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Зерттеулер1 мин

MintFlow: Minimal Trajectory Intervention for Constrained Flow Matching

Flow matching models excel at generative modeling, and many downstream applications require their samples to satisfy prescribed constraints, such as observed measurements and physical laws. However, existing constrained samplers often face a trade-off: \textit{enforcing…

arXiv — cs.AI
Зерттеулер1 мин

Keep It CALM: Analyzing the Limits of Global Unsafety in Text-to-Image Generation

Training-free safeguards for text-to-image generation often rely on a reusable safety signal, such as an unsafe direction or global toxic subspace, applied broadly across prompts. We provide a controlled geometric analysis of this global-unsafety assumption and reveal a…

arXiv — cs.AI
Зерттеулер1 мин

Traversing the Satisfaction-Diversity Frontier in Text-to-Image Diffusion

Text-to-image generation enables users to explore several images generated from the same prompt. For these generated images to be useful, each one must reflect the user's preferences, measured by a learned reward, and differ visually from the others to maintain diversity.…

arXiv — cs.AI
Зерттеулер1 мин

MEA: A Reward-Driven Multi-Agent System for Faithful Model Explanations

Recent years have seen the employment of a plethora of machine learning (ML) models in high-stakes domains, but they remain largely opaque to the practitioners who act on their predictions. While post-hoc explanation methods offer a lens into this model behavior, wielding them…

arXiv — cs.AI
Зерттеулер1 мин

FlashSinkhorn 2: Block-Sparse Entropic Optimal Transport

Streaming GPU solvers for entropic optimal transport (EOT), such as FlashSinkhorn, avoid storing the dense kernel but still evaluate all $n\times m$ point pairs in every Sinkhorn iteration. We present \textbf{FlashSinkhorn~2} (FS2), a solver for squared-Euclidean cost on…

arXiv — cs.AI
Зерттеулер1 мин

Right Order, Wrong Scale: Auditing LLM Judges for Occupational AI Measurement

LLM judges are increasingly used to assess whether AI outputs meet workplace requirements, but agreement on response rankings does not establish agreement on acceptance rates or occupational aggregates. We introduce O*NET-BENCH, an audit suite derived from an existing survey of…

arXiv — cs.AI
Зерттеулер1 мин

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…

arXiv — cs.AI
Зерттеулер1 мин

Tropical Reinforcement Learning

Reinforcement learning for large language models typically maximizes expected return, adding up the probabilities of all successful trajectories. However, the classical sum formulation can only report how often the model policy succeeds, not which solution actually worked, and…

arXiv — cs.AI
Зерттеулер1 мин

HXAI: Hierarchical Privacy-Preserving Explainable AI in Distributed Energy Systems

Balancing electricity demand and supply is increasingly difficult due to the inherent intermittency of renewable power generation and the stochastic power consumption. Grid operators require fine-grained, decision-relevant insights into household energy consumption to manage…

arXiv — cs.AI
Зерттеулер1 мин

World Editing: Intervening on Executable Worlds at Increasing Depth

Interactive world models are increasingly capable of generating environments and acting within them, yet deliberately editing an existing executable world remains underexplored. We formulate world editing as intervening on an existing world while preserving properties that…

arXiv — cs.AI
Зерттеулер1 мин

How To Train Your World Model: Fine-tuning vs RAG for LM-based World Modeling

World models (WMs) simulate the transition dynamics of environments, enabling agents to plan over the consequences of their actions. In text-based environments, fine-tuning a Language Model (LM) to serve as a WM has emerged as a dominant paradigm. However, despite the widespread…

arXiv — cs.AI
Зерттеулер1 мин

DeReAct: Decomposed Reasoning and Acting for Reliable AI Agents

ReAct-based agents typically rely on a single LLM policy to propose actions, interact with the environment, and decide when a task is complete. This coupling makes action authorization and completion control difficult to enforce independently, allowing errors to propagate and…

arXiv — cs.AI
Зерттеулер1 мин

How to Have a Sensitive Debate: An Instance-Optimal Protocol for AI Debate

As powerful AI systems reach and sometimes surpass the abilities of human experts across a range of cognitively demanding tasks, the problem of accurate oversight and supervision of these systems has become increasingly urgent. One promising approach is AI debate, which seeks to…

arXiv — cs.AI
Зерттеулер1 мин

Learning What to Investigate Next: Meta-Reasoning for Long-Horizon Research Agents

Long-horizon research agents must decide both how to investigate and what to investigate next as evidence accumulates. This is hard to learn because such decisions are sparse in long execution traces, and their consequences may emerge several investigations later. We introduce…

arXiv — cs.AI
Салалық ЖИ1 мин

EmTech Future 2026: When AI Meets Everything

Yossi Matias, Vice President & Head of Google Research, explores how AI is beginning to reshape biology, infrastructure, manufacturing, and science, and why its greatest impact may come when it intersects with other fields. Step inside the newsroom with our MIT Technology Review…

MIT Technology Review — AI (topic feed)
Білім базасы1 мин

DeepSeek Painted My Portrait

Here's the reference image, but I let Deep Seek V4.1 flash use a website that basically replicates the functionality of Microsoft Paint. And here is what it created. It's actually not bad. It's very stylized, but like there's no detail. And so the technique that Astri used of…

YouTube — Matthew Berman
Криптосфера және блокчейн1 мин

Итоги уходящей недели - то, что имеет значение

Итоги уходящей недели - то, что имеет значение Бизнес ИИ- компаний Карта рынка ИИ в клиентском сервисе: куда уходит маржа Как ИИ-агенты могут расшатать экономику маркетплейсов Появилась соцсеть для людей и агентов Вышла платформа, которая выступает закупщиком для экономики…

Telegram — blockchainRF (блокчейн / мозг / space / WEB 3.0)
Стартаптар1 мин

Ұлыбританияның ең үлкен ИИ-суперкомпьютері 2030 жылдарға дейін кешіктірілуі мүмкін — оған электр жетпейді

⚡ Ұлыбританияның ең үлкен ИИ-суперкомпьютері 2030 жылдарға дейін кешіктірілуі мүмкін — оған электр жетпейді. Британиялық стартап Nscale Лондонға жақында 23 мыңға жеткізетін Nvidia чиптері бар деректер орталығын сала жатыр, бұл елдің ең үлкен ИИ-суперкомпьютері болуға тиіс және бұлтты...

Telegram — GPT Time | Нейросети | ИИ
Зерттеулер1 мин

«OpenAI мәдениеті бүлінген» — компанияның ең ескі қызметкерлерінің бірі ИИ қауіпсіздігіне қорқыныштан кетті

⚠️ «OpenAI мәдениеті бүлінген» — компанияның ең ескі қызметкерлерінің бірі ИИ қауіпсіздігіне қорқыныштан кетті. Дэвид Робинсон OpenAI-да үш жарым жыл жұмыс істеді және үлкен запусктар үшін қауіпсіздік есептерін дайындауды басқardı. Енді ол өзіне сенгені үшін кетпекте...

Telegram — GPT Time | Нейросети | ИИ