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。
VisionWeave：MLLMの本質的な能力としてエラスティック視覚表現を統合
Multimodal large language models have become the dominant paradigm for visual understanding, but incur substantial costs by encoding inputs into dense, fixed-size patch tokens. However, visual information is unevenly distributed: some regions require fine-grained detail, while others admit compact r…
U-Space：言語モデルにおける不確実性の発生時期と理由の解明
Large language models are informing decisions with ever-higher stakes. As the consequences of their errors grow, a central question becomes harder to ignore: how much can we trust an individual answer? Yet recognizing when to defer remains difficult because language models can present incorrect conc…
DecepEval：LLMエージェントの欺瞞評価ベンチマーク
As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, raising concerns about their reliable deployment. Existing evaluations show that LLM agents can deceive, but often examine isolated scenarios or narrowly defined conditions, limit…
Recursive Game Creator：エージェンティックな製品レベルゲーム体験フレームワーク
Recent game design agents have made substantial progress in generating playable games. However, program correctness does not ensure an enjoyable experience for players. We present Recursive Game Creator, an experience-oriented harness to advance agentic game development from rough game prototypes in…
nanoMuse：あらゆるデバイス向けのオープンソース個人エージェント
Assistants from 2011 answered and waited, and agents from 2023 did a task and stopped. In September 2026 Meta's Muse showed an agent for one person, with accounts, devices, memory and a conversation that lasts, closed, in a vendor's cloud, in one country. Such an agent is expected to act on a person…
UNREAL：検索と長コンテキストを単一モデルで統一
Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an entire corpus. We ask whether a single model-internal mechanism can select evidence across this range. We introduce UNifying REtrieval And Long-Contex…
CheckerBench：長期的なエージェントは静的解析チェッカーを合成できるか？
Static-analysis checker synthesis requires agents to interpret a defect specification, inspect a repository, implement analyzer-specific logic, and refine the checker through repeated compilation and analysis feedback. Existing coding-agent benchmarks focus on tasks such as patch generation or vulne…
エビデンスからアクションへ: ツール利用エージェントの失敗パターン
Tool-using agents make consequential changes to external state, yet correct outcomes do not guarantee that their actions were supported by evidence established beforehand. We study where this evidence-to-action chain breaks as agents move from deciding whether to act to executing single actions and…
TRACE: MoE言語モデルのFP4強化学習向けロールアウトガイド量子化認識トレーニング
Reinforcement learning (RL) for post-training large language models (LLMs) incurs substantial computation and memory overhead during rollout generation, which motivates low-precision rollout for efficient RL training. However, existing FP4 RL methods suffer from a key limitation: they primarily opti…
クロストークナイザーオンポリシー蒸留の再考: アライメント適用範囲から監督信頼度へ
On-Policy Distillation (OPD) trains a student on its own generations using teacher feedback. With different tokenizers, comparing teacher and student predictions requires alignment at both sequence and vocabulary levels. In this paper, we examine whether expanding this alignment coverage improves le…
Gemini Nano Banana 2.1 generally available (GA) : Released Gemini Nano Banana 2.1 ( gemini-nano-banana-2.1 ), the latest high-efficiency image generation and conversational editing model. An update to Nano Banana 2 ( gemini-3.1-flash-image ), it maintains Flash-level speed and cost efficiency while…
OpenAI API変更ログ 2026-10-06
Feature gpt-6-luna v1/decisions Released the Decisions API in beta with gpt-6-luna . Turn text and images into typed answers 10x faster than the Responses API. / Update Simplified API usage tiers from five to three: Build, Launch, and Grow. Organizations automatically upgrade as total credit purchas…
サイバー検証プログラムの拡充
We’re launching a new, expanded version of our Cyber Verification Program (CVP), which makes advanced cyber capabilities and reduced blocking classifiers available to qualifying security professionals.
EU テキスト出所規則への当社のアプローチ
How OpenAI is approaching text watermarking under EU rules. Learn where watermarks apply, how detection works, and why access starts with researchers.
AIの使い方に合わせた広告戦略構築
OpenAI introduces a new visual ad format in ChatGPT and expands measurement tools, attribution partnerships, and brand suitability for advertisers.
