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スクリプト認識Mixture-of-Expertsで実現する多言語シーンテキスト認識の統合
Multilingual scene text recognition (STR) remains challenging due to the scarcity of training data for most languages and the difficulty of serving diverse scripts within a single model. Existing solutions either deploy one recognizer per language, inflating cost and introducing error accumulation,…
GAE：3D一貫性のある世界生成のための幾何学的ネイティブ潜在空間の学習
We present a compact geometry-native latent space as a shared foundation for perception and generation. Visual generators can produce photorealistic frames without preserving a consistent 3D scene. We argue that this is not only a modeling problem but also a representation problem: generators typica…
RULER：SVG 生成のための事例別ルーブリック報酬
Generating Scalable Vector Graphics (SVG) code from natural-language instructions is an open-ended task without absolute visual ground truth, leaving both evaluation and policy optimization without a faithful signal. Scalar metrics (CLIP, Aesthetic) calibrated on natural images transfer poorly to st…
onPanda: トークンレベル補正によるLLMとエージェント向けオンポリシー学習データの効率的アノテーション
We present onPanda, an interactive tool for efficiently annotating LLM alignment data and agent trajectories. onPanda adopts token-level correction as its core interaction: while reading a model response, the annotator locates the first inappropriate token and either picks a substitute from the mode…
VideoGen-Agent: ビデオ生成エージェントの強化
Recent advances in video generative models have enabled high-fidelity, temporally coherent video generation. However, these models often struggle to satisfy prompts requiring specialized knowledge, specific identities, physical consistency, or ordered events. In this paper, we present VideoGen-Agent…
Document Retrieval-Aware Chunking (D-RAC): PDF正規化とマルチモーダルMarkdown変換によるエンタープライズドキュメントの汎用検索対応取り込み
Retrieval-Augmented Generation (RAG) systems over enterprise knowledge bases must ingest heterogeneous document formats -- PDFs, Word documents, presentations, and scans -- whose content is locked inside complex visual layouts, multi-column pages, and dense tables. Rule-based extraction and OCR dest…
GameHorizon Suite: ゲームプレイにおける多段階データ評価
Modern video games provide a measurable testbed for AI models, combining abilities of visual understanding, instruction decomposition, goal planning, and precise action control over multiple temporal horizons. Existing datasets and benchmarks, however, either cover a narrow range of games, lack lang…
RRSI: エージェントハーネスの正則化再帰自己改善
An LLM agent's capability is largely magnified by its harness, namely the prompts, control flow, tooling, memory, and context management surrounding the frozen backbone model. Recent methods increasingly automate this process by iteratively proposing and selecting component-wise edits of an agent ha…
WorldCrafter: 暗黙的な3D対応メモリを持つ一貫したビデオワールドモデル
Video world models enable interactive exploration of dynamic environments, yet struggle to respect prior observations over long horizons and across viewpoints. We present WorldCrafter, a video world model that learns a camera-queryable implicit 3D-aware memory for this purpose. The key insight is to…
tokenizers v1: エンコード、デコード、スケーリング計測
V7がAIエージェントに機関的記憶を付与
Using GPT-5.6, V7 turns scattered company files into context agents can use to complete complex, source-linked work.
OmniEcho：体現化エージェント向け空間音響理解
Humans can effortlessly localize the direction of a sound source and integrate it with visual cues for reasoning, yet this remains challenging for embodied agents. In particular, it is still unclear how to effectively evaluate and model spatial audio understanding in embodied settings. To address th…
Grounded Action Model：ロボティクスの基盤となる 3D グラウンディング
Manipulation policies must know which objects matter and where they are, yet the pretrained backbones that current robot foundation models build on, from language in vision-language-action models (VLAs) to video generation in world-action models (WAMs), do not directly require this metric grounding,…
RewardVerse：動画報酬モデリングのための評価基準ガイド付きポリシー最適化
Reinforcement learning (RL) is vital for optimizing video generation models, with a robust reward model (RM) serving as the cornerstone. However, existing video reward models often produce unstable scalar scores because they directly map complex, subjective video quality into a single score without…
Spatial-Interactor：物理世界との相互作用を通じた空間推論学習
Spatial reasoning is essential for vision-language models (VLMs) to understand and act in the physical world. Reasoning in dynamic environments requires VLMs to perceive local state transitions caused by object motion and viewpoint changes and integrate them over long trajectories to maintain an upd…
OmniEdu: 学習と教育向けオープンファウンデーションモデル
Educational foundation models must solve problems, understand curriculum structure, diagnose learner difficulties, and provide appropriate instructional support. Existing educational language models often focus on either problem solving or tutoring, with training mixtures organized by source or task…
VLMの知能をロボット制御に転送
Humans can seamlessly adapt to both physical and digital worlds, suggesting that while a digital-to-real gap exists in embodiment, environment and task, human intelligence itself may transfer across this gap. This naturally raises a fundamental question: can the intelligence of vision-language model…
Googleの「AI & Economy」チームに新たな専門家が参加
Text "AI & Economy Research Program" all over a green grid background, with the Google G logo in the bottom right corner
Googleとファッション業界の未来をともに創造
Jane Wade and Sergio Hudson
オーストラリア青少年安全ブループリント発表
OpenAI introduces the Australian Youth Safety Blueprint, a six-pillar roadmap for safer AI experiences that protect and empower young people.
