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。
Native Action-Prior Learning from Videos for World Action Models
World action models integrate future visual dynamics with robot action prediction, but their scalability remains limited by the need for action-annotated robot trajectories. Observation-only videos contain rich evidence about interaction dynamics, but existing approaches typically use them either to…
再帰的自己改写による複雑タスクのスケーリング軌跡
Successful trajectories on difficult tasks provide valuable supervision for model improvement, but specialized harnesses introduce interventions that may be unavailable during deployment. We propose Recursive Self-Rewrite (RSR), a framework that uses one base model, Qwen-3.8-27B, to discover success…
ChathamがOpenAIで資本市場専門知識をスケール
Chatham Financial uses Codex and GPT-5.6 to build technology and redesign workflows, cutting trade validation from 30 minutes to under 4.
Anthropic、1億ドル投資して1万人のエンジニア育成とエンタープライズAI人材ギャップ解決へ
Claude Frontier Academy trains Frontier Deployed Engineers to the standard of Anthropic’s own — a $100 million commitment to train 10,000 by the end of 2027.
終わらない補完
Advanced AI may matter most for the routine work behind breakthrough ideas. Explore why execution could shape the next economy and the pace of progress.
Gemini Live の Guided Vision、アクセシビリティ対応
stylized hero image reading "Guided Vision"
Albertsons Companies、小売業を内部から再構想
Albertsons Cos. is using ChatGPT Enterprise and the OpenAI API to help teams work faster and make grocery shopping easier for millions of customers.
Olmo-core 3 発表：大規模MoE向けのオープンでスケーラブルな訓練インフラ
UniWAM：統一World-Action Model
Vision-language-action models benefit from the understanding and reasoning capabilities of pretrained vision-language models, but action-only supervision provides limited grounding in world dynamics. Conversely, world-action models inherit spatiotemporal priors from video generation models, yet rema…
AGO AI Quality Gate：検索拡張生成の根拠ベースのリリース判断
Enterprises adopting retrieval-augmented generation (RAG) face a recurring operational decision: promote, revise, or block a system version. The evidence is incomplete and the metrics come from fallible LLM judges. We report on AGO AI Quality Gate (AGO), an evidence-first quality-gate framework depl…
CANOPY：マルチモーダルRAG向けの適応的粒度証拠圧縮
Multimodal RAG retrieves text, tables, images, and videos, but choosing a retrieval granularity does not determine how much context to retain within each item. Coarse units include irrelevant content, while uniformly fine selection can remove context needed to interpret the evidence. Existing compre…
Latent-MOPD: Latent Multi-Teacher On-Policy Distillation
On-policy distillation (OPD) trains a student on the responses it generates. Existing LLM multi-teacher OPD transfers what specialists predict through their output distributions. We introduce Latent-MOPD, to our knowledge the first representation-level multi-teacher OPD method for LLMs. It integrate…
World Action Modeling with Progressive Visual Planning
World action models (WAMs) have emerged as a promising paradigm for robotic control by jointly predicting future visual dynamics and actions from an initial observation and instruction. However, existing WAMs struggle with long-horizon prediction, as generating dense video rollouts is highly ineffic…
RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversations
A companion that talks with a person for months should come to understand them. It should remember what they said, infer who they are, and know when the past bears on the message in front of it. Testing this requires a real person's record, and such records are private, so benchmarks generate the pe…
Ego2Act：一人称動画生成における目的指向マニピュレーションの評価
Video generation models are increasingly being explored as world simulators for embodied planning and learning. To do so effectively, these models must not only generate visually appealing frames, but also predict how environments dynamically evolve when executing goal-directed actions. While evalua…
ループトランスフォーマーのデコード、ほぼ無コストで改善
Looped Transformers achieve parameter efficiency by repeatedly executing a shared block across recurrent loops. Each loop yields an intermediate representation decodable for the same next token, yet standard decoding discards earlier states. Because earlier loops embody less computation, recurrence…
トークン削減、より良いアクション: GPT-6 Astraロボットエージェントが成功率14%向上かつトークン65%削減
Vision language model (VLM) agents can control robots through visual feedback and action primitives, but repeated model invocations and redundant observations incur substantial token overhead. We introduce PyRUA-Lean, an interactive code-execution framework that couples feedback-driven primitive com…
