花崗岩の埋め込み多言語R2:Apache 2.0 Multilingual Embeddingを32Kコンテキストで開く - ベストサブ-100M 取得品質
花崗岩の埋め込み多言語R2:Apache 2.0 Multilingual Embeddingを32Kコンテキストで開く - ベストサブ-100M 取得品質
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Co-Scientist: A multi-agent AI partner to accelerate research
Introducing Co-Scientist, a collaborative AI partner built with Gemini to help researchers accelerate scientific breakthroughs.
翻訳待ち・原文を表示中 · enAdaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling
Overview of adaptive parallel reasoning. What if a reasoning model could decide for itself when to decompose and parallelize independent subtasks, how many concurrent threads to spawn, and how to coordinate them based on the problem at hand? We provide a detailed analysis of recent progress in the field of parallel reasoning, especially Adaptive Parallel Reasoning. Disclosure: this post is part landscape survey, part perspective on adaptive parallel reasoning. One of the authors (Tony Lian) co-led ThreadWeaver (Lian et al., 2025), one of the methods discussed below. The authors aim to present each approach on its own terms. Motivation Recent progress in LLM reasoning capabilities has been largely driven by inference-time scaling, in addition to data and parameter scaling (OpenAI et al., 20
翻訳待ち・原文を表示中 · enAlphaEvolve: How our Gemini-powered coding agent is scaling impact across fields
Explore how AlphaEvolve's Gemini-powered algorithms are driving impact across business, infrastructure, and science.
翻訳待ち・原文を表示中 · enEnabling a new model for healthcare with AI co-clinician
Researching the path to AI-augmented care and development of an AI co-clinician.
翻訳待ち・原文を表示中 · enWorkflows for work that runs the business
Workflows is now in public preview.
翻訳待ち・原文を表示中 · enGradient-based Planning for World Models at Longer Horizons
GRASP is a new gradient-based planner for learned dynamics (a “world model”) that makes long-horizon planning practical by (1) lifting the trajectory into virtual states so optimization is parallel across time, (2) adding stochasticity directly to the state iterates for exploration, and (3) reshaping gradients so actions get clean signals while we avoid brittle “state-input” gradients through high-dimensional vision models. Large, learned world models are becoming increasingly capable. They can predict long sequences of future observations in high-dimensional visual spaces and generalize across tasks in ways that were difficult to imagine a few years ago. As these models scale, they start to look less like task-specific predictors and more like general-purpose simulators. But having a powe
翻訳待ち・原文を表示中 · enSpaces: A CLI Built for Humans and Agents
Spaces: A CLI Built for Humans and Agents
翻訳待ち・原文を表示中 · enSpeaking of Voxtral
Voxtral TTS: A frontier, open-weights text-to-speech model that’s fast, instantly adaptable, and produces lifelike speech for voice agents.
翻訳待ち・原文を表示中 · enIntroducing Forge
Today, we’re introducing Forge, a system for enterprises to build frontier-grade AI models grounded in their proprietary knowledge.
翻訳待ち・原文を表示中 · enIntroducing Mistral Small 4
Introducing Mistral Small 4
翻訳待ち・原文を表示中 · enMistral partners with NVIDIA to accelerate open frontier models
Mistral partners with NVIDIA to accelerate open frontier models
翻訳待ち・原文を表示中 · enLeanstral: Open-Source foundation for trustworthy vibe-coding
Leanstral: Open-Source foundation for trustworthy vibe-coding
翻訳待ち・原文を表示中 · enIdentifying Interactions at Scale for LLMs
Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI. To gain a comprehensive understanding, we can analyze these systems through different lenses: feature attribution, which isolates the specific input features driving a prediction (Lundberg & Lee, 2017; Ribeiro et al., 2022); data attribution, which links model behaviors to influential training examples (Koh & Liang, 2017; Ilyas et al., 2022); and mechanistic interpretability, which dissects the functions of internal components (Conmy et al., 2023; Sharkey et al.
翻訳待ち・原文を表示中 · enRails testing on autopilot: Building an agent that writes what developers won't
Rails testing on autopilot: Building an agent that writes what developers won't
翻訳待ち・原文を表示中 · enVoxtral transcribes at the speed of sound.
Voxtral transcribes at the speed of sound.
翻訳待ち・原文を表示中 · enTerminally online Mistral Vibe.
Terminally online Mistral Vibe.
翻訳待ち・原文を表示中 · enHeaps do lie: debugging a memory leak in vLLM.
Heaps do lie: debugging a memory leak in vLLM.
翻訳待ち・原文を表示中 · enInformation-Driven Design of Imaging Systems
An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. Many imaging systems produce measurements that humans never see or cannot interpret directly. Your smartphone processes raw sensor data through algorithms before producing the final photo. MRI scanners collect frequency-space measurements that require reconstruction before doctors can view them. Self-driving cars process camera and LiDAR data directly with neural networks. What matters in these systems is not how measurements look, but how much useful information they contain. AI can extract this information even when it is encoded in ways that humans cannot
翻訳待ち・原文を表示中 · en