OmniVChat: ネイティブ音声映像対話の統合、ベンチマーク、訓練
We define OmniVChat (Omni Video Chat) as the task of native audio-visual dialogue between a user and an omni model. In OmniVChat, omni models directly and simultaneously receive audio and video from a user and return text. The user's query is embedded in the audio and video, without a separate text…
Gemini 2.5 models access update : To ensure reliable performance for everyone, we are limiting access to the 2.5 models to users who have actively used them in the past. These models are not deprecated and will continue to be served until further notice through the API. For any new projects, use our…
RecreationWorld: ハイブリッドコンピュータユースエージェント向けのスケーラブルで検証可能な環境
Computer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line. Real digital work requires both, interleaved rather than stacked end to end. We study hybrid CUAs that autonomously decide when to explore an interface…
IntBMoE: 専門家合成における完全参加型Mixture-of-Expertsへのブロックレベル条件付けの統合
Mixture-of-Experts (MoE) scales capacity, but existing designs cannot set three quantities independently. For a single token, participation is how many experts contribute knowledge to its output, execution is how many are actually computed (compute cost), and materialization is how many expert-sized…
CodeMidas：コード自体からのエージェント型コーディングRL環境のスケーリング
Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, while existing methods typically rely on development artifacts such as issues and commits, limiting the range of tasks that can be e…
Accentureとのパートナーシップで組込型評価を推進
We’re partnering with Accenture on independent evaluation of frontier AI—part of our recent commitment to embed evaluators at Anthropic. Both we and Accenture expect to invest at least $1 billion to build capacity in this area over the next five years.
Sakana AIの新プロジェクト「Frontier Intelligence Group（FIG）」発表
グローバルデータの探索をより簡単に
UN System Data Commons Data webpage
Cooley、ChatGPTでIPO業務を高速化
Cooley built GO Public with ChatGPT Work to bring intelligence to the IPO process, helping lawyers surface issues earlier and focus judgment where it matters most.
JEPA-Anything: 異なるワールド間での予測モデル学習
World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction. Yet predictive models remain domain-specific: can a common learning principle support world modeling across radically different systems? We introduce JEPA-Anything, a domain-agnostic fram…
EOSトークンの不一致: オンポリシー蒸留における長さインフレーションの理解
We study length inflation in on-policy distillation (OPD), where student responses can become excessively long and even exhaust the generation budget. We identify termination-token mismatch between base students and post-trained teachers as an important source of this behavior. Across Qwen3, Llama,…
コーディングエージェント向けハーネス設計の実証的研究
Coding harnesses shape how autonomous coding agents translate model capabilities into long-horizon software-engineering performance, yet existing work typically evaluates harnesses as monolithic systems, leaving the effectiveness of individual components unclear. To enable component-level comparison…
SoL-Pi：効率的エージェントハーネスのための再帰的自動研究ループスケーリング
As coding agents move from supervised code completion to unattended, around-the-clock exploration, their work expands from isolated predictions into long trajectories of reasoning, tool use, and feedback. Token efficiency therefore becomes important for scaling recursive self-improvement. We take an…
DeepSeek-V4.1-Flash：KVキャッシュ圧縮の限界に挑戦
The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation, prefill remains computationally expensive, and large KV caches continue to strain HBM and SSD capacity and data-transf…
Antigravity Agent 09-2026 : Released antigravity-preview-09-2026 , which replaces and deprecates antigravity-preview-05-2026 . If you run on a remote sandbox ( environment: "remote" ) and read only output_text or model_output steps, update the agent string and nothing else changes. If you run tools…
法務向けAstraの発表
OpenAI for Law brings frontier intelligence for law, custom firm workflows, connected legal data sources, and legal-grade controls for confidential client work.
ライフサイエンス検証プログラムの開始
Sakana AIのプロダクトチーム内部
モデル非整合性報告の枠組み
OpenAI shares a framework for tracking, investigating, and disclosing model misalignment, alongside six reports of unexpected or concerning model behavior.
高齢者のAI日常活用をサポート
OpenAI and AARP are bringing free, hands-on ChatGPT workshops to 1,000 older adults across 10 U.S. cities to build practical AI skills safely.
AIで広告の新たな形を構想する
Explore new AI-powered advertising experiences from OpenAI, including Sponsored Agents, tools for marketers, and integrations with HubSpot and Shopify.
HexがGPT-6 Astraで複雑な分析をビジュアルレポートに変換
GPT-6 Astra helps Hex’s data agents turn answers into interactive visualizations that employees are proud to share.
AI利用をビジネス価値に結びつける方法
Learn how ChatGPT Work and Codex analytics help teams understand AI usage and spend, identify training needs, and connect adoption to business outcomes.
MistralとMozillaがオープンでプライベートな多言語AIをWebブラウザに統合
Open, private and multilingual AI is coming to your web browser. Mistral and Mozilla team up to put powerful, trustworthy AI where you already browse.
労働者が新しい働き方を切り開く
New OpenAI Economic Research shows how workers use AI beyond traditional roles and which new activities become recurring parts of their work.
RayOrch: ファウンデーションモデルのデータ準備のためのリネージ制御マルチグレイン データフロー のプログラミングと実行
Preparing high quality training data for foundation models requires scalable pipelines that transform heterogeneous documents and videos into structured records. Such pipelines expand each parent item into an ordered and input dependent sequence of children, whose counts may be long tailed. GPUs sho…
MiniMax-H3は物理世界について推論できるか? マルチモーダル生成モデルの評価
Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H3 exemplifies this transition by combining multimodal context understanding with joint audio-visual generation in a shared latent framework. Its unif…
ActionPiece：自動回帰型ビジョン言語アクションモデルのアクショントークン化を再考
Action tokenizers play a central role in autoregressive vision-language-action (VLA) models, determining both the targets for policy training and the executable commands recovered from predicted tokens. Their fidelity is commonly evaluated using pointwise reconstruction metrics such as mean squared…