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Казахстан предложил разработать Декларацию об ответственном использовании ИИ
Казахстан предложил разработать Декларацию об ответственном использовании ИИ Mail.KZ
Поиск: ИИ в Казахстане
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Нейросеть вместо поликлиники: 94% казахстанцев выбирают самолечение, рискуя здоровьем
Нейросеть вместо поликлиники: 94% казахстанцев выбирают самолечение, рискуя здоровьем Qaz365.kz
Поиск: Нейросети Казахстан
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Казахстан лидирует в Центральной Азии по использованию цифровых платежей и банковских услуг
📊 Казахстан лидирует в Центральной Азии по использованию цифровых платежей и банковских услуг По данным исследования Global Findex 2025 Всемирного банка, 87% казахстанцев старше 15 лет имеют финансовый счет. Это самый высокий показатель среди четырех стран Центральной Азии,…
Telegram — BlueScreen.kz
Startups1 мин
Alem AI Talent Lab открыл прием заявок для будущих магистрантов
Astana Hub объявил о продолжении приема заявок в Alem AI Talent Lab — программу для будущих магистрантов, которые хотят развиваться в сфере искусственного интеллекта, науки и технологического предпринимательства. Заявки принимаются до 11 октября 2026 года. Участники программы…
The Tech.kz — венчур и стартапы
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OpenAI сделала Auto-review в Codex бесплатным. Если войти через ChatGPT, проверки не расходуют лимиты — независимо от...
OpenAI сделала Auto-review в Codex бесплатным. Если войти через ChatGPT, проверки не расходуют лимиты — независимо от их количества. У многих эта функция включена в настройках, но ещё ни разу ничего не проверила. Если запускать Codex с полным доступом, --yolo или approval_policy…
Telegram — Вайб-кодинг
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Accelerating Floating-Point Satisfiability Solving via Gradient Normalization
Satisfiability Modulo Theories (SMT) solvers are foundational to software verification, program analysis, and compiler testing, particularly over the theory of Quantifier-Free Floating-Point (QF_FP). While recent optimization-based SMT solvers have successfully applied gradient…
arXiv — cs.AI
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Sigma-Hunter: A Domain-Specific Language Model for Threat Hunting and Detection Engineering
Detection engineers must translate threat reports, forensic observations, and hunt hypotheses into precise, testable rules. General-purpose large language models (LLMs) can draft such rules, but often produce invalid YAML, incorrect log sources, unsupported fields, or overly…
arXiv — cs.AI
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Verify Less, Evolve More: Training Idea-Level Critics for Verification-Efficient ML Evolving Agents
As large language models become more powerful, self-evolving agents are able to tackle challenging tasks including AI for machine learning (AI4ML). In AI4ML, while empirical verification is available, it often requires computationally costly model training and evaluation,…
arXiv — cs.AI
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PAIR: Bridging Perception and Action in Vision-Language-Action Models
Vision-language-action (VLA) models map visual observations and language instructions to continuous robot actions. This task requires a transition from representations that describe the scene and instruction to representations that support action generation. Many…
arXiv — cs.AI
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Can AI Agents Make Open-Ended Scientific Discovery? Evidence from Station
Recent AI systems have made rapid progress in scientific discovery when given well-defined metrics, but whether they can autonomously undertake open-ended scientific discovery remains unclear. We investigate AI's ability to tackle open-ended tasks in Station, an open-world…
arXiv — cs.AI
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Adaptive Workflow Intelligence: A Cognitive Architecture for Context-Driven Enterprise Automation
Enterprise systems increasingly rely on automated workflows, yet many AI-driven solutions remain brittle under non-stationary conditions, evolving policies, and delayed operational feedback. While reinforcement learning and large language model (LLM) agents offer partial…
arXiv — cs.AI
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Socio-Foundation: A Model for Generalizable Individual Behavior Simulation via Hierarchical Capability Distillation
Simulating individual behavior requires large language models (LLMs) to preserve persona traits while adapting to dynamic social contexts. However, general-purpose LLMs often flatten distinct personas, while task-specific tuning suffers from fragmentation and generalization. To…
arXiv — cs.AI
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Whose Memory Is It? Scope-Aware Commit Rules for Long-Term LLM Memory
Persistent memory allows an LLM agent to carry experience across conversations, but it also turns a local reasoning mistake into a durable one. During deliberation, an agent may consider a plan, simulate a tool result, report another speaker's belief, and then reject all of…
arXiv — cs.AI
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Enabling Dynamic Computation in Looped LMs
Looped LMs are parameter efficient and promise dynamic computation (saving memory and FLOPs on easy tokens). However, state-of-the-art open Looped LMs trained with this dynamic computation capability (Ouro models) do not realize it in practice as each loop iteration (depth)…
arXiv — cs.AI
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Route-Verify-Vote: Procedure-Conditioned Self-Consistency for Mixed-Domain Reasoning
Compositional generalization remains challenging when language models must combine familiar reasoning operations in unfamiliar ways. The Scenario-Based Commonsense Reasoning Evaluation (SCoRE) 2026 tests this ability on three mixed domains absent from training and requires…
arXiv — cs.AI
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From High Recall to High Utility: Dataset-Adaptive Post-Processing of LLM-Generated Customer Intents
