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Mosaic：マルチセッション エージェント強化学習
Most existing agentic reinforcement learning methods assume single-session trajectories, where interaction proceeds within one continuously accumulated context. However, agentic techniques such as context compaction and subagent forking may produce trajectories spanning multiple sessions, creating a…
OpenGCUA：コンピュータ利用の汎用AIに向けた完全オープンスタック
Computer use has reached a turning point: the focus is expanding from GUI-only interaction to systems that complete tasks on users' computers through visual and programmatic interfaces. We define a generalist computer-use agent (GCUA) as a system in which the model chooses how to observe, act, and o…
MJev-Spatial：ネイティブアテンション マッチング経由のSystem One空間インテリジェンス
Spatial intelligence lets an agent perceive and act in the physical world, and many of its judgments must be made at interaction rates: which spatial relation holds, how far an object is, or where to place it. Vision-language models (VLMs) produce these answers by free-form text generation, paying f…
Waypoint-OPD：ゲートル付きオンポリシー蒸留による強化学習誘発ポリシーシフトの転移
Policy-shift distillation reuses the difference between a teacher's log probabilities before and after reinforcement learning (RL) as supervision on student-generated trajectories. However, this endpoint policy shift cannot reveal whether local action preferences evolved consistently during teacher…
OpenAI API Changelog 2026-10-15
Feature gpt-live-1 gpt-6-luna v1/live/sessions Added native Decisions integration in GPT-Live . Your app can act while your user is speaking or react to what Live says. Let Live call Decisions directly through native delegation, or trigger decisions as input or output transcript deltas arrive.
Sullivan & Cromwell、契約審査に法務専門知識を導入
In collaboration with OpenAI, Sullivan & Cromwell developed Agreement Analyzer, an AI tool for identifying issues in a draft agreement.
FLUX.3 Image KleinのDiffusers対応開始
Muse Spark Openのご紹介――MetaのオープンウェイトフロンティアマルチモーダルMoEモデル
独立評価者に関するアップデート
Anthropic announces updates to embedding independent evaluators
HiRO-ACEv2：極端な気象を3km解像度で従来の一部コストでシミュレーション
SentinelOne、マルウェア分析を数週間から数時間に短縮
With OpenAI Daybreak, SentinelOne cuts weeks of malware analysis to hours, creating capacity to investigate more threats.
AmpがOpenAIモデルでAIエージェントをコード以外の領域で活用
See how Amp pushes the frontier of what agents can do with GPT-6 Astra, Ultrafast, GPT-Live-1, and Sign in with ChatGPT.
BlockがOpenAIテクノロジーを活用したAI駆動セキュリティを構築
Block is building AI-powered security with OpenAI. Its security team investigates threats faster and proposes patches in minutes.
Third Moment Research、1万台のエージェントを36時間以上運用する方法
With GPT-6 Astra and GPT-6.1 Sol, Codex, and Daybreak for cybersecurity, this AI lab builds agents that focus for days and improve with use.
τ-Emotion-Bench：実世界のユーザー感情を通じたLLMエージェントの相互作用脆弱性の露呈
In long-horizon tool-calling tasks, LLM agents can fail not only because of tool-use errors, but also because of ineffective interaction with users. Yet existing benchmarks largely assume cooperative and behaviorally stable users, leaving the effect of emotional variation on agent reliability undere…
Google DeepMindとシンガポール政府、詐欺検知向けのファインチューニングAIモデル構築で協業
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
マルチモーダルエージェントメモリの進化——見る、保つ、学ぶ
Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities. However, enabling multimodal agents to continually acquire useful information from complex, heterogeneous multimodal d…
関数空間時間自己蒸留によるポリシー最適化
Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in language models, but scalar outcomes provide coarse supervision over response trajectories. Self-distillation offers denser token-level guidance by comparing an unassisted policy with a feedback-informed self-teacher, yet th…
生成が知ること: 統一マルチモーダルモデルの細粒度理解を解き放つ
Fine-grained visual understanding requires distinguishing subtle differences in local attributes and spatial relations, yet remains challenging for existing vision-language models. Unified multimodal models (UMMs) offer a new opportunity through generation pathways that preserve rich visual informat…
PrismaCoder：分解ルーブリック合成によるオープンマルチモーダルコード知能の境界拡張
Multimodal code intelligence connects the symbolic world of programs with the visual world their execution creates. Interest in this capability is burgeoning, as models are increasingly asked to produce charts, webpages, animations, and semi-structured documents. Recently, proprietary models have pu…
拡散モデルの補正トレーニング
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 t…
インタラクティブビデオ生成における投機的サンプリング
Interactive video generation commonly relies on small bidirectional chunks to reduce response latency. Yet our preliminary experiments show that larger chunks improve video quality and better utilize GPU parallelism, allowing more latents to be sampled with only a modest increase in sampling time. M…
BizBench：実世界のビジネスで長期目標エージェント導入の隠れた課題を明らかに
Existing long-horizon agent benchmarks often emphasize final task success while providing limited assessment of intermediate execution validity. Yet real-world business operations require agents to navigate extensive, potentially outdated or conflicting policies while satisfying critical constraints…
MAI-Gym：モバイルとコンピュータ環境におけるGUIエージェント評価と強化学習訓練のためのオープンオールインワンプラットフォーム
Online reinforcement learning (RL) with verifiable rewards has driven rapid progress in mathematics, coding, and tool use, but scaling this success to GUI agents remains challenging due to the high cost of interactive environments and the scarcity of realistic training tasks. We introduce MAI-Gym, a…
DeltaStreamer：人間らしいエージェント的ストリーミング動画理解を目指して
We propose DeltaStreamer, a novel agentic framework for streaming video understanding inspired by how humans observe, remember, and investigate. Streaming video understanding requires a system to retain evidence before knowing which details future queries will need. Existing approaches often obscure…
