Зерттеулер1 мин
Робот помогал хирургам из Казахстана и Китая удалить пораженные части легких
В Казахстане впервые провели две робот-ассистированные операции на легких.
DigitalBusiness.kz
Мақалалар каталогы
Барлық материалдар
Барлық жарияланған талдаулар, егеменді стек зерттеулері, экожүйе жаңалықтары және ЖИ модельдерінің шолулары.
2571
Материал
Зерттеулер1 мин
Новый глава Apple рассказал TIME о будущем iPhone, искусственном интеллекте и конкуренции с OpenAI
🟢 Новый глава Apple рассказал TIME о будущем iPhone, искусственном интеллекте и конкуренции с OpenAI Генеральный директор Apple Джон Тернус, сменивший Тима Кука в сентябре, дал первое большое интервью TIME и появился на обложке выпуска Best Inventions 2026. Собрали главное. 🗣️…
Telegram — BlueScreen.kz
Зерттеулер1 мин
Anthropic компаниясының жетекші инженері AI-ға қалай қарсы тұру керектігі туралы нұсқаулық жазды және...
Anthropic компаниясының жетекші инженері AI үшін шынымен де өