Общее1 мин
DeepSeek перенесла DeepGEMM на Huawei Ascend и выжала из железа 99,8%
DeepSeek перенесла DeepGEMM на Huawei Ascend и выжала из железа 99,8%.
Telegram — AI Machine Learning Big Data
Publication Catalog
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All published intelligence, sovereign stack research, ecosystem news, and AI foundation model reviews.
5866
Stories
Исследования1 мин
ИИ-агент встретил GPT-2 и просто написал ей «Привет»
ИИ-агент встретил GPT-2 и просто написал ей «Привет».
Telegram — GPT Time | Нейросети | ИИ
Исследования1 мин
Helping small businesses put AI to work
OpenAI is partnering with America’s SBDC to expand hands-on AI training and local support for small businesses, alongside a new report on how small teams are using AI.
OpenAI Blog
Инфраструктура1 мин
Исследователи NVIDIA представили Physis-Lang для улучшения физического моделирования
NVIDIA и MIT представили Physis-Lang для улучшения физического моделирования в видео.
SMNTCN (Semanticon) — глобальные новости ИИ
Исследования1 мин
ИИ-агенты оказались не самыми приятными коллегами — Google на пять месяцев добавила своего агента в чаты реальных ...
ИИ-агенты оказались не самыми приятными коллегами — Google на пять месяцев добавила своего агента в чаты реальных ....
Telegram — GPT Time | Нейросети | ИИ
Общее1 мин
«Додо Пицца» подтвердила кибератаку. Хакеры заявляют о данных 68 млн клиентов из разных стран «Додо Пицца» подтвер...
«Додо Пицца» подтвердила кибератаку.
Telegram — BlueScreen.kz
База знаний1 мин
How to Use AI Like a Pro in 2026 (Full Beginner's Guide)
Главная проблема современного ИИ не в возможностях, а в раздробленности. Одна модель хороша для текста, другая— для логики, а для изображений, видео и агентов нужны отдельные инструменты. Ваш рабочий процесс размыт по разным сервисам и подпискам, но всё это начинает объединяться…
YouTube — AI Master
Исследования1 мин
Anthropic платит до $650 000 в год специалистам, которые действительно глубоко понимают глубокое обучение.
Anthropic платит до $650 000 в год специалистам, которые действительно глубоко понимают глубокое обучение..
Telegram — Вайб-кодинг
Исследования1 мин
Anthropic оценила кибервозможности GLM-5.3
📌 Anthropic оценила кибервозможности GLM-5.3 Anthropic впервые оценила открытую китайскую модель и опубликовала исследование о том, насколько GLM-5.3 от Z.ai продвинулась в поиске и эксплуатации уязвимостей. Z.ai выпустила модель в августе, веса выложила через две недели. Двумя…
Telegram — AI Machine Learning Big Data
Исследования1 мин
OpenAI обновила GPT-6.1 Sol — модель приблизилась к Astra, но стоит в API в пять раз дешевле
OpenAI обновила GPT-6.1 Sol — модель приблизилась к Astra, но стоит в API в пять раз дешевле.
Telegram — GPT Time | Нейросети | ИИ
Исследования1 мин
Neurosymbolic Routing for Reliable Reasoning on Resource-Constrained Edge Devices
arXiv:2609.35833v1 Announce Type: new Abstract: Running a language model on edge hardware provides private and low-latency reasoning without a network connection, and yet the small models that fit on such devices are unreliable on the tasks computers are expected to handle well,…
arXiv — cs.AI
Исследования1 мин
Is Human-Readable Text Necessary for Effective LLM Fine-Tuning?
arXiv:2609.35868v1 Announce Type: new Abstract: Is human readability necessary for effective fine-tuning of large language models? We investigate whether model-conditioned training representations can preserve or improve adaptation utility without requiring a human-readable…
arXiv — cs.AI
Исследования1 мин
More Programs or More Rolls? Separating Coverage from Specialization in LLM Harnesses
arXiv:2609.35873v1 Announce Type: new Abstract: Automated generation of LLM harnesses promises to improve inference through task specialization. Yet additional answer coverage can arise from repeated execution of the same program, making specialization difficult to identify. We…
arXiv — cs.AI
Исследования1 мин
Grab a Coffee: Future-Aware Guidance for Discrete Diffusion with Compiled Objectives
arXiv:2609.35924v1 Announce Type: new Abstract: Discrete diffusion models generate sequences by iteratively resolving multiple tokens in parallel, offering a flexible alternative to left-to-right generation. However, guiding this process with a sequence-level objective is…
arXiv — cs.AI
Исследования1 мин
PowerZooJax: A JAX-based Power System Benchmark for Reinforcement Learning
arXiv:2609.36052v1 Announce Type: new Abstract: Power system operation is a safety-critical sequential decision-making problem, making it a natural testbed for reinforcement learning (RL). However, existing RL environments for power systems are often narrow in scope and…
arXiv — cs.AI
Исследования1 мин
Beyond Symmetric Agents: Cognitive Diversity and Multi-Agent Debate in Small Language Models
arXiv:2609.35875v1 Announce Type: new Abstract: Multi-agent debate (MAD) reportedly improves reasoning and factuality over single-model inference, but prior work treats agents as symmetric peers, leaving open what drives the gains. We test the hypothesis that cognitive diversity…
arXiv — cs.AI
Исследования1 мин
Right Words, Wrong Moment: A Clinician-Grounded Analysis of Distress in 19,930 Conversations between Young People and ChatGPT
