in your prompt without hurting prompt-cache efficiency.

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 outlines priorities and principles for rigorous, secure, and independent third-party AI safety assessments of frontier models and safeguards.

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：A hard model swap exposes every user at once, and rolling back means cold-starting the old deployment under pressure. Here's how staged traffic ramps, metric gates, and automatic rollback work on dedicated inference.

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： Last week TypeSafe AI unveiled Jev, their first example of a new category of model that they are calling "System One models" (I'm with Maggie Appleton, I think "decision models" is a better name for these). Jev is an interesting variant on the usual LLM format: it still accepts text inputs, but instead of text output it returns floating point numbers corresponding to categories, yes/no questions, ratings, and associated confidence scores. TypeSafe describe Jev like this: Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out. It's also very fast, and really cheap. Regular LLMs are priced in terms of input and output tokens, with output generally charged at significantly higher rates. Jev char…

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： Cloudflare Python Workers are now generally available After a two year preview, Cloudflare's support for running Python code in their server-side Workers platform is now stable: "Python is now a first-class, fully supported language on the Cloudflare Developer Platform". A neat thing about this is how it works. Cloudflare are running Python compiled to WebAssembly via Pyodide in their V8-based workerd runtime. This comes with some limitations, documented here - most notably both multiprocessing and threading are non-functional in the WebAssembly VM. One particularly interesting detail of this is the local development environment story - their pywrangler development tool (confusingly packaged as workers-py on PyPI) runs a full local simulation of th…

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 definitive Jev podcast with its lead creator.

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：Many developers find that an agent idea works inside Claude Code or Codex, then struggles once they rebuild it with their own loop. The Strands Agents team at AWS is targeting that gap with Strands harness, a fully assembled, general-purpose agent harness. It runs locally or deploys to a cloud provider, ships for Python and […] The post AWS Strands Agents Team Releases Strands Harness: An Open-Source Agent Harness With 28% Lower Token Cost at Comparable Accuracy appeared first on MarkTechPost.

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：At Lib Dems’ conference in Brighton, leader will accuse Andy Burnham of relying on Donald Trump and ‘tech bros’ to solve problem Ed Davey is to call for a global nuclear-style non-proliferation treaty to halt the development of super-intelligent AI when he makes his keynote speech at the Liberal Democrat conference, accusing Andy Burnham of relying on Donald Trump and “tech bros” to solve the problem. When he addresses the faithful on the final day of the gathering in Brighton, the party leader will call for a global pause on super-intelligent AI, comparing it to a nuclear arms race that could result in technology that could “destroy us all”. 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：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：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：California Gov. Gavin Newsom has signed seven bills designed to prevent AI data centers from passing utility costs onto residents, as reported earlier by the Los Angeles Times. The package of laws requires the California Public Utilities Commission to introduce a new rate classification for data centers, while forcing them to pay for upgrades to local power grids and water systems. Other bills included in the package will require proposed data centers to disclose their estimated water use to local governments, along with information about energy efficiency and drought planning. They must also meet certain energy, water, and fuel consumption … Read the full story at The Verge.

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：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：xAI's Grok 4.6 is now available in Amazon Bedrock: a frontier model for long-running agents, coding, and knowledge work, with a 500K token context window and four reasoning effort levels. It runs on both the bedrock-mantle and bedrock-runtime endpoints, with Converse API and cross-Region inference support.

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：Amazon Web Services (AWS) is lifting the lid on a new open source, general-purpose AI agent, designed to give developers The post AWS open-sources an AI agent it says is 45% cheaper than Claude Code and Codex appeared first on The New Stack.

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：Every AI factory needs power and cooling that fit its computing architecture. As AI infrastructure expands, power, cooling, water, site and grid constraints are shaping what builders can deploy. Choosing products that fit the complete factory design helps builders turn computing capacity into useful AI output. To help builders make those decisions, NVIDIA is introducing […]

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：Discussion | Link