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Все опубликованные аналитические материалы, исследования суверенного стека, новости экосистемы и обзоры моделей ИИ.

2676
Материалов
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

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

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

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

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

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

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

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

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

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

OpenAI COOKED

Here's everything that happened at OpenAI Dev Day, which I was fortunate enough to attend. There were a lot of announcements, many of which are very cool, including everybody getting one of these, a mod retro that you can literally customize and build your own games, of course,…

YouTube — Matthew Berman
Исследования1 мин

Sam Altman says OpenAI won’t go public until its models are safe

For months, people have wondered when OpenAI will go public. CEO Sam Altman says it won't happen until the company can make better promises about model safety, with no firm timeline in sight. "We intend to continue with AI progress … but as the models have had this surge forward…

The Verge — AI
Исследования1 мин

David's takes on OpenAI DevDay

All right, so we're at the opening night of day. Yeah. What do you think about the announcements, David? >> So, it's kind of mixed, you know, doesn't feel like it's for developers, first of all. A lot of this is just consumer products or like enterprise features and just general…

YouTube — David Ondrej (AI Agents)
Исследования1 мин

Liquid AI выпустила модели, которые принимают решения и не генерируют ни одного токена

⚡️ Liquid AI выпустила модели, которые принимают решения и не генерируют ни одного токена Liquid AI представила decision models, первая из них называется d1. Модель получает контекст в виде текста или JSON и за один вызов возвращает откалиброванные вероятности по заранее…

Telegram — AI Machine Learning Big Data
Исследования1 мин

The Buckmaster Interview (Navier-Stokes) - Numberphile

Tristan Buckmaster speaks about his role in the controversial Navier-Stokes solution. We are seeking an interview with OpenAI too. More links & important information in full description below ↓↓↓ A bit extra from this interview on Here's OpenAI's page about the proof (which also…

YouTube — Computerphile AI