Стартапы1 мин
Ансамбльное прогнозирование с помощью моделей временных рядов IBM Granite
Сообщение в блоге IBM Research об «Обнимающем лице»
Hugging Face Blog
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Все материалы
Все опубликованные аналитические материалы, исследования суверенного стека, новости экосистемы и обзоры моделей ИИ.
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Материалов
Исследования1 мин
В ChatGPT можно скрывать переписки под PIN-кодом или Face ID
🔒 В ChatGPT можно скрывать переписки под PIN-кодом или Face ID OpenAI начала постепенно запускать функцию Locked Chats, которая позволяет защитить отдельные переписки в ChatGPT от посторонних. Пользователи могут скрыть личные или конфиденциальные чаты из обычной истории и…
Telegram — BlueScreen.kz
Исследования1 мин
Европейские пионеры используют модели NVIDIA Earth-2 для прогноза погоды
Модели NVIDIA Earth-2 помогают прогнозировать погоду и климат в Европе.
SMNTCN (Semanticon) — глобальные новости ИИ
Исследования1 мин
NVIDIA трансформирует европейскую розницу с помощью ИИ и роботов
NVIDIA внедряет открытые модели и физический интеллект в европейскую розницу.
SMNTCN (Semanticon) — глобальные новости ИИ
Исследования1 мин
Школьники из Казахстана стали лучшими на престижном конкурсе на Бали с проектом, который проверяет ошибки ИИ
Два школьника из Алматы получили серебро на IRCYS 2026 на Бали. Их система PlanLens находит ошибки в планах ИИ-агентов.
DigitalBusiness.kz
Исследования1 мин
Claude теперь сам предлагает, что сделать: Anthropic запустила проактивного ИИ-агента
📎 Claude теперь сам предлагает, что сделать: Anthropic запустила проактивного ИИ-агента Компания запустила тестовую версию Claude Duet — проактивного ИИ-агента на базе модели Claude Opus 5.5. В отличие от обычного чат-бота, он не ждет постоянных команд, а самостоятельно…
Telegram — BlueScreen.kz
Исследования1 мин
Anthropic представила Claude Duet, автономного ИИ-агента
Anthropic представила нового ИИ-агента под названием Claude Duet. Он призван облегчить повседневную работу профессионалов и владельцев малого бизнеса: они смогут планировать встречи, делать финансовые прогнозы и разрабатывать веб-приложения, сообщает Tech-news. Главный…
Tech-news.kz
Исследования1 мин
Anthropic представила Claude Duet — персонального ИИ-агента, который работает без постоянных команд
⚡️ Anthropic представила Claude Duet — персонального ИИ-агента, который работает без постоянных команд Новый сервис построен на базе Claude Opus 5.5 и станет конкурентом Dots от OpenAI и Grok Bot. Duet работает в облаке, использует браузер и подключённые приложения, выполняет…
Telegram — GPT Time | Нейросети | ИИ
Исследования1 мин
Тест-драйв под дождем и курс на искусственный интеллект
Тест-драйв под дождем и курс на искусственный интеллект Kapital.kz
Kapital.kz
Исследования1 мин
Anthropic решила бросить вызов OpenAI Dots и Grok Bot, выпустив Claude Duet на базе невероятно мощной Opus 5.5.
Anthropic решила бросить вызов OpenAI Dots и Grok Bot, выпустив Claude Duet на базе невероятно мощной Opus 5.5. • Доступен на тарифе Claude Pro (для Dots нужна подписка ChatGPT Pro за $100 в месяц). • Работает на Opus 5.5, которую называют лучшей моделью в мире. • Имеет…
Telegram — Вайб-кодинг
Исследования1 мин
Forging More Tasks into Better Harnesses: Agentic Harness Evolution via Successive Scale Racing
arXiv:2610.28815v1 Announce Type: new Abstract: Agentic harness evolution optimizes an agent's instructions, tools, and control flow by using its task execution experiences as evolving evidence. While more evolution tasks should intuitively improve performance, our preliminary…
arXiv — cs.AI
Исследования1 мин
Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
arXiv:2610.28698v1 Announce Type: new Abstract: Scaling data, diversity and model capacity has improved embodied learning in physical environments, where diverse experiences remain grounded in shared physical laws. Whether these gains extend to virtual worlds with different…
arXiv — cs.AI
Исследования1 мин
SpecEvo: Speculative Evolution with Large Language Models for Cost-Efficient Scientific Discovery
arXiv:2610.28706v1 Announce Type: new Abstract: LLM-driven evolutionary search is a promising paradigm for scientific discovery, but existing methods remain costly and slow, often applying expensive reasoning uniformly while underusing evidence from failed, redundant, and…
arXiv — cs.AI
Исследования1 мин
Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?
