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
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All published intelligence, sovereign stack research, ecosystem news, and AI foundation model reviews.
2503
Stories
Исследования1 мин
哈萨克斯坦的小学生通过检查人工智能错误的项目在巴厘岛举行的一项著名比赛中成为最好的学生
来自阿拉木图的两名学童在巴厘岛举行的 IRCYS 2026 比赛中获得银牌。他们的 PlanLens 系统发现人工智能代理计划中的错误。
DigitalBusiness.kz
Исследования1 мин
克劳德现在建议该怎么做：Anthropic 推出了主动式人工智能代理
📎 Claude 现在亲自建议该怎么做：Anthropic 推出了主动式 AI 代理 该公司推出了 Claude Duet 的测试版本，这是一种基于 Claude Opus 5.5 模型的主动式 AI 代理。与常规聊天机器人不同，它不会等待持续的命令，而是独立地......
Telegram — BlueScreen.kz
Исследования1 мин
Anthropic introduced Claude Duet, an autonomous AI agent
Anthropic has introduced a new AI-agent called Claude Duet. It is designed to make everyday work easier for professionals and small business owners: they can schedule meetings, make financial forecasts and develop web applications, reports Tech-news. Claude Duet's main…
Tech-news.kz
Исследования1 мин
Anthropic 推出了 Claude Duet，一款无需永久命令即可工作的个人 AI 代理
⚡️ Anthropic 推出了 Claude Duet，这是一款无需永久命令即可工作的个人人工智能代理。新服务建立在Claude Opus 5.5的基础上，将成为OpenAI和Grok Bot的Dots的竞争对手。 Duet 在云中运行，使用浏览器和连接的应用程序，并执行...
Telegram — GPT Time | Нейросети | ИИ
Kapital.kz
Исследования1 мин
Anthropic 决定发布基于极其强大的 Opus 5.5 的 Claude Duet，来挑战 OpenAI Dots 和 Grok Bot。
Anthropic 决定发布基于极其强大的 Opus 5.5 的 Claude Duet，来挑战 OpenAI Dots 和 Grok Bot。 • 适用于Claude Pro 计划（Dots 需要每月支付100 美元订阅ChatGPT Pro）。 • 运行Opus 5.5，被称为世界上最好的型号。 • 有…
Telegram — Вайб-кодинг
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
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
AutoFraming: Synthesizing Dynamic Frames of C Code with LLMs
arXiv:2610.29046v1 Announce Type: new Abstract: Large language models (LLMs) have shown considerable promise in synthesizing formal specifications from source code. However, existing approaches primarily focus on functional contracts and loop invariants, while largely neglecting…
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
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
D-Skill: Distilling Teacher Knowledge through Agent Skills
arXiv:2610.28914v1 Announce Type: new Abstract: Frontier language-model agents excel at complex tasks, but high inference costs make transferring their capabilities to smaller models essential. Traditional knowledge distillation achieves this through parameter updates, requiring…
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
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
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
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
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
PerMIT: Securing Persistent Memory for LLM Agents via Machine-Checked Integrity Theorems and Scalable Community Trust
arXiv:2610.28987v1 Announce Type: new Abstract: Persistent memory is a largely undefended surface for injection and poisoning, and the same risk limits memory sharing among agents. We present PerMIT, a memory architecture with provable integrity guarantees under an explicit…
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
Sketch2Sim: an end-to-end modular framework for hand-drawn circuit analysis
arXiv:2610.28836v1 Announce Type: new Abstract: This study proposes a sketch-to-simulation system to solve handwritten circuit diagrams. The system converts raw images into a Simulation Program with Integrated Circuit Emphasis (SPICE)-compatible netlist and automatically…
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
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
An Evidence-Driven Multi-Agent Compound AI System for Head and Neck Proton Radiotherapy Planning
arXiv:2610.29010v1 Announce Type: new Abstract: Radiation treatment planning requires balancing competing objectives to satisfy clinical criteria while accommodating physicians' preferences. This study aims to develop an evidence-driven, multi-agent compound AI system that…
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
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
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
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
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
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