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:2610.14186v1 Announce Type: new Abstract: Could model-free reinforcement learning algorithms actually be learning a model of the environment? In this work, we apply counterfactual interventions which hold everything about a state fixed, besides a targeted change, to explore if the value function of a Proximal Policy Optimization agent forms a world model of the Crafter environment. Despite only being trained to optimize reward, we discover that the value function learns concepts such as the rules governing when a diamond is collectible, water and food having an impact on health, heuristics regarding inventory, and more. Finally, we discuss the safety implications of our visualizations and some shortcomings of current reinforcement learning age…

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:2610.14180v1 Announce Type: new Abstract: Organisations that want language-model agents for security work face a choice between open-weight models, which they can run and audit themselves, and far more expensive closed frontier models. We study that choice on CTI-REALM (arXiv:2603.13517), a benchmark in which an agent turns a threat-intelligence report into a working detection by querying a live telemetry store. We evaluate twenty-six models, nineteen open-weight and seven closed, under a common harness, scorer and panel of LLM judges. Our main finding is that an open-weight model can match the strongest closed models at less than a tenth of their cost, provided it is given enough turns. The benchmark's published limit on agent messages is a c…

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:2610.14149v1 Announce Type: new Abstract: VETNET represents an ecosystem of live benchmark datasets and evaluation frameworks designed to accelerate AI-driven innovation in animal science and veterinary medicine through standardized, high-quality datasets and multi-institutional collaboration across academic and non-academic sectors. This framework addresses the gap between AI advances in human medicine and the fragmented state of veterinary AI development.

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:2610.14146v1 Announce Type: new Abstract: Long-horizon terminal benchmarks measure whether an agent can sustain progress through extended workflows with process rewards, not only whether its final state passes a binary check. We present Long-Horizon-Terminal-Bench 2.0 (LHTB 2.0), an extension of Long-Horizon-Terminal-Bench that preserves its 46 original tasks while correcting task and evaluation-infrastructure defects and extending 31 tasks. LHTB 2.0 contains 77 tasks across 13 domains and eight task types. The extended tasks are produced through a systematic reconstruction-task pipeline. We start from a working scientific or engineering artifact, then use a parameterized reference implementation to generate multiple scenarios, reveal six as b…

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:2610.14136v2 Announce Type: new Abstract: Iterative sampling in diffusion models can lead to error accumulation due to exposure bias. The denoiser is trained on real images interpolated with noise, but at inference its inputs are constructed from its own imperfect predictions from the earlier steps, something it has never seen at training time. This might be one reason diffusion models often struggle without additional help, such as CFG. We introduce a correction step for diffusion models which attempts to reduce this training-inference gap. The corrector takes the base model's prediction as input and is trained against the same ground-truth target. This makes correction an explicit learning task, with training inputs provided by the base mode…

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:2610.14118v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) has driven their deployment toward general-purpose harness environments, where LLM-based agents operate in sandbox workspaces and interact directly with file systems and shells. While numerous post-training pipelines exist for tool-use agents, their counterparts for harness-based agents remain scarce and are predominantly one-shot and teacher-dependent: a stronger external model synthesizes tasks and collects trajectories, the target agent is trained once, and evaluation results are consumed by human researchers rather than fed back into the loop. Such pipelines are inherently bounded by the capability of the external model and the scope of human pr…

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:2610.14115v1 Announce Type: new Abstract: Large language models have significantly lowered the barrier to writing code. Naturally, this has also led to the development of data science agents that promise automating the design and execution of data science from task formulation to model development. Existing evaluations of agentic data science (agentic DS) assess downstream predictive performance and code correctness, providing preliminary insight in agentic DS capabilities. We argue that these evaluations overlook the fundamental principles of data science: methodological data science- and domain knowledge. As such, only assessing downstream performance masks methodological failures such as data leakage and inappropriate validation, limiting 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：arXiv:2610.14005v1 Announce Type: new Abstract: Under the accelerated pace of AI development, any benchmark whose difficulty is calibrated with humans or current AI systems becomes circular, or saturates soon. How can we then evaluate machines that may become more capable than the current machines and the people who write the tests? One answer to the question hinges on inverse problems, inductive inference questions for which difficulty depends on the complexity of the underlying mechanism, given the observations and the previous knowledge. Levin's Kt, a computable variant of Kolmogorov complexity, puts a theoretical upper bound to that difficulty, balancing the simplest representation encoding for the mechanism and the logarithm of the time steps 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：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： Tool: LaTeX renderer Inspired by Paul Ford and Aboard Foundary's new web app Tiny Little Office, which provides a suite of web app content editors (a Word Processor, Outliner, Presentation Builder, Spreadsheet, Calculator, Drawing Tool, Diagram Maker, Animator, Calendar, and Trip Planner). Tiny Little Office's signature trick is to store all of the document data in the #fragment section of the URL, so every document created using it becomes a bookmarkable and shareable link. It also has an llms.txt file telling agents how to construct URLs that will render as documents. Claude used that file to generate this link to a deck that explains how the URL format works: Tiny Little Office inspired me to have Claude Code (Opus 5.5 Extra) build a similar too…

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：Where to start with agent evals, offline vs online evals, evaluator types, LLM judges, benchmarks with Harbor, multi-turn evals, and keeping costs down.

