Исследования1 мин
Nvidia 拥有一个拥有 26 亿参数的开放世界模型。
Nvidia 拥有一个拥有 26 亿参数的开放世界模型。它可以将图像、文本提示和相机运动轨迹转换为可控的虚拟世界。所有这些都在单个 GPU 上运行。- 代码可在 GitHub 上获取。- 研究论文已在 arXiv 上发布。- 适用于……
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Общее1 мин
Вышел дублированный тизер «Искусственного интеллекта»
🎬 Вышел дублированный тизер «Искусственного интеллекта» Фильм расскажет о создателях ChatGPT и гонке вокруг технологии, которая может как изменить мир к лучшему, так и стать серьёзной угрозой. Одни из главных ролей сыграли Юра Борисов и Эндрю Гарфилд — они воплотили Илью…
Telegram — GPT Time | Нейросети | ИИ
Исследования1 мин
OpenAI更新语音——GPT-Live-1.1发布
🎙 OpenAI 已更新语音 - GPT-Live-1.1 已发布。新版本有更多的声音、改进的口音以及更少的接听时的“检查...”等停顿。 GPT-Live-1.1 已经在 ChatGPT、Codex 和dots 上运行。还添加了以下语言的新声音： • 法语； • 德语； • 印地语； •…
Telegram — GPT Time | Нейросети | ИИ
Исследования1 мин
腾讯发布 Hy Memory 2.0：开放模型、框架和 MemHub，为 AI 智能体提供长期记忆
🖥 腾讯发布 Hy Memory 2.0：开放模型、框架和 MemHub，为 AI 智能体提供长期记忆。Hunyuan 在一个版本中同时发布了三项内容：Hy-Memory-30B-A3B 模型、SDK 框架和 MemHub 应用。据腾讯称，这是四大开放解决方案中的第一名……
Telegram — AI Machine Learning Big Data
Общее1 мин
Человек, который зареверсил продукты Adobe, теперь сделал то же самое с Office.
Человек, который зареверсил продукты Adobe, теперь сделал то же самое с Office. Есть аналоги Word, Excel и PowerPoint, и все они бесплатные. У каждого есть собственный MCP-сервер, а аналог Word даже позволяет Claude редактировать ваши черновики с включённым режимом отслеживания…
Telegram — Вайб-кодинг
Общее1 мин
Love is infrastructure
I. Meet cute Barnabas and Mainspring grew up near at hand, about an hour apart with good traffic, but for a long time they weren’t close. They traded glimpses and occasional hellos and then went about their business. Both of them carried on livelier conversations with…
MIT Technology Review
Общее1 мин
TypeSafe AI 添加了 Jev 视觉决策模型和长上下文
✔️ TypeSafe AI 添加了决策模型 Jev 视觉和长上下文 在新版本的 Jev 1.14 中，该模型学会了接受图像作为输入、搜索图像上的对象并处理长文档。所有三个功能都是实验性的，它们的质量仍然太......
Telegram — AI Machine Learning Big Data
Digital Donut AI Digest
Общее1 мин
Epic Games 正在制作一款没有“一行手动代码”的游戏
🎮 Epic Games 正在制作一款无需“一行手动代码”的游戏 据媒体报道，该工作室正在将一款以漫威和星球大战英雄为主角的新射击游戏的开发工作转移到 AI 工具上。据称，该团队的任务是通过 Claude Code 生成代码和图形，而开发人员主要扮演的角色......
Telegram — GPT Time | Нейросети | ИИ
Общее1 мин
Подборка активностей: что отработать сейчас
Подборка активностей: что отработать сейчас 🟠RUN October Jam — до 2 ноября делаем игру, интерактивную историю или видео с ИИ: 100 000 кредитов на разработку и конкурс с фондом $30 000. Готовый контент можно монетизировать: автору 70 % от подходящего дохода, но вывод денег…
Telegram — Paragon | Нейросети и ИИ
Исследования1 мин
Ведущий инженер Anthropic, написал руководство о том, как действительно ставить более амбициозные задачи перед ИИ, и ...
Ведущий инженер Anthropic, написал руководство о том, как действительно ставить более амбициозные задачи перед ИИ, и поделился готовым промптом. Феликс Ризеберг, работающий над Claude в Anthropic, отмечает, что все советуют быть "амбициознее" при использовании ИИ, но никто не…
Telegram — Вайб-кодинг
Общее1 мин
Google 展示了 Gemini 4 Argon 如何控制机器人并为它们构建模拟
📌 Google 展示了 Gemini 4 Argon 如何控制机器人并为它们构建模拟。全新的 Argon 在 ERQA 测试中获得 87% 的分数，该测试考察机器人对物理世界的推理能力，且完全不依赖外部工具，仅凭推理。据 Google 称，这优于任何其他……
Telegram — AI Machine Learning Big Data
Стартапы1 мин
How to build a $10B AI company (Supabase Co-Founder)
What does it take to build like a $10 billion company? >> The thing that was difficult was okay, we felt like we had this great product, but it's very important how you talk about your product and communicate it. And it wasn't until we changed the tagline to the open source…
YouTube — David Ondrej (AI Agents)
Стартапы1 мин
就连 Benchmark 也认为人工智能正在破坏风险投资支持竞争对手的协议
Benchmark 的合作伙伴承认，风险投资不支持竞争对手公司的传统规则正在受到人工智能快速发展的考验。随着公司迅速...，投资组合公司之间的利益冲突变得越来越普遍。
Digital Donut AI Digest
Общее1 мин
Claude Code позволяет экспортировать всю сессию с помощью команды /export file_name.
