Исследования1 мин
Gambling with our lives’: Anthropic researcher quits, warns against self-improving AI
Anthropic researcher Jacob Coxon resigned over AI extinction fears, calling for pacing agreements between labs.
TechCrunch — AI
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All published intelligence, sovereign stack research, ecosystem news, and AI foundation model reviews.
2224
Stories
Стартапы1 мин
Сегодня в 18:00 прямой эфир на тему: «HackAlem AI изнутри: как готовят крупнейший в мире AI-хакатон с участием Ope...
Сегодня в 18:00 прямой эфир на тему: «HackAlem AI изнутри: как готовят крупнейший в мире AI-хакатон с участием Ope....
Telegram — Astana Hub
Стартапы1 мин
Besxar is building an orbital semiconductor factory, one SpaceX rocket at a time
If you want to manufacture in space—and bring the products back again—there’s a limited set of options: Wait to go to the International Space Station, or partner with a handful of start-ups launching spacecraft that spend time in orbit before they return to Earth. But the…
TechCrunch — AI
Исследования1 мин
GPT-6 Astra: The next generation in intelligence for work
Meet GPT-6 Astra, OpenAI’s most capable model for business, with advanced reasoning, computer use, and stronger writing and design judgment.
OpenAI Blog
Исследования1 мин
Worried Anthropic researchers warn that AI ‘could kill all humans’
A senior Anthropic safety researcher has said there is more than a 10 percent chance artificial intelligence "could kill all humans" by the end of the decade, just hours after a colleague resigned over fears the AI lab and its rivals are carelessly racing to build "superhuman…
The Verge — AI
Исследования1 мин
OpenAI JUST solved math....
So, this has been one of the most insane days in AI possibly ever. OpenAI announces that one of its next generation models that we haven't seen has produced a historic landmark mathematical breakthrough. It's been an open problem for close to a century. Has millions of dollars…
YouTube — Wes Roth
Политика и регулирование1 мин
Beyond Right and Wrong: Evaluating Second-order Social Reasoning in Large Language Models
arXiv:2609.05437v1 Announce Type: new Abstract: Previous AI alignment efforts have focused primarily on first-order social norms -- teaching models what is socially acceptable or unacceptable (e.g., `do not steal'). However, social intelligence depends not only on norm…
arXiv — cs.AI
Исследования1 мин
AutoFyn Technical Report: Non-Parametric Expert Iteration for Long-Horizon Agents
arXiv:2609.05446v1 Announce Type: new Abstract: We introduce AutoFyn, an agent harness inspired by the Expert Iteration algorithm, adapting a frozen model across many rounds by updating persistent state from verified reward signals rather than model weights. Each round begins…
arXiv — cs.AI
Исследования1 мин
The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry
arXiv:2609.05643v1 Announce Type: new Abstract: This Comment emerges from TPC26 (https://tpc26.org), a conference convening leaders from academia, national laboratories, and industry who are reshaping materials science discovery. The meeting explored how AI, autonomous agents,…
arXiv — cs.AI
Исследования1 мин
When and What to Teach: Budget-Aware Online Adaptation for Web Agents
arXiv:2609.05513v1 Announce Type: new Abstract: Web agents have achieved significant success in automating complex internet tasks but deploying them in real-world environments requires continuous online adaptation. Given that deploying powerful proprietary models remains…
arXiv — cs.AI
Исследования1 мин
EnvCraft: Synthesizing Executable Environments in Agentic RL for Claw-like Agent
arXiv:2609.05576v1 Announce Type: new Abstract: The paradigm of LLMs has rapidly shifted from passive language interfaces to autonomous Claw-like agents that execute long-horizon tasks across stateful workspaces. While Agentic Reinforcement Learning (Agentic RL) provides a…
arXiv — cs.AI
Исследования1 мин
PGP-Clinical-TimeKAN: Prior-Guided Joint Probabilistic Forecasting of Clinical Trajectories
arXiv:2609.05488v1 Announce Type: new Abstract: Clinical deterioration unfolds through coupled, partially observed trajectories, not a single diagnostic label. We introduce PGP-Clinical-TimeKAN, a trajectory-first framework for joint probabilistic forecasting of multivariate…
arXiv — cs.AI
Исследования1 мин
Damage-Aware Bandit Pruning for Vision and Language Transformers
arXiv:2609.05448v1 Announce Type: new Abstract: Structured post-training pruning of transformers requires selecting complete functional units whose suppression causes limited degradation. We formulate structured-unit selection for language and vision transformers as a…
arXiv — cs.AI
Исследования1 мин
The Failure Happens Before the Drift: The Social Dynamics of Values in LLM Agent Societies
arXiv:2609.05514v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents are increasingly used as proxies for human participants in social science research, yet it remains unclear whether they can faithfully simulate diverse and conflicting human value systems. We…
arXiv — cs.AI
Исследования1 мин
SciLitBench: Benchmark and Design Principles for LLM-Powered Systematic Literature Reviews
arXiv:2609.05505v1 Announce Type: new Abstract: Systematic reviews require sustained human judgment across thousands of records, yet existing evaluations of large language models (LLMs) typically examine review stages in isolation. We introduce SciLitBench, a multi-stage…
arXiv — cs.AI
Исследования1 мин
Agents Trust Tools Too Much: Measuring Reliance on Unreliable Tools
arXiv:2609.05587v1 Announce Type: new Abstract: Existing evaluations of tool-using agents primarily measure whether an agent can successfully complete diverse tasks with tools. These evaluations generally assume that tools return reliable information. However, tool returns in…
arXiv — cs.AI
Исследования1 мин
EdgeMem: LLM-Free Agent Memory Construction and Retrieval via Evidence-Preserving Multi-Anchor Hypergraph
arXiv:2609.05553v1 Announce Type: new Abstract: Agent memory allows LLM agents to use earlier interactions when answering new queries. Existing methods often compress interaction histories into summaries or other LLM-generated representations. Repeated generation adds cost and…
arXiv — cs.AI
Исследования1 мин
CUSP: Decomposable Collective Uncertainty for Multi-Agent Multimodal Reasoning
arXiv:2609.05708v1 Announce Type: new Abstract: Aggregating heterogeneous vision-language models (VLMs) can improve multimodal reasoning, but neither an individual model's confidence nor that of the aggregated answer measures reliability at the system level. We present CUSP…
arXiv — cs.AI
Исследования1 мин
ARC-Bench: Closed-Loop Replanning Masks Broken Action Ranking in Frozen JEPA World Models
arXiv:2609.05461v1 Announce Type: new Abstract: Reward-free latent world models plan by scoring candidate actions with distances in a frozen latent space: an action is preferred if its predicted future embedding lands closer to the goal embedding. This silently assumes that…
arXiv — cs.AI
Исследования1 мин
What LLM Trading Agents Actually Do in Production: A Six-Month, Population-Scale Record from Two Fleets
arXiv:2609.05663v1 Announce Type: new Abstract: We present a continuous, population-scale measurement record of autonomous language-model trading agents operating in production across two systems with one design lineage: DX Terminal Pro (3,505 user-funded vaults trading real ETH…
arXiv — cs.AI
Исследования1 мин
RAPID: Reliability-Aware Pair Importance Distillation
arXiv:2609.05481v1 Announce Type: new Abstract: Inter example relational distillation transfers a teacher's representation geometry by matching relations among examples within a mini batch. Computing all pairs has quadratic complexity in the batch size, whereas uniform…
arXiv — cs.AI
Стартапы1 мин
Recovering Temporal and Geographic Signals from Language Model Embeddings
arXiv:2609.05721v1 Announce Type: new Abstract: Understanding whether language-model embeddings encode structured real-world information is important for both representation analysis and information retrieval. We study this question for temporal and geographic signals using a…
arXiv — cs.AI
Исследования1 мин
SCAFFOLD: Self-Improving Web Agents via Recursive Parametric Skill Abstraction
arXiv:2609.05511v1 Announce Type: new Abstract: Web agents need to navigate visually rich, long-horizon interfaces that change across sites, yet most previous agents still learn each task in isolation and discard the procedural knowledge they accumulate. Recent skill-augmented…
arXiv — cs.AI
Исследования1 мин
Beyond Prompts: Measuring and Optimizing LLM Tool-Agent Harnesses
arXiv:2609.05736v1 Announce Type: new Abstract: LLM tool agents can be improved without retraining by modifying the runtime harness around a fixed model: prompts, tool interfaces, middleware, state handling, and recovery logic. We study this setting as resource-bounded harness…
arXiv — cs.AI
Исследования1 мин
Deep belief networks are exact
arXiv:2609.05572v1 Announce Type: new Abstract: We prove that every strictly positive probability distribution on \(\{-1,1\}^n\) is represented exactly by a sigmoid belief network with finite parameters. This answers a question of Sutskever and Hinton. The proof upgrades their…
arXiv — cs.AI
Исследования1 мин
Compiling VGDL into Causal Models
arXiv:2609.05459v1 Announce Type: new Abstract: Reinforcement learning and large language models often struggle to accurately capture the causal mechanics of game environments. Standard reinforcement learning agents tend to rely on spurious correlations, while large language…
arXiv — cs.AI
Исследования1 мин
When Does Memory Help? A Cost-Aware Evaluation of Long-Term Memory in Tool-Using LLM Agents
arXiv:2609.05441v1 Announce Type: new Abstract: Long-term memory for LLM agents is evaluated today by conversational recall benchmarks (LoCoMo, LongMemEval), which measure question answering over dialogue history, not whether remembered facts change what a tool-using agent does.…
arXiv — cs.AI
Исследования1 мин
CriticGen: Generation-Aware Evaluation as Actionable Feedback
arXiv:2609.05439v1 Announce Type: new Abstract: Current evaluation methods for large language models are coarse-grained and decoupled from generation, producing generic explanations that fail to provide actionable feedback for model improvement. We propose CriticGen, a…
arXiv — cs.AI
Исследования1 мин
Planning and Scheduling Business Processes under Control-Flow Uncertainty
arXiv:2609.05578v1 Announce Type: new Abstract: Scheduling activities in business processes can improve efficiency (e.g., reduce makespan), but is challenging because the exact sequence of activities required to complete a case is often uncertain due to decisions based on data…
arXiv — cs.AI
Исследования1 мин
Reasoning-Aware Compression: Identifying and Protecting Vulnerable Reasoning Circuits for Energy-Efficient LLM Deployment
arXiv:2609.05512v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) impose substantial energy costs during deployment, yet current compression methods apply uniform quantization across all components, risking damage to critical reasoning circuits. We present a…
arXiv — cs.AI