InterEvolve: ヒューマノイドロコモーション操作の報酬プログラムテスト時進化
We study test-time evolution for humanoid loco-manipulation: solving tasks that a controller was never trained for by repurposing its existing skills, improving from its own attempts, and retaining what it learns, without retraining. Our key insight is that a broad controller already holds much of t…
OneStreamer：ストリーミング動画インタラクションにおける知覚、メモリ、プロアクティブレスポンスの統合
Streaming video LLMs must retain evidence before its relevance to future tasks is known and respond when sufficient evidence becomes available. The challenge is to form reusable factual memory without compromising real-time perception. We introduce OneStreamer, which jointly learns query-independent…
ポスト学習段階での損失関数の最適化
An emerging hypothesis about reinforcement learning (RL) post-training of large language models (LLMs) is that it merely sharpens existing behaviors of a base model, improving single-shot accuracy at the cost of solution coverage. Although this trade-off has been observed in math and coding tasks, i…
World Observer：永続的なワールドモデリングのための協調的なアクター・オブザーバー生成
How can a world model continuously observe regions beyond the actor's current view? Video world models simulate how an environment evolves from an agent's actions, yet remain actor-centric. Once an object leaves the actor's view, they lose direct evidence of its evolution, often failing to preserve…
メモリを超えて：明示的な信念状態を使った長時間エージェントの活用
Large language model (LLM) agents can now undertake increasingly complex tasks, but the way they organize interaction history into memory does not ensure a coherent understanding of the current world. We introduce PoS, an inference-time framework that constructs and continually maintains explicit be…
The DenがChatGPT Workで週10〜15時間を解放
As it opens a new location, the social club prepares grant applications in 2 hours instead of 3 days and liquor-license materials in 3 hours instead of 4 days.
Barclays、Claudeの規模拡大により業務効率化と顧客体験を向上
Barclays, the British universal bank, is expanding its strategic collaboration with Anthropic to integrate secure, enterprise-grade AI systems across its global operations.
Gemini 4 Argon：次世代フロンティア知能の時代へ
Stylized promotional blog key art graphic with modern editorial branding and the text "Gemini 4 Argon"
GeminiのSkillsが反復的な業務を自動化
Example skill for presentation prep
SynthID Bio 発表
Proof of concept for watermarking AI-generated proteins while preserving biological function.
組織的なモデル蒸留キャンペーンの中断
Learn how OpenAI disrupted a campaign to extract protected model reasoning and is strengthening defenses against adversarial distillation.
中小企業の AI 活用を支援
OpenAI is partnering with America’s SBDC to expand hands-on AI training and local support for small businesses, alongside a new report on how small teams are using AI.
GroundAnything：並列デコードと高速ビジュアルグラウンディングの両立
Autoregressive (AR) grounding models serialize spatial predictions, introducing sequential latency and imposing a causal order on output tokens. We view grounding as visual evidence extraction: objects, locations, and spatial relations are jointly constrained by the image and query, yet their depend…
GroundingPI: ビジュアルプリミティブを用いた物理インテリジェンス基盤モデル
Precise grounding matters. It specifies which object is the target and where that object is, even in clutter and for tiny objects, and it has to be fast enough for closed-loop control. Yet vision-language-action (VLA) and world-action models (WAMs) take perception from general-purpose vision-languag…
ステレオで世界を聞く：没入型ジョイント映像・音声生成のための動的空間対応学習
Recent joint video-audio generation models have achieved strong semantic correspondence and temporal synchronization. However, applications such as AR/VR and interactive gaming further require stereo audio to provide an immersive sense, which remains largely overlooked. Effective stereo audio requir…
OmniReasoning：オーディオビジュアル結合推論の限界突破
Recent advances have enabled unified omni-modal models in understanding audio, vision, and language. However, existing benchmarks, training data, and learning methods largely treat the modalities independently, leaving the capability of audio-visual joint reasoning poorly evaluated and insufficientl…
FrameMorrow：先読みトークンを使った将来誘導型フレーム選択による長時間動画生成
Long-horizon video generation requires models to effectively leverage an increasingly long generation history. As the generated history grows, retaining all previous content becomes increasingly expensive and redundant, making effective historical selection essential. Existing approaches often deter…