arXiv:2610.28763v1 Announce Type: new Abstract: Frontier coding agents are increasingly framed as end-to-end automated researchers, and ML for science offers a demanding test of that promise: improving a scientific model often requires domain insight beyond routine ML…
arXiv — cs.AI
Исследования1 мин
A Small Model Asks Better: Fine-Tuning Query Clarification for Retrieval-Augmented Generation
arXiv:2610.28686v1 Announce Type: new Abstract: Query clarification is essential for retrieval-augmented generation (RAG) systems that face ambiguous user queries. We consider multi-turn grounded conversational scenarios where a user query is either ill-formed or ambiguous in…
arXiv — cs.AI
Исследования1 мин
DiffResearch: Accelerating Deep Research with Diffusion Language Models
arXiv:2610.28909v1 Announce Type: new Abstract: Deep research systems iteratively plan, gather evidence, synthesize, and revise, making end-to-end latency a key bottleneck for interactive use. Almost all production systems are built on autoregressive (AR) language models and…
arXiv — cs.AI
Исследования1 мин
Decision language models as affordable and rapid alternatives to LLM judges of clinical generative AI
arXiv:2610.28826v1 Announce Type: new Abstract: Automated judges of clinical artificial intelligence (AI) outputs are necessary, but large language model (LLM) judges are costly and slow at scale. We compared Jev, a decision language model that returns structured judgments, with…
arXiv — cs.AI
Исследования1 мин
Hidden Messages Between Diffusion Language Model Agents: Causal Evidence for Latent Communication
arXiv:2610.28905v1 Announce Type: new Abstract: Diffusion language models form answers through a sequence of revisable continuous states, but multi-agent systems usually discard those states and make agents communicate only after decoding text. We ask whether one independent…
arXiv — cs.AI
Исследования1 мин
Simply Logical -- The First Three Decades
arXiv:2610.28912v1 Announce Type: new Abstract: This paper charts the evolution of the Prolog textbook Simply Logical -- Intelligent Reasoning by Example from print with runnable programmes on a 3.5-inch diskette published in 1994, via various intermediate online versions, to…
arXiv — cs.AI
Исследования1 мин
Back Up Before You Regret: Measuring Backup Awareness and Automating Backup in Coding Agents
arXiv:2610.28934v1 Announce Type: new Abstract: Coding agents now complete tasks autonomously, modifying real-world state such as artifacts, software environments, and configurations along the way. Yet they show limited backup awareness: recognizing that an action will modify…
arXiv — cs.AI
Исследования1 мин
Self-Improving Agents with World Model Predictive Paths
arXiv:2610.28945v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly expected to interact with environments and improve their decisions during deployment. However, how anticipating future outcomes and drawing on accumulated interaction experience…
arXiv — cs.AI
Исследования1 мин
Speedrunning Relational Foundation Model Pretraining
arXiv:2610.28855v1 Announce Type: new Abstract: Progress on foundation model pretraining is gated by who can afford a trial, and relational pretraining is no exception. Recipes are developed inside individual labs, reported under private protocols, and do not accumulate.…
arXiv — cs.AI
Исследования1 мин
To Manipulate or Not: Opinion Dynamics in LLM Interactions
arXiv:2610.28875v1 Announce Type: new Abstract: Large language models increasingly take part in extended conversations where they can both shape and be shaped by another party's views, yet most persuasion research measures only one side of that exchange. We introduce a…
arXiv — cs.AI
Исследования1 мин
GRAIL: From Saliency to Semantics in Time-Series Explanation
arXiv:2610.29036v1 Announce Type: new Abstract: Explaining a time-series model requires both locating the evidence and extracting meaningful knowledge from it. Yet a single timestep is not a semantic unit: the meaningful patterns live across many samples, not in any one of them.…
arXiv — cs.AI
Исследования1 мин
Does Every Instance Count Equally? Order Effects in LLM Multi-Instance Processing
arXiv:2610.29057v1 Announce Type: new Abstract: Users increasingly rely on large language models (LLMs) to analyse collections of independent inputs, such as product reviews or social media posts, and return a single aggregated answer. This setting requires multi-instance…
arXiv — cs.AI
Исследования1 мин
AgentPolicyForge: From Executable Environments to Policy Benchmarks and Adaptive Guards
arXiv:2610.28989v1 Announce Type: new Abstract: Customized agent deployments define policies over tools, resources, and execution conditions. Providing these policies in the task agent's prompt does not reliably prevent violations. Training a guard requires examples of tool…
arXiv — cs.AI
Исследования1 мин
Behavior Beyond Text: Evaluating Refusal Transfer Failure in LLM Agents
arXiv:2610.28982v1 Announce Type: new Abstract: While large language models are increasingly deployed as tool-calling agents, their safety evaluations still focus primarily on their ability to refuse harmful requests in text. We investigate whether the refusal behavior in text…
arXiv — cs.AI
Исследования1 мин
Fig: Automated Multiagent Behavior Elicitor
arXiv:2610.28891v1 Announce Type: new Abstract: Many social evaluations for multiagent systems study behaviors as they emerge from environments instead of systematically intervening in the environment to elicit such behaviors. We introduce Fig (Finding Interactions in Games), an…
arXiv — cs.AI
Исследования1 мин
GenXR-Bench: Benchmarking Coding Agents for Interactive XR Development
arXiv:2610.29014v1 Announce Type: new Abstract: Coding agents are rapidly advancing from single-file generation to long-horizon repository-scale engineering, yet the evaluation of their capabilities to author interactive extended reality (XR) applications remains an open…
arXiv — cs.AI
Исследования1 мин
Inheritance as an Admission Test for LLM Prompt Merge Operators
arXiv:2610.29023v1 Announce Type: new Abstract: Large language model (LLM) agents are often improved by iterating until a fixed verifier is done. Once that verifier saturates, the loop has nothing left to climb. We instead score agents against the current population, and a…
arXiv — cs.AI