MemPilot: LLMエージェント向けのオンデマンドマルチモーダルメモリキュレーション
Memory has become integral to the LLM agent ecosystem, supporting information retention and reuse across interactions. However, most existing agent memory systems construct memory in a query-agnostic manner, which can incur unnecessary preprocessing cost and discard details that later prove essentia…
MC-Sparse：拡散Transformersの密集注意と疎注意のギャップの解体と解決
Sparse attention is a primary approach to reducing the latency of diffusion transformers in long-sequence generation tasks, such as video and high-resolution 3D asset generation. However, existing methods can degrade generation quality and fidelity at high sparsity levels. Through controlled oracle…
トレースからエージェント的世界へ：インタラクティブ環境シミュレーション用エージェント言語ワールドモデル
Realistic environment replicas are increasingly valuable for training and evaluating LLM agents, yet the original systems may be inaccessible or impractical to reproduce. We explore agentic language world modeling: rather than rebuilding an executable environment, a world model agent serves as the e…
VepAgent：ツール拡張強化学習による因果関係から遷移への橋渡し—ビデオイベント予測
Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in video understanding, yet their reliance on retrospective summarization and text-centric priors often limits their ability to bridge unobserved causal transitions when applied to Video Event Prediction (VEP). To addres…
MiniCorp：AIエージェント企業のラストマイル
The last mile toward enterprise AGI is a company that runs itself. Training and adapting such agents require longitudinal enterprise data, which remain scarce, costly to acquire, and often restricted by privacy constraints. Historical archives are also frequently incomplete and record only what actu…
HuatuoGPT-3: 基盤モデルからのRL単独ドメイン適応
Domain adaptation aims to turn a general-purpose large language model (LLM) into an expert for a target domain. While the dominant SFT+RL pipeline offers a convenient cold start, it may reduce exploration diversity and introduces additional complexity through multi-stage optimization. These limitati…
拡散言語モデルのための表現空間MMD
We introduce a post-training method for diffusion language models (DLMs) that minimizes Maximum Mean Discrepancy (MMD) between generated and reference distributions in the feature space of a frozen pretrained DLM. To estimate MMD, we retain contextual features at individual token positions, obtainin…
オプティマイザの最適化：言語モデルがより高速な分子緩和を発見
Geometry optimization is a major cost in many quantum-chemical workflows: each optimization step requires one force evaluation, and at the density-functional level that evaluation dominates the wall time. Research in this area has produced a broad range of optimization methods, and we ask whether a…
OpenAI API Changelog 2026-10-05
Feature Added an in-product flow for HIPAA compliance support in API Organization settings > General . Admins of eligible organizations can now accept the standard Business Associate Agreement (BAA) and enable HIPAA compliance support for their organization. See the Help Center for eligibility, cove…
AutoSciBench: 科学エージェント評価の自動ベンチマーク生成
As agents rapidly evolve, existing benchmarks can become saturated, limiting their ability to distinguish capabilities and reveal remaining failure modes. Particularly in scientific domains, constructing and updating benchmarks requires substantial time, labor, and domain expertise, making it diffic…
EVISKILL: 再現可能なエビデンスに基づくスキル進化のグラウンディング
Continual skill evolution enables LLM agents to accumulate and refine reusable procedural knowledge from interaction experience without updating model parameters. Its effectiveness depends on determining not only what to change, but also why a change is justified and when it should become persistent…
RobotUse：計算、コンテキスト、決定の配分
Robot agents must connect their intended actions to observed outcomes while retaining the context needed to revise their choices over repeated attempts. Existing interfaces often leave these choices inside predefined tools or require agents to manage detailed execution code and its growing history.…
自己生成フィードバックがテスト時トレーニングを不安定化：長時間適応の因果分解
Test-time training (TTT) lets a model store information in its weights during inference. When the model learns from its own output, however, each update also changes the model that generates the next training example. Across 128K-token streams, retaining generated-text updates worsens prediction on…
SearchJev：サーチエージェント向けの高速で較正済みSystem-1モデル
Search agents repeatedly make short decisions about relevance, evidence sufficiency, and search actions. Using generative language models for these decisions introduces latency and unreliable confidence. We present SearchJev, a fast and calibrated System-1 model that separates search decisions from…
ASCENT：検証済み経験の自己蒸留によるロングホライズンエージェントのオンラインテストタイム学習
A large language model (LLM) agent solves long-horizon tasks through many reasoning-action turns, with one verification signal at termination. Deployed agents face streams of related tasks, making their trajectories a natural resource for improvement. In-context adaptation agents store reflections,…
Kandinsky 6.0 Video：ビデオとオーディオの同期生成向けファウンデーションモデル
We present Kandinsky 6.0 Video, a family of foundation diffusion models for synchronized text-to-audio-video generation, comprising Kandinsky 6.0 Video Lite (3B parameters) and Kandinsky 6.0 Video Pro (29B parameters). Both models generate 5-second video clips with synchronized 44 kHz audio, includi…
エージェント完了を宣言するも、データベースは異議を唱える
推論モデルの自己進化を問い直す：質問の質を問い直す
Self-evolving reasoning models learn from their own generated questions, yet repeated self-training can lead to performance collapse. In this paper, we investigate why performance deteriorates over successive rounds and how to sustain self-evolution. Our analysis identifies two recurring quality pro…
LMBuild：構築可能で機能する構造を生成するLLMエージェントの評価
LLM-based agents are increasingly capable of generating complex 3D structures, with the potential to reshape how objects are designed and realized in the physical world. Yet, producing elegant geometry is fundamentally different from producing objects that can be built and perform their intended fun…
ALoDLM：適応的にループされた拡散言語モデル
Diffusion language models (DLMs) enable fast generation by predicting multiple tokens in parallel, but their practical adoption remains limited by a persistent quality gap relative to comparably sized autoregressive (AR) models. We attribute this gap to a computation-difficulty mismatch: within a pa…
GPT-6ファミリーのモデルガイド
Learn how startups can choose GPT-6 models, tune reasoning effort, improve prompts and skills, coordinate tools, and prepare workflows for production.
AstaBriefをオープンソース化、Astaの高速レポート生成モデル
2026年9月発表の最新AI関連ニュース
A video showing the September AI updates
AutoSynthData：エンタープライズエージェント向けトレーニングデータの自動生成
嗜好とルーブリック報酬を組み合わせた学習後のテキスト画像生成モデルの改善
Recent text-to-image generation models have achieved remarkable visual quality, but improving them through post-training remains challenging because no single reward signal captures the full range of human preference. In this work, we develop a simple and effective post-training recipe for open-doma…
オンポリシー蒸留における成果と崩壊: 強化学習の観点
On-policy distillation (OPD) has become an important approach to language model post-training. However, despite its performance gains, OPD can also collapse into excessively long and repetitive generation, and the mechanism underlying these divergent outcomes remains poorly understood. We explain th…
SEER: 時系列予測のための自己進化イベント推論・検索
Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional forecasting based solely on historical numerical observations insufficient. While language models can retrieve external news, standard retrieval-augmented approaches struggle with high nois…
World Action Learning: インタラクション中心スペクトル潜在ガイダンス
Learning general-purpose robot policies requires large-scale real-world interaction data, yet collecting robot demonstrations remains expensive and difficult to scale. Egocentric videos offer abundant human interaction experience with task-relevant semantics for robotic manipulation, but direct tran…
DuoMatching: 数ステップ動画生成の結合周辺分布マッチング
Streaming video generation has benefited from distribution matching distillation (DMD), which matches the joint distribution of video frames to a video teacher's approximation of the real video distribution. Although this joint matching mitigates drift during autoregressive rollouts, limitations rem…
長編動画生成のためのテスト時分布内強制
Modern autoregressive (AR) video diffusion models excel at short-horizon video generation, yet generating long videos remains challenging due to drifting, where colors and textures shift, and motion dynamics decay. Existing works primarily rely on KV conditioning, which selects or modifies cached ke…
EyeRobot 2.0：リストカメラなしの正確な操作を実現するアクティブガゼシステム
Inspired by human vision, we introduce a framework using active gaze to enable fine-grained bimanual manipulation with only a single stereo camera. EyeRobot 2.0 physically attends to a 3D fixation point in the scene by swiveling two eye viewpoints to center their gaze on it. The resulting images are…
PointWAM：器用なロボット操作向け3D世界アクションモデリング
World action models jointly learn to forecast world dynamics and predict robot actions, such that the learned internal world dynamics guide accurate actions. Existing approaches typically represent the world as RGB frames or latent counterparts while predicting actions as end-effector poses or joint…
Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Models
Masked diffusion language models (dLMs) offer a promising parallel alternative to autoregressive models for complex reasoning. However, they face a distinct credit-assignment challenge, since a few commitments during denoising sharply reduce the uncertainty over the remaining masked positions and sh…
HyperBrowseComp: A Multilingual and Multimodal Stress Test for Web-Browsing Agents
We introduce HyperBrowseComp, a multilingual and multimodal browsing benchmark comprising 423 manually authored and human-validated questions across 13 languages, written by native or highly proficient speakers. Questions are designed to be extremely challenging. Each question targets a concise, pub…