RoTA-Bench：インタラクティブな投影アライメントを通じた3D空間推論評価
Can vision-language models turn 2D observations into 3D understanding that supports precise action? This requires inferring object geometry, anticipating the effects of rotation, and refining actions through visual feedback. We introduce RoTA-Bench to evaluate these abilities through interactive 3D-…
InfiniSplat v2：任意視点からのフィードフォワード3DGS分析と改善
Feed-forward Gaussian reconstruction builds 3D scenes directly from input images for novel view rendering, but learning Gaussian attributes from predicted geometry for high-quality rendering under large viewpoint changes remains challenging. We analyze limitations in Gaussian attribute learning and…
SwarmRSI：進化するスワーム科学エージェントを通じた再帰的自己改善
Recursive self-improvement (RSI) in scientific research aims to improve agents' research capabilities through their own experimental experience. Scientific agent swarms can combine evidence and implementations from different research directions to guide their agents' subsequent experiments. However,…
ActionOPD: トークンではなく決定を蒸留する
Distilling capable language-model agents into smaller models requires aligning the student's behavior with the teacher's. This raises a fundamental question: at which states and at what granularity should this alignment be performed? Offline imitation provides dense but token-specific supervision an…
Cortex: コンテキストをパラメトリックメモリに統合
Context can improve large language model performance, but these gains may be lost after context removal. Inspired by biological systems consolidation, we formulate Memory Consolidation (MC) as internalizing these gains while preserving non-target capabilities, connecting context-specified editing, s…
RuleTrek: 複雑なシナリオにおけるエージェント的ルール推論のベンチマーク
Large language models (LLMs) increasingly tackle complex real-world tasks in specialized domains. However, identifying and correctly applying domain-specific rules remains challenging when scenario information is distributed across multiple files. Existing rule-reasoning benchmarks typically represe…
Critique-Guided On-Policy Distillationによる信頼性の高い教師信号の復元
On-policy distillation (OPD) trains a student model on its own rollouts with token-level supervision from a teacher, assuming that the teacher can reach a correct answer from any prefix the student writes. We call this property the recoverability of the teacher and show it fails on incorrect rollout…
Doc2Harness: コーディングエージェントの長期的なハーネス構築におけるLLMのベンチマーク
Agents driven by large language models are increasingly deployed on long-horizon tasks, where they use tools and modify real workspaces. How well they perform depends as much on the harness around the model as on the model itself. Existing benchmarks measure downstream task success under a fixed har…
Player-1: 合成体験から学習する
Computer-use agents have demonstrated strong capabilities in interpreting screens and executing keyboard and mouse actions. However, games, with their visually rich worlds and diverse mechanics, remain challenging and offer a demanding next step toward general digital control. Current game-agent lea…
2026年米中間選挙に向けた準備
Earlier this year, we shared an update on our election safeguards. Here, we outline how we’ve continued to strengthen these safeguards ahead of this year’s US midterms.
EUのAIの次の章：エージェント型へ
New analysis shows how agentic AI adoption is growing across EU enterprises and small businesses—and where deeper use could unlock more value.
Anthropicがシンガポールオフィスを開設、シンガポール政府とMOUに署名
Anthropic opens its fifth Asia-Pacific office in Singapore and signs an MOU with the government on AI safety, evaluation, and responsible adoption.
Gemini 4 Argonの責任ある安全な開発
Gemini 4 Argon logo next to a large white "4", all on a gradient blue background
LFM2.5-24B-A2Bで高速・プライベートなオンデバイスエージェント実行
フロンティア時代のインフラストラクチャ：オープンな業界協力を通じたインテリジェンスのスケーリング
AI is reaching such a pivotal moment, and the focus is shifting from building larger frontier models to intelligence that delivers practical impact. The post Infrastructure for the frontier era: Scaling intelligence through open industry collaboration appeared first on Microsoft Azure Blog .
ロボティクス向けGemini 4 Argon
Gemini 4 Argonが開発者向けに利用可能に
Build complex software and cybersecurity workflows with Google Gemini 4 Argon. Check out the Gemini API Developer Guide and read the Google Antigravity and Android Studi…
Gemini 4 ArgonがGeminiアプリに登場
Gemini 4 Argon brings frontier reasoning and agentic coding to the Google Gemini app. Google AI Pro and Ultra subscribers can now access Gemini 4 Argon.
Gemini 4 Argon：最難題の解決に向けて設計
TBD
Higgsfield、AI動画を1分から10分以上に拡張
Higgsfield brings creators longer videos with GPT-6 Astra, precise visuals with GPT-Image-2.5, and a path to voice filmmaking with GPT-Live.
ソクラテス的スキル学習：転移可能なエージェントスキルのための教育と実践のインターリービング
Agent Skills improve LLM agents by making task knowledge reusable across sessions. Learning transferable Skills requires evidence about a procedure and its conditions of use, which a successful task attempt can leave untested. We introduce Socratic Skill Learning (SSL), which interleaves expert teac…
未知へ：オープン科学研究課題でのAIエージェントベンチマーク
Can AI agents carry out research on open scientific problems? We introduce Into the Unknown, a benchmark that provides an agent with a science paper along with a research direction to pursue and tasks it to write a follow-up paper. We work with scientists at US Department of Energy (DOE) national la…
Self-Evolving Agent Harness
Harness self-evolution revises an agent's software scaffolding, including prompts, memory, workflows, and executable components, while keeping model weights fixed. We formalize a feedback-driven process in which accepted revisions persist across runs. The framework separates update targets from prop…