AI各社の公式発表を1画面に。各社のページを個別に開かなくても最新の動きが追えます。
Reflex：視覚グラウンディングされた反応型ヒューマノイド制御
Humans effortlessly react to the world; they adeptly return a tennis shot, catch a falling cup, step aside from a passing cart, or pass a box tossed during truck unloading. These everyday actions require anticipating a moving target and reflexively responding with the whole body within about a secon…
DiffusionART：拡散モデルの効率的で安定したスケーラブルな強化
Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in efficiency by poor credit assignment, while forward-process methods such as DiffusionNFT are efficient yet unstable without carefully tuned exponential-…
REMORY：コンテキスト圧縮のための残差メモリ学習
Long-horizon agents compact their history to continue within a finite context window, but a textual summary alone may not support every subsequent decision. We introduce REMORY, a neural memory network that supplements the summary with a bounded sequence of soft memory tokens. Given the history and…
Memento 3：省察的ルールブックを用いたモデルベース再帰的自己改善
Learning to act in unfamiliar environments requires agents to infer how the world works and revise that understanding as new evidence arrives. Yet limited observations can support multiple world models that explain past interactions but predict different outcomes in unseen states. We introduce Memen…
時空的事前分布を超えて：密集対応マッチングのための汎用的アプローチ
Dense correspondence matching has historically been bounded by simplifying spatio-temporal priors, such as smooth motion and rigid geometry. While effective for classical tasks, these assumptions break down in image editing and reference-guided generation (IEG), where transformations can preserve vi…
TestPrism：単一参照を超えたテスト評価の再考
Large language model (LLM) coding agents have advanced test generation across diverse programming tasks. However, the common practice of evaluating tests against a single reference solution overlooks alternative valid implementations and can overstate test quality. We introduce TestPrism, comprising…
大規模言語モデルの基礎
This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather than comprehensive coverage of all cutting-edge technologies. The book is structured into six main chapters, each exploring a key area: pre-training, generative models, prompti…
OuroWorld：3D世界を多様でエンドレスループする3Dシネマグラフとして実現
Recent 3D world models generate photorealistic, explorable scenes that remain frozen in time. OuroWorld is a mask-free framework that turns any static 3D Gaussian Splatting scene into a 3D cinemagraph: a dynamic scene with vivid, diverse motion looping seamlessly from any viewpoint. A vision-languag…
DreamTrue：反事実後処理を用いたアクション忠実性ロボットワールドモデル
We present DreamTrue, a multi-view, cross-embodiment robot world model for action-faithful and physically plausible video prediction. Training such a model on existing robot datasets faces two obstacles: imprecise calibration can impair action following, while limited coverage of unsuccessful intera…
マルチエージェント一人称視点ワールドモデルの細粒度具現化インタラクション
Egocentric world models predict first-person observations conditioned on an agent's actions, but most focus on a single agent. Real embodied settings often involve multiple agents that act and interact within a shared environment. Existing multi-agent world models rely on coarse actions like locomot…
MiMo-V2.6：自己改善に向けた強化学習のスケーリング
Reinforcement learning (RL) is the central training paradigm for advancing large foundation models towards self-improvement. This report introduces the MiMo-V2.6 series, an omni-modal family that pushes the frontier of model intelligence by scaling RL compute. Prior to RL, we conduct mid-training on…
SuperNav：任意のシーンで任意のタスクに対応するエージェント型ナビゲーションシステム
General-purpose service robots need navigation systems that can handle diverse human requests in unfamiliar environments, combining task generality with scene generality. Some existing methods fine-tune multimodal large language models (MLLMs) to predict navigation actions, making their behavior dep…
TokenRouter：トークンレベルLLMルーティング向け高速配信システム
Large language model (LLM) routing distributes inference work across different models, advancing the cost-quality Pareto frontier of LLM serving. While coarse-grained routing at the session or query level has been widely adopted in production systems, recent algorithmic work shows that fine-grained…
AgentGarten：エージェント進化のためのコード世界
Interactive virtual worlds allow agents to learn through exploration and interaction. What agents can learn is bounded by the environments they practice in, which must be faithful, with consistent state, rules, and dynamics, and realistic, with observations that follow the real-world visual distribu…
Learn2Play Bench：LLMエージェントは未知の環境で経験からどれだけ学べるか
Learning from experience is essential for LLM agents to adapt to unfamiliar and dynmaic environments. Evaluating this ability is therefore important for understanding how effectively agents acquire and use new knowledge. Existing benchmarks have sought to evaluate this ability, but they primarily ev…
存在しないモデルをあなた自身が作った
Deep Research Agent deep-research-pro-preview-12-2025 deprecation : The deep-research-pro-preview-12-2025 agent is deprecated and will be shut down on October 23, 2026 . Migrate your requests to one of the newer Deep Research agent versions: deep-research-preview-04-2026 : Designed for speed and eff…
OpenAI API Changelog 2026-10-08
Feature gpt-6.1-sol v1/responses Added Ultrafast mode for GPT-6.1 Sol in the Responses API. Use gpt-6.1-sol with service_tier: "ultrafast" to reduce the time between generated output tokens. It is available to all API users, subject to rate limits, with global processing and US and EU data residency…
AI対応の「フェイク拠点」作戦の破壊
OpenAI disrupted two AI-enabled influence operations that used false-front journalists and a think tank to spread geopolitical messaging.
Anthropic Cyber Missionの発表
米国の科学発展に向けた取り組みの強化
Anthropic is committing $150 million over three years to the Genesis Mission, a federal initiative to accelerate scientific and technological discovery through AI.
