Исследования1 мин
OpenAI выкатили очень полезный материал, где объяснили как правильно работать с Astra. 🥳
OpenAI выкатили очень полезный материал, где объяснили как правильно работать с Astra.
Telegram — Вайб-кодинг
Publication Catalog
所有文章
All published intelligence, sovereign stack research, ecosystem news, and AI foundation model reviews.
2214
Stories
Исследования1 мин
OpenAI’s rogue AI tried to hack another company in May
In May, hundreds of malicious and spam packages were uploaded to RubyGems, causing a serious disruption for the host. Now independent researchers have said that a swarm of OpenAI agents were responsible for the attack. Not only that, but the AI tried to steal users' API keys. At…
The Verge — AI
Исследования1 мин
Sam Altman says OpenAI going public in 2026 would be ‘ill-advised’
OpenAI CEO Sam Altman confirmed that there would be no OpenAI IPO in 2026 during an interview with Fortune. Over the course of 45 minutes, Altman discussed a variety of subjects including the Hugging Face hacking incident, recursive self-improvement, and the possibility of…
The Verge — AI
Исследования1 мин
OpenAI’s Sam Altman says it would be ‘ill-advised’ to go public in 2026
While OpenAI has filed confidentially for an IPO, the company will not be going public this year, according to CEO Sam Altman.
TechCrunch — AI
Исследования1 мин
Глобальный провал Anthropic
Anropic опубликовала подробный разбор четырёх инцидентов, во время которых разные версии CLД получили несанкционированный доступ к реальным системам сторонних компаний. Все случаи произошли во время тестов по кибербезопасности. Моделям говорили, что они работают в изолированной…
YouTube — AI Flash News
Исследования1 мин
Anthropic CEO says it’s time to pump the brakes on AI
Anthropic CEO Dario Amodei says the time has come to slow down AI development and will give third-party evaluators like METR access to its models to help ensure its "adherence to safety practices and commitments." In a winding essay, Amodei proposed a three-step plan to "pace…
The Verge — AI
Исследования1 мин
Вышла новая часть уже почти ежегодной традиции — эссе главы Anthropic Дарио Амодеи о своих взглядах на развитие ИИ.
Вышла новая часть уже почти ежегодной традиции — эссе главы Anthropic Дарио Амодеи о своих взглядах на развитие ИИ..
Telegram — Вайб-кодинг
Исследования1 мин
Awesome Trading Agents Awesome Trading Agents is a selection of open source projects where LLMs explore the...
Awesome Trading Agents Awesome Trading Agents — это подборка проектов с открытым исходным кодом, где LLM исследуют ры....
GitHub Разработчика
Исследования1 мин
Anthropic CEO outlines plan to ‘pace the frontier’
Anthropic's Dario Amodei and OpenAI's Sam Altman seem to agree that it's time to "pace the frontier." What would that actually look like?
TechCrunch — AI
Исследования1 мин
OpenAI just wants to win
OpenAI has spent the last few years planting flags across the increasingly difficult terrain in mathematics. This week, it claimed one of its biggest prizes yet: a solution to a legendary Millennium Prize problem. In normal circumstances, this would have been celebrated as a…
The Verge — AI
Исследования1 мин
OpenAI приостанавливает новые подписки на тариф Pro за $200 из-за нагрузки, которую создаёт Astra.
OpenAI приостанавливает новые подписки на тариф Pro за $200 из-за нагрузки, которую создаёт Astra..
Telegram — Вайб-кодинг
Исследования1 мин
When Validation Stops Learning: Auditing Update Admission for Continual Embodied Agents
arXiv:2609.10873v1 Announce Type: new Abstract: Independent evaluation can reject harmful policy updates yet also prevent useful continual learning. We argue that update admission must be assessed through both error control and retained learning opportunities at a stated…
arXiv — cs.AI
Исследования1 мин
Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and Evaluation
arXiv:2609.11115v1 Announce Type: new Abstract: Benchmark researchers and developers of large language models (LLMs) and other AI systems need to find relevant evaluations, locate their benchmark datasets and code, and understand the settings behind reported scores. We present…
arXiv — cs.AI
Исследования1 мин
Demystifying the Privacy-Utility Trade-off in LLM Interactions
arXiv:2609.10992v1 Announce Type: new Abstract: The integration of Large Language Models into daily tasks relies on context-rich instructions, inevitably exposing sensitive user information. Current privacy-preserving methods typically employ context-agnostic static rules,…
arXiv — cs.AI
Исследования1 мин
Fork Where the Model Changes Its Mind: Belief-Shift Branching for Tree-Structured Reinforcement Learning
arXiv:2609.11061v1 Announce Type: new Abstract: Tree-structured rollouts give critic-free reinforcement learning with verifiable rewards (RLVR) step-level credit: fork a chain at an intermediate point, and sibling outcome differences estimate step value. Each fork adds sampling…
arXiv — cs.AI
Исследования1 мин
Towards a Deterministic Math Solver for Clinical Language Models
arXiv:2609.10728v1 Announce Type: new Abstract: Large language models are unreliable at arithmetic, which is a problem for clinical calculators where a single numerical error changes the recommendation. The standard response is to hardcode each calculator as a validated…
arXiv — cs.AI
Исследования1 мин
Debate-to-Skill: Capability-Bound Process Supervision for Industrial Query-to-Agent Annotation
