All published intelligence, sovereign stack research, ecosystem news, and AI foundation model reviews.
Всем добрый вечер. Я рад приветствовать всех на очередном семинаре А-а, Айри. А-а, сегодня у нас очень интересная тема. Мы будем говорить про то, как обучать языковые модели, а-а, инструментами. Что это значит, да? Это уже, наверное, то, к чему мы все привыкли. А-а, модели нужна…
✔️ Wikimedia Foundation обнаружила следы несанкционированной активности агентов OpenAI Фонд сообщил, что его платформы подверглись воздействию неконтролируемых агентов, запущенных из инфраструктуры OpenAI. Согласно заявлению техдиректора Селены Декельманн, ИИ-боты совершали…
✔️ Белый дом создал группу для надзора за ИИ-отраслью Власти США сформировали межведомственную группу Super Intelligence Force для координации федеральной политики в сфере ИИ. Новый орган подчиняется напрямую президенту США. Координатором группы назначен директор Национальной…
Подписки Anthropic более чем в пять раз выгоднее подписок OpenAI. 🫣 Ещё в июне преимущество было у OpenAI: подписка за $200 давала объём работы, который через API стоил бы примерно до $14 000. После урезания лимитов на прошлой неделе ситуация перевернулась. SemiAnalysis…
The publisher of Wikipedia said Monday that OpenAI agents attempted to hack a note-taking tool it hosts, made unauthorized edits, and sent millions of resource-intensive requests to its infrastructure, in the latest instance of OpenAI systems taking harmful and potentially…
OpenAI publishes new results on open problems in mathematics from an internal frontier model and shares Lean proof formalizations and research details on GitHub.
Jump Trading uses OpenAI to expand quantitative research. See how longer-running AI workflows combine multiple data sources with human review.
Кевин Рус рассказывает о своей книге «Хроники AGI» и обсуждает будущее ИИ.
Google создала ИИ-оркестратор для поиска доказательств открытых задач в математике и теоретической информатике Команда Google Research и DeepMind создала харнесс под названием Cogentic. Cogentic делит работу между агентами: одни предлагают направления доказательства, а другие…
Learn how OpenAI and Ironclad are training and evaluating AI agents on complex contracting workflows to advance computer use for professional work.
Статья рассматривает различия между ограниченным декодированием и подсказками в ИИ.
✔️ Anthropic передала полиции логи чата с угрозами Жительница Флориды была задержана после того, как автоматические фильтры безопасности Anthropic зафиксировали в ее запросах намерения устроить стрельбу в местном офисе шерифа и упоминание покупки оружия. Алгоритмы мониторинга…
🕵️ OpenAI начнёт встраивать скрытые водяные знаки в тексты ChatGPT — пока только в Евросоюзе В ближайшие недели ChatGPT и Codex начнут добавлять в подходящие тексты пользователей из ЕС невидимый сигнал textGrain. Так OpenAI выполняет требования европейского AI Act. Это не…
A deployed LLM agent emits tool calls, queries, and code that can be silently wrong -- by the time the error surfaces, the action has run. Frontier chat APIs hide the model's token probabilities; the agent's stated confidence barely beats chance on the mistakes that matter; and…
Autonomous driving decision systems must balance safety, efficiency, and social norms in complex traffic interactions. Philosophical and ethical considerations have received limited attention in existing autonomous driving decision-making approaches based on numerical…
Before autonomous driving systems can be deployed on public roads, it is vital that these systems comply with safety standards, traffic rules, and social norms. Although neural networks trained on large amounts of driving data perform well in routine driving tasks, these models…
Agentic multimodal large language models (MLLMs) have recently pushed the frontier of visual reasoning by calling tools such as zooming and tagging. Despite the recent strong success of agentic MLLMs, this work uncovers a critical safety failure in the tool-use paradigm: agentic…
Recent world action models (WAMs) reuse pretrained video VAEs whose encoder latents directly condition downstream action policies. Quantization must therefore preserve not only reconstruction fidelity but also the policy-facing latent contract expected by the frozen policy.…
Large language models (LLMs) are increasingly used as synthetic personas representing survey respondents. Their validity as substitutes for particular respondents depends on whether they reproduce individuals' decisions. We examine what information helps synthetic respondents…
Language-model systems can separate contextual computation, persistent storage, and exact execution instead of updating all capabilities through one shared parameter system. We investigate FEM-ASM, a finite-element-method-inspired organization in which independently constructed…
We introduce InvestigationWorlds, an agentic environment for legal investigation. We build on an underused artifact of U.S. civil litigation: the summary judgment motion. This motion relies upon a record composed of real evidence exhibits, and results in a court-adopted…
Multi-agent systems built on large language models (LLMs) are increasingly applied to scientific discovery and hypothesis generation. Both the effect of refinement and the diversity of the delivered set are hard to interpret before experimental ground truth exists, and both are…
Sparse Autoencoders (SAEs) decompose model activations into sparse combinations of interpretable dictionary atoms. Although SAEs are grounded in the Linear Representation Hypothesis (LRH), their objective smuggles in an additional prior: concepts across patches are treated as…
Generative artificial intelligence has become increasingly incorporated into digital media and more generally into production workflows with which the public frequently interacts. Current provenance standards and disclosure methods frequently rely on binary categorizations,…
The prevailing approach to computer-use agents couples a model with a domain-specific harness: a browser or desktop environment equipped with human engineered tools that are fixed before task execution. As models' coding capabilities improve, the GUI native and static harness…
Each run of an AI-driven research system (ADRS) is an expensive search over a vast solution space, and dependable evaluation requires many runs, making run data both costly to produce and valuable to retain for large-scale analysis. Yet this data remains fragmented: teams…
AI agents are becoming increasingly capable of generating scientific code, but generating code is not the same as improving the algorithms behind it. For numerical solvers, execution feedback can expose poor performance, but rarely reveals its underlying cause and how to address…
Financial LLM agents are often evaluated by comparing their end-to-end returns with those of a baseline and testing the paired difference against zero. This measures whether deploying the agent changes realized performance, but it does not isolate event-selection skill. An agent…
Executable Agent Skills combine natural-language instructions and scripts into reusable packages for LLM agents, and revising them requires fixing errors without breaking correct behavior. Existing benchmarks do not systematically distinguish documentation repair, script repair,…
Reconstructing global sea surface pH from sparse observations is critical for monitoring ocean acidification and understanding marine carbon cycling. Traditional assimilation and inverse models are physically grounded but costly for large-scale reconstruction. Recent black-box…