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：Slide Hub is an internal Databricks app. Field engineers use it to find and reuse...

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：In collaboration with OpenAI, Sullivan & Cromwell developed Agreement Analyzer, an AI tool for identifying issues in a draft agreement.

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：Muse Spark Open, a coding-optimized multimodal model from Meta, is now available on Modal Auto Endpoints.

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：Tool-augmented LLM agents discover relevant APIs from large registries before planning execution, yet finite context windows prohibit injecting all tool definitions into a single prompt. The dominant approach, which we term Retrieve-then-Plan (RtP), embeds the user query and retrieves similar tools via approximate nearest-neighbor search. We study an alternative Plan-then-Retrieve (PtR) paradigm in which the LLM first generates Abstract Tool Stubs (hypothesized function signatures describing needed capabilities) which then serve as retrieval queries against the registry. Evaluating 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：Multimodal agents commonly generate freeform reasoning before each action. For small models, limited model capacity can result in lengthy reasoning that provides little useful guidance for action generation while incurring substantial inference cost. To address this challenge, we introduce Selection-based Structured Reasoning (SSR), a framework that reformulates reasoning as selection instead of open-ended generation. SSR represents recurring high-level reasoning as pre-specified, reusable natural-language candidates. At each turn, the model selects from the reasoning candidates based on their…

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：Meta Muse Spark Open, Meta's open-weight model for agentic coding, is live on Together AI with a 1M-token context window on serverless and dedicated infrastructure.

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：on Mac with Apple silicon lets AI researchers and developers experiment locally with the latest models and methods in a private environment. The open-source array framework leverages the unified memory architecture of Apple silicon, enabling anyone to run new inference and fine-tuning methods on their own hardware and explore other AI techniques. , we highlighted the advantages of the M5 architecture and its GPU Neural Accelerators.MLXIn a previous post

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 v1.39.3 fixes a medium-severity authorization issue in the nearObject GraphQL operator.

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：Datalab released OmniParseBench, an open OCR benchmark with 16,288 yes/no tests on 2,937 pages in 92 languages. Every score is a pass rate you can slice by tag, script or language. The post Datalab Releases OmniParseBench: An Open OCR Benchmark With 16,288 Pass/Fail Tests to Audit OCR Parsers 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：Data teams all get asked the same questions: "what will sales be next quarter?,"...

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：FactSet’s Text-to-Formula (TTF) system has a deceptively difficult job: take a natural-language...

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：Consulting firm McKinsey & Company is going to ask some entry-level candidates to have a 10-minute conversation with an AI simulation of a client as part of the interview process, according to The Wall Street Journal. The AI simulation "aims to reflect 80% of the scenarios McKinsey consultants might encounter in their day-to-day work," the WSJ says, and it isn't designed to "mimic an unhinged client." The AI won't score the candidates - humans will be in the room to assess the candidates and see how well they deal with the simulated situations. The McKinsey tool will be in testing for undergrads seeking business-analyst jobs in "some" offi … 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：Fish Audio released Drama 3, a 35B mixture-of-experts text-to-speech model with about 3B active parameters. It takes plain-language direction, covers 83 languages and ships open weights under a revenue-capped community license. The post Fish Audio Releases Drama 3, a 35B Open-Weight MoE Text-to-Speech Model 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 AI Engineer New York, frontier labs and financial firms explored how to give agents useful autonomy while keeping consequential decisions under control.

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： Tool: Tree Appreciator Another game experiment with Claude. This one used the default Claude iPhone app (chat mode, not Claude Code mode) where I prompted Claude Opus 5.5 Max with: Build a three js game called "Tree appreciator" - it shows you two algorithmically generated trees and the game mechanics is about appreciating the differences between them. Think hard about how to make sure it is both fun and beautiful. Somewhat embarrassingly given my recent talk, I've found that telling Opus 5.5 "make sure it is fun" works uncomfortably well. In this case Claude crunched for 3 hours and 7 minutes before retuning a cosy little spot-the-difference game with music and animation and achievements that's quite compelling to play. Tags: game-design, javascri…

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