タンパク質折り畳み学習は広範な推論能力へ転移するのか
Large language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them. Protein folding is a natural testbed, because one solved structure yields thousands of exactly checkable spatial and topological statements. We ask: can lea…
動画生成モデル、Post-Trainingと Alignmentの調査
Video generation has rapidly progressed from short, low-quality clips to high-resolution, long-duration sequences with complex spatiotemporal dynamics. Despite strong generative priors learned through large-scale pretraining, pretrained video models often fail to reliably follow human intent, mainta…
EgoTools：実世界の一人称動画におけるツール中心の推論
Real-world embodied tasks, from everyday activities to professional procedures, require agents to act under physical constraints while tracking evolving object and task states. Tool use sits at the heart of such tasks, as many everyday and professional activities are tool-mediated. Understanding the…
現在のシーン以降：能動的視点選択によるイベント参照グラスピング
A robot that observes people interacting with objects should be able to carry out later requests that refer back to those interactions. Such requests may specify a grasp target by the role it played in a past event rather than by its name or appearance. Moreover, the target may no longer be visible…
スパース報酬を活かす：マルチ報酬強化学習での密度認識報酬集約
Multi-reward reinforcement learning trains large language models to satisfy multiple behavioral objectives simultaneously. Reward-wise normalization, as used in GDPO, preserves reward-specific relative information within rollout groups, but different objectives can still exhibit uneven learning prog…
信頼性あるAIレビュアーのための欠落要素：レトリカルロバストネスのベンチマーキングからSciCoreレビューへ
AI reviewers can assign different judgments to manuscripts that report the same science in different wording, potentially rewarding rhetorical optimization over scientific improvement. We formulate Rhetorical Robustness as the joint requirement of stability across content-preserving rewrites and dis…
DyRAD: 動的運転シーンのレーダー新規ビュー合成
Reconstructing dynamic driving scenes from recorded sensor data supports closed-loop evaluation of autonomous driving systems by synthesizing observations beyond the original trajectory. Unlike cameras and LiDAR, radar measures radial velocity directly through Doppler. Yet existing radar novel-view…
Agent Error Dataset：失敗分析と誤り対応ファインチューニング向けに50,000の誤り診断ペアをスケーリング
An unsuccessful LLM agent rollout contains more information than its final reward: the observations available to the agent, the actions it chose, and the environment's responses. Reusing this experience for learning requires identifying a decision to revise and testing a concrete alternative. We int…
More Choices, Fewer Decisions：JEVライク直接決定モデルにおける序列スケール偏向
Direct-decision models turn text into low-latency structured labels and scores, making them attractive for classification and automatic evaluation. Yet reliability requires more than accuracy: a model must also use the ordinal decision scale supplied by the user faithfully. We analyze JEV~1.13 and t…
Mid-Harness：ターミナルエージェント向けモデルとハーネス間のアクション規模化
Terminal agents act through stochastic model generations, yet the ability to generate a useful action does not ensure its reliable execution. A poor command (e.g., wrong package install) can change the environment in ways that hinder subsequent progress, even when the model could generate a better a…
EvoDuet：科学的発見に向けたWebサーチとタスク解決の二段階共進化
Evolutionary search with large language models (LLMs) can stall when progress requires external knowledge the model lacks. Supplying relevant documents helps, but simply adding web search tool can keep returning the same pages as solutions change. We introduce EvoDuet, a bi-level optimization method…
False Frontiers：自己進化型検索エージェントの共同不正行為を診断・緩和
Self-evolving search agents build their own training curricula by jointly optimizing a proposer that generates questions and a solver that answers them. This closed loop introduces a failure mode we call co-cheating: the proposer and solver increasingly agree on shared errors, so internal reward imp…
UniEvo-VL：マルチモーダルモデルの自己改善に向けたオンポリシー自己蒸留トレーニング手法
Modern multimodal models bring generation and understanding into a single unified system, which enables them to provide and learn from their own feedback. Motivated by this unified capacity, we introduce UniEvo-VL, a self-evolving framework for multimodal models to learn from this constructive self-…
Open TTS Leaderboard：多言語テキスト音声合成とボイスクローニングのスケーラブル評価
NVIDIA Kumo Tabularが表形式予測の精度と効率で新境地を開く
事実だけでなくソース情報を正確に把握する：MCPエージェント向けソース対応検証