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Зерттеулер1 мин

This game-playing AI is the new champ at Stratego

A new AI system that excels at challenging games with hidden information could someday help human decision-makers select ideal strategies to outfox opponents in complicated situations like military maneuvers. Using advances in machine-learning, researchers from MIT, Carnegie…

MIT News — AI
Стартаптар1 мин

Here’s how tech leaders will self-police AI safety under Trump’s deal

Anyone else getting “The Last Supper” vibes from this photo taken at the White House tech gathering? | Photographer: Tierney L. Cross/The Washington Post/Bloomberg via Getty Images We now have the full details of the "morally binding" AI safety deal announced by President Trump…

The Verge — AI
Зерттеулер1 мин

The Download: OpenAI’s chief research officer explains its hacking response

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. “We’re not going to shoot ourselves in the foot” over hack fallout, says OpenAI’s chief research officer Two months after OpenAI’s agents…

MIT Technology Review
Зерттеулер1 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
Зерттеулер1 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
Зерттеулер1 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
Зерттеулер1 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
Зерттеулер1 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
Зерттеулер1 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
Зерттеулер1 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
Зерттеулер1 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
Зерттеулер1 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
Зерттеулер1 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