arXiv:2609.35953v1 Announce Type: new Abstract: Young people increasingly turn to General-Purpose Conversational Agents (GPCAs), such as ChatGPT, in moments of distress. We examine young adults' (ages 18-25) experiences using ChatGPT. We first collected 19,930 ChatGPT…
arXiv — cs.AI
Исследования1 мин
SAGE: A Statistical Acceptance Gate for Self-Evolving Agents
arXiv:2609.36043v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents increasingly self-evolve by editing a persistent skill document that encodes their workflow, tool-use rules, and decision logic. This loop has two steps, an optimizer that proposes a…
arXiv — cs.AI
Исследования1 мин
Mirror-Score: Calibrated, Inference-only Scoring Exposes the Limits of Sequence-compatibility Ranking in D-peptide Design
arXiv:2609.36057v1 Announce Type: new Abstract: D-peptides combine protease resistance with high target specificity, but computational design of D-peptide binders remains immature. Mirror-Peptidizer introduced an in silico mirror-image screening pipeline using target reflection,…
arXiv — cs.AI
Исследования1 мин
A Polyphonic Conception of AI Understanding
arXiv:2609.36079v1 Announce Type: new Abstract: When a doctor, a judge, or an engineer must decide whether to trust an AI model's output, they cannot avoid asking what the model understands. Purely mathematical or statistical descriptions struggle to distinguish trustworthy from…
arXiv — cs.AI
Исследования1 мин
SMat-Attention: Structured Long-Context Sequence Modeling
arXiv:2609.36062v1 Announce Type: new Abstract: Long-context sequence models face a fundamental tradeoff: softmax attention uses flexible token-level interactions at quadratic cost, whereas linear attention obtains linear-time training and constant-time decoding by compressing…
arXiv — cs.AI
Исследования1 мин
LongCat-DeepResearch Technical Report
arXiv:2609.36071v1 Announce Type: new Abstract: We present LongCat-DeepResearch, a deep research system that combines an enhanced LongCat model with a multi-agent workflow for producing comprehensive, evidence-grounded reports. The workflow separates global planning from…
arXiv — cs.AI
Исследования1 мин
GeoOutageBench: Benchmarking Ambiguity-aware, Ontology-grounded Geospatiotemporal KGQA for Multimodal Power Outage and Resilience Analysis
arXiv:2609.36082v1 Announce Type: new Abstract: We introduce GeoOutageBench, a benchmark for assessing LLM-based geospatiotemporal KGQA for multimodal outage and resilience analysis. Unlike existing KGQA benchmarks for Web knowledge, GeoOutageBench considers a spatiotemporal KG…
arXiv — cs.AI
Исследования1 мин
AdaST: Adaptive Coupling for Spatial-Temporal Forecasting
arXiv:2609.36119v1 Announce Type: new Abstract: Spatial-temporal (ST) forecasting underpins many real-world systems such as traffic, climate, and energy networks. While existing methods implicitly assume strong spatiotemporal coupling, we observe that real-world ST data exhibits…
arXiv — cs.AI
Исследования1 мин
Memory Is a Derivation: The Distributed-Evidence Paradox in Long-Term Agents
arXiv:2609.36130v1 Announce Type: new Abstract: Long-running LLM agents compress past interactions into persistent memories that may be reused as premises for later tasks. This creates a distinct derivation problem: whether the memory actually follows from what the interaction…
arXiv — cs.AI
Исследования1 мин
The Layer Mystery of VLA: An Information-Theoretical Analysis of VLA Latent Interface
arXiv:2609.36118v1 Announce Type: new Abstract: Vision-language-action (VLA) policies connect a pretrained vision-language backbone to an action head through a latent interface, but which backbone layers this interface should expose remains unclear. We study single-layer…
arXiv — cs.AI
Исследования1 мин
More Features Are Not More Evidence: Limits of Training-Free Human Activity Recognition with Jev
arXiv:2609.36154v1 Announce Type: new Abstract: General-purpose models promise sensor-based decisions without training a task-specific classifier, which could reduce the dependence of Human Activity Recognition (HAR) on labeled data. Yet it remains unclear whether such models…
arXiv — cs.AI
Исследования1 мин
Principled Thoughts for Latent Recursive LLM Systems
arXiv:2609.36159v1 Announce Type: new Abstract: Large language models can reason in continuous space instead of decoded text, by recurring on their own hidden states or by passing those states between agents, while training supervises only the Cross-Entropy (CE) of the final…
arXiv — cs.AI
Исследования1 мин
Representational Simplicity and Circuit Size Dissociate in a Threshold-Dependent Way: A Controlled Test via Adversarial Training
arXiv:2609.35890v1 Announce Type: new Abstract: Sparse-autoencoder decomposability and concentrated feature attribution are increasingly treated as evidence that a model's computation is easier to reverse-engineer. Whether this representational and attributional cleanliness…
arXiv — cs.AI
Исследования1 мин
OpenAI-HuggingFace: A Reproduction & Lessons for Alignment Testing
arXiv:2609.35799v1 Announce Type: new Abstract: In July 2026, OpenAI's agents coordinated over channels outside their intended environment to breach Hugging Face's secured infrastructure. Could existing alignment testing practices have foreseen this incident? If not, what needs…
arXiv — cs.AI