2026年利用規約アップデート
We’re publishing a new version of our Usage Policy. In this post, we summarize the changes we’ve made.
エッジ向けマルチモーダルオープン「d1」判断モデル
インフラストラクチャライフサイクル全体でのAI変革：サプライチェーンから運用オペレーションまで
The opportunity is bigger than making individual tasks faster. It’s to build a system that learns from how infrastructure is designed, sourced, and operated. The post AI transformation across the infrastructure lifecycle: From supply chain to fleet operations appeared first on Microsoft Azure Blog .
Falcon ASRの発表
1つのモデルファミリー、2つの金メダル：IOIとIMOのためのNemotronファインチューニング
Playgroundを導入：カスタムゲームを作成してプレイ
Overview of Playground
ティーンエイジャーの学習と計画を支援し、AIの未来を形作る
College Planner is coming to ChatGPT for Teens to help students manage college applications, alongside new flashcards, quizzes, and a teen AI council.
Radisson Hotel Groupがホテル検索をChatGPTに統合
Radisson partnered with Accenture to build a ChatGPT plugin using OpenAI technology, helping travelers find, compare, and book hotels while planning their trips.
LiveMACE：動的市場でのLLMエージェント機能のプロセス考慮型評価
Evaluating agents by outcomes alone can obscure the capabilities that produce them. This problem is especially pronounced in evolving environments, where outcomes reflect a closed-loop interaction between agent behavior and changing external conditions. We introduce LiveMACEBench, a process-aware be…
SALD：拡散モデル向け自己参照優位学習
Recent work on language-model adaptation has shown that single models can obtain informative training signals by evaluating their behavior in demonstrationor feedback-augmented contexts, with the help of a teacher network, which is driven by the student's learned parameters. Inspired by this interna…
VIS-Ground：コンテキストグラウンディングを用いた動画インタラクティブストーリーテリング
Video interactive storytelling enables viewers to actively steer how a video unfolds. However, once we allow viewers to intervene during generation, a new challenge arises: The viewer's request can have latent dependencies on both the grounding source and the current rendered video state. These depe…
RobotWorld: 多様なタスクと実装形態にわたるロボット使用のためのマルチモーダルエージェントベンチマーク
General-purpose agents increasingly write code, use tools, and complete complex digital tasks, raising the question of how far these capabilities carry into the physical world. To investigate this, we introduce RobotWorld, a challenging simulation testbed for robot use: turning instructions and obse…
RunningTab: 環境側タブを使用した直接ワークスペース操作
Much knowledge work produces new deliverables from files a workspace already holds, and LLM agents are beginning to take such work over. Through direct corpus interaction, an agent can search and read any of those files from a terminal with no indexing, and producing a deliverable from many of them…
Tetris3D: オブジェクトが一致する3Dシーン生成
We propose Tetris3D, a generative framework for single-image 3D scene reconstruction that recovers objects which are physically and geometrically coherent as a scene. Existing methods often generate objects independently or couple them implicitly, providing limited guidance for ensuring fine-grained…
UltraText Bench: 画像生成におけるビジュアルテキストレンダリング評価のための包括的なバイリンガルベンチマーク
Dense visual text requires image generators to reproduce long strings across multiple regions with correct placement and legibility. As short-string rendering improves, evaluation must test sustained performance across more demanding scenes. We introduce UltraText Bench, a bilingual benchmark for pr…
SGF+：自己回帰型ビデオ生成のための勾配フロー分離
Autoregressive video generation requires denoising the current frames while writing their key-value representations as context for future predictions. However, these two roles typically share parameters, and we find that their gradients exhibit distinct patterns and systematic negative alignment, hi…
GRACE：効率的なビデオ生成のための生成対応潜在圧縮
Highly compressed video autoencoders offer an effective way to accelerate video diffusion models, as the Diffusion Transformer (DiT) operates on far fewer tokens. However, such autoencoders are challenging to train, since a higher compression ratio degrades reconstruction quality and recovering it r…
Long-WAM：World-Action Modelのコンテキスト規模の拡張
Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action. We present Long-WAM, a model-system framework for scaling the context of causal world-action models under real-time control constraints. Our central finding is that…
OpenAI API Changelog 2026-10-07
Update chat-latest Updated the chat-latest snapshot, which points to the latest model available in ChatGPT for Plus, Pro, Business and Enterprise users. We recommend leveraging the GPT-6 model family for production API usage, but feel free to use this model to test the latest improvements for chat u…
GPT-6とインテリジェントUIをすべての人へ
GPT‑6 is rolling out globally in ChatGPT with Intelligent UI, delivering faster responses with visuals and interactive experiences you can explore and use directly.
EmbeddingGemma 2：軽量でオープンなマルチモーダル埋め込みモデル
AtlassianとOpenAIが提携を拡大、企業知識を実行可能に
Atlassian and OpenAI are expanding their partnership to connect frontier models with enterprise knowledge and help teams plan, build, and deliver work.
Mistral Large 4を発表
AI数学進展の成果をシェア
OpenAI publishes new results on open problems in mathematics from an internal frontier model and shares Lean proof formalizations and research details on GitHub.
Jump Trading、ChatGPTで量的研究をスケール化
Jump Trading uses OpenAI to expand quantitative research. See how longer-running AI workflows combine multiple data sources with human review.
Ironclad を活用したコンピュータ利用の高度化
Learn how OpenAI and Ironclad are training and evaluating AI agents on complex contracting workflows to advance computer use for professional work.
Falcon-Emiratiが学ぶもの：言語方言、文化、そしてニュアンス