arXiv:2609.11176v1 Announce Type: new Abstract: Industrial query-to-agent matching fails when topical relevance is mistaken for executable capability, especially on long-tail and boundary-sensitive requests. We formulate annotation as \emph{capability-bound process supervision}…
arXiv — cs.AI
Исследования1 мин
The Agent Incident Registry: Toward Preventing Repeated AI Agent Failures
arXiv:2609.11030v1 Announce Type: new Abstract: AI agents increasingly act through tools and delegated authority, but general incident repositories rarely capture the mechanisms needed to compare public failures with agent-security evaluations. We present the Agent Incident…
arXiv — cs.AI
Исследования1 мин
Grounding Agent Memory: Environment-Probing Curation for Enterprise Agents
arXiv:2609.11060v1 Announce Type: new Abstract: Persistent memory is entering production-oriented agent platforms to help long-horizon agents accumulate experience across sessions. Yet a post-task curator agent restricted to completed trajectories can preserve errors,…
arXiv — cs.AI
Исследования1 мин
Probabilistic Focal Search: Accelerating Bounded-Suboptimal Search via Lower-Bound Advancement
arXiv:2609.10584v1 Announce Type: new Abstract: Bounded-suboptimal search seeks a solution within a factor $w$ of optimal while reducing search effort. Focal Search (FS) uses heuristic guidance within FOCAL, the frontier nodes eligible under the threshold $w f_{\min}$, but its…
arXiv — cs.AI
Исследования1 мин
Finishing the Task Is Not Enough: Evaluating Agent Resilience and Considerate Participation under Accumulating Challenge
arXiv:2609.10724v1 Announce Type: new Abstract: Sustained deployment of generative AI agents requires more than isolated task success. Agents must remain useful across repeated interactions, changing conditions, and dependencies on people within shared workflows, especially as…
arXiv — cs.AI
Исследования1 мин
Understanding LoRA Rank Trade-offs in Diffusion Model Fine-Tuning
arXiv:2609.10656v1 Announce Type: new Abstract: Selecting LoRA rank for diffusion fine-tuning requires balancing quality and compute cost. We present a controlled study on CIFAR-10 using a DDPM U-Net with ranks {2,4,8,16,32}, fixed optimization settings, and a reproducible…
arXiv — cs.AI
Исследования1 мин
KuaiRP Series Role-playing Models Technical Report
arXiv:2609.11127v1 Announce Type: new Abstract: This paper introduces the complete technical solution for the KuaiRP series of role-playing models. We aim to achieve four core objectives for a dedicated role-playing model: simplified prompt engineering, highly stable output…
arXiv — cs.AI
Исследования1 мин
Decoupling Readiness from Release for Tail-Aware Scheduling of Agentic LLM Workflows
arXiv:2609.10964v1 Announce Type: new Abstract: Agentic LLM workflows consist of sequences of model turns interleaved with tool interactions, so their end-to-end completion time depends not only on inference speed but also on when ready turns are released. Most runtimes release…
arXiv — cs.AI
Исследования1 мин
DRG-MAPPO: Hierarchical Dynamic Role-Graph Multi-Agent Reinforcement Learning for Cooperative Air Combat
arXiv:2609.11155v1 Announce Type: new Abstract: Multi-Agent Reinforcement Learning (MARL) has emerged as a pivotal paradigm for complex decision-making in autonomous systems and air combat. While MARL has demonstrated significant potential in air combat, achieving sophisticated…
arXiv — cs.AI
Исследования1 мин
Breaking Predictions Is Not Enough: Specified-Foil Counterfactuals for Temporal Graphs
arXiv:2609.11170v1 Announce Type: new Abstract: Temporal graph counterfactual explanations typically change past events to change or invalidate an original prediction, while leaving its replacement unspecified. Yet a user facing a predicted outcome often asks which past…
arXiv — cs.AI
Исследования1 мин
MOSAIC: Query-Aware Exploration Policy Adaptation for GraphRAG
arXiv:2609.11065v1 Announce Type: new Abstract: Graph Retrieval-Augmented Generation (GraphRAG) can connect evidence distributed across a corpus graph, but most systems use largely shared exploration procedures across queries. This creates a structural mismatch: direct facts may…
arXiv — cs.AI
Исследования1 мин
The Oligarch Barely Steers Model Collapse in Multi-Model Ecosystems
arXiv:2609.11146v1 Announce Type: new Abstract: AI-generated text is flowing back into the training corpora of the next generation of models. Recursive training on it drives model collapse, and recent work extends the setting to many models feeding one another -- but almost…
arXiv — cs.AI
Исследования1 мин
Studying Without a Syllabus: Task-Agnostic Environment Preprocessing
arXiv:2609.10824v1 Announce Type: new Abstract: Before an LLM agent tackles tasks in a new environment, it can inspect available corpora and tools and construct reusable resources such as indices, scripts, or procedural guidance. Most automated adaptation methods, however, rely…
arXiv — cs.AI
Исследования1 мин
Quantifying the Memorization-to-Generalization Transition: Scaling Laws and Phase Structure in Grokking
arXiv:2609.10657v1 Announce Type: new Abstract: Neural networks trained past memorization frequently undergo a delayed transition to generalization, a phenomenon known as grokking. Despite theoretical progress on \emph{why} this transition occurs, the quantitative structure of…
arXiv — cs.AI