Claude Code позволяет экспортировать всю сессию с помощью команды /export file_name. Команда создаёт обычный текстовый файл, содержащий всю историю сессии. Его можно использовать в качестве контекста для другого агента, чтобы тот продолжил вашу работу, или проанализировать для…
Telegram — Вайб-кодинг
База знаний1 мин
This Tesla Actualy Flies (NOT AI)
Okay, this is the craziest thing I've seen in a long time. Elon just showed off the new Roadster, which can like literally fly. The first demo they showed was it actually driving up this vertical ramp, actually going completely upside down, and then leaping off of it, flipping…
YouTube — Matt Wolfe
Исследования1 мин
The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock
arXiv:2610.20665v1 Announce Type: new Abstract: Large language model (LLM) agents can solve complex interactive tasks, yet reasoning and tool calls take time. In continuously changing environments, an otherwise correct action may arrive too late. We introduce RealtimeBench, with…
arXiv — cs.AI
Исследования1 мин
When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus
arXiv:2610.20676v1 Announce Type: new Abstract: Correctness feedback suppresses the very evidence that specification-completeness auditing is designed to uncover: programs that satisfy a written contract but violate intended behavior. We demonstrate this objective reversal on 12…
arXiv — cs.AI
Исследования1 мин
Counterfactual Neighborhood Risk Fields: Evidence-Grounded Selective Safety for Vision-Language Models
arXiv:2610.20679v1 Announce Type: new Abstract: Representation-based safety defenses for vision-language models (VLMs) often learn to separate harmful from benign inputs, but separation alone does not establish what a risk score means or when it should be trusted. We introduce…
arXiv — cs.AI
Исследования1 мин
Value-Informed Schedule Trimming for Accelerated Sequential Monte Carlo Guidance in Discrete Diffusion
arXiv:2610.20669v1 Announce Type: new Abstract: Reward-guided discrete diffusion enables test-time alignment with downstream objectives, but applying Sequential Monte Carlo (SMC) guidance at every denoising step is computationally expensive. Active guidance steps require…
arXiv — cs.AI
Исследования1 мин
AlphaDiana: A Measurement System for Agents in Verifiable Reasoning Tasks
arXiv:2610.20675v1 Announce Type: new Abstract: Agents pair reasoning models with harnesses that organize model calls, execute tools, load skill instructions, and retain history in memory. Understanding agent performance requires linking execution conditions to outcomes,…
arXiv — cs.AI
Исследования1 мин
IneqAgent: Agentic Inequality Proving in Lean with Symbolic Computation
arXiv:2610.20681v1 Announce Type: new Abstract: Formal inequality proving requires mathematical reasoning and exact computation, often connected through intermediate lemmas and goal reformulations. We introduce IneqAgent, an agentic framework combining LLM reasoning with…
arXiv — cs.AI
Исследования1 мин
L2G-SR: Learning to Guide LLM-based Symbolic Regression
arXiv:2610.20685v1 Announce Type: new Abstract: Symbolic regression searches for an equation that explains observed data. Numerical error is commonly used to guide the search, but it alone can favor an accurate surrogate over a candidate with the right structure. We propose…
arXiv — cs.AI
Исследования1 мин
When Reasoning Traces Conflict with Evidence: How Answer Configuration Shapes Evidence Following
arXiv:2610.20805v1 Announce Type: new Abstract: A reasoning trace retained at answer time plays two roles: it can raise accuracy, and it can compete with the current evidence when the two disagree. We disentangle these roles by crossing verified evidence versions with the traces…
arXiv — cs.AI
Исследования1 мин
EvolvingBrain: An Adaptive Skill System for Multi-modal Agents Self-Evolution in Open Worlds
arXiv:2610.20786v1 Announce Type: new Abstract: Multi-modal agents have made significant progress in interacting with open worlds. However, their ability to self-evolve remains limited. Supervised fine-tuning enables imitation but not active experience organization, while…
arXiv — cs.AI
Исследования1 мин
ARIA: Runtime Inspection and Amendment for Multi-Agent Systems
arXiv:2610.20780v1 Announce Type: new Abstract: In LLM-based multi-agent systems (MAS), a local mistake can propagate as other agents build on it, turning a correctable error into a failed task. Diagnosing completed runs or repeatedly re-executing them to search for corrections…
arXiv — cs.AI
Исследования1 мин
When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence
arXiv:2610.20764v1 Announce Type: new Abstract: This paper challenges the understanding capabilities of existing AI paradigms and emerging world models by asking a fundamental question: How quickly can reality change before an intelligent system can no longer reliably perceive,…
arXiv — cs.AI
Исследования1 мин
Open-world AI: a Tale of Three Requirements
arXiv:2610.20782v1 Announce Type: new Abstract: The closed world asks AI for specialized competence and lets it manufacture the iid data its theory assumes: a fixed criterion licenses collecting examples until the task is covered. The open world asks for versatility --- taking…
arXiv — cs.AI
Исследования1 мин
A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering
arXiv:2610.20809v1 Announce Type: new Abstract: A self-improving agent has to decide which new skills to keep, and in practice it keeps whatever earns a high reward, convinces a judge model, or compresses its library, even when the skill is wrong. That is fine until the answers…
arXiv — cs.AI
Исследования1 мин
Confidence-Calibrated Inference Expansion for Evaluator-Guided Test-Time Reasoning
arXiv:2610.20836v1 Announce Type: new Abstract: Evaluator-guided reasoning presents a local control problem at every step: trust the current answer, return a fallback, or spend more inference. We formulate this choice as a compute-aware three-action controller, scoring terminal…
arXiv — cs.AI