Large language models can extract useful signals from heterogeneous enterprise data, but high-recall extraction often produces outputs that are duplicated, uneven in granularity, semantically overlapping, or too numerous for downstream systems and human reviewers to use…
arXiv — cs.AI
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Not Every Call Needs a Frontier Model: Per-Call-Site Evaluation of Small Language Models in a Deployed Agentic Home-Automation System
An agentic system issues several structurally different kinds of LLM calls. It routes intent, classifies actions, grounds language in a device registry, plans multi-agent pipelines and writes the Python code those pipelines run. The difficulty of these call sites varies by an…
arXiv — cs.AI
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Justice After Identity: Large Language Models and the View from Everywhere
The search for a common view of justice and fairness has challenged human collective activity, as our diverging judgments are unavoidably shaped by the self-interests of social position, personal benefit, cultural inheritance, and historical circumstance. John Rawls famously…
arXiv — cs.AI
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Humanity's Sixth Sense: Benchmarking Intuitive Visual Reasoning in Multimodal Models
Humans perceive far more in a scene than what is explicitly depicted: a single glance captures past causes and future trajectories; a quick peek determines if a vehicle can fit between two parked cars; a few seconds of video reveals who holds authority in a room; and a fleeting…
arXiv — cs.AI
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Humanize: Judgement Engineering for Agentic Coding
Agentic coding makes code generation cheap, but reliable completion remains difficult: the agent that writes the code is a weak judge of whether it is done. We present Humanize, a multi-agent orchestration workflow for agentic coding built around judgement engineering: explicit,…
arXiv — cs.AI
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How Could AI Eliminate Humanity? A Failure-Mode Analysis of Civilizational Risk
This article develops a failure-mode framework for analyzing how advanced artificial intelligence could contribute to human extinction, irreversible civilizational collapse, or permanent human disempowerment. The central thesis is that catastrophic AI risk does not require…
arXiv — cs.AI
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BEACON-SP: Ontology-Grounded GraphRAG Framework for Clinical Suicide Risk Assessment
We present BEACON-SP, an ontology-grounded Graph Retrieval-Augmented Generation (GraphRAG) framework for clinician-facing decision support in behavioral health settings such as suicide prevention, where effective assessment requires integrating heterogeneous clinical,…
arXiv — cs.AI
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Sequential Probabilistic Uncertainty Estimation for Parallel Multi-Agent Reasoning Systems
LLM-based multi-agent systems (MAS) have attracted growing attention for improving reasoning through interaction among multiple agents. In this work, we focus on parallel multi-agent reasoning systems, where several agents solve the same problem over multiple rounds and…
arXiv — cs.AI
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How Fragile Is On-Device Language Model Safety? Localizing Safety-Critical Parameters for Sparse Fault Analysis
As small language models (SLMs) are increasingly deployed on resource-constrained and on-device platforms, including as components of agentic systems, the integrity of locally stored model parameters becomes an important safety concern. We investigate whether safety-sensitive…
arXiv — cs.AI
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An Empirical Study of Agent Skills' Downstream Utility
Agent Skills package procedural guidance and resources for reuse, but a relevant Skill does not necessarily improve task performance. Existing studies characterize Skill content and evaluate downstream performance, yet provide limited explanations of how utility depends on…
arXiv — cs.AI
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Agent Plasticity: Measuring Self-Improvement Through Experience
AI agents increasingly operate in environments where they can diagnose failures and improve through experience, yet existing evaluations largely measure what an agent can do at a fixed point in time rather than how effectively it learns. Evaluating self-improvement requires…
arXiv — cs.AI
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Using AI to mitigate the growing environmental threat of data centers
The global building boom of power-hungry data centers is straining electrical grids, causing greater reliance on energy from polluting fossil fuels. Christina Delimitrou, a newly tenured associate professor at MIT, is fighting this environmental threat by rethinking how the…
MIT News — AI
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AdaGuard: Enhancing Safety and Policy Compliance with Reasoning-Enabled LLM-As-A-Judge Guardrails
Enterprise generative AI applications require robust safety mechanisms that can accommodate diverse risk postures, evolving policies, and varying latency constraints. Current guardrail solutions often suffer from rigidity, relying on fixed policy sets and offering limited…
arXiv — cs.AI
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Shared-Roadmap Generation and Evaluator for Multi-Agent Path Planning Using Heterogeneous Graph Neural Network
Multi-agent path planning (MAPP) in continuous environments often relies on roadmaps to balance safety and search efficiency. However, traditional roadmap generation methods, such as lattice grids or standard sampling-based approaches, frequently face a trade-off between graph…
arXiv — cs.AI
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Learning to Report Unsafe Tasks in a Multi-Agent Game
When agents share a reward for completed tasks, reporting unsafe work can reduce the reporter's reward by stopping a task. Audits can make reporting optimal without ensuring that further training teaches a silent team to report. We study this learning problem in a game where any…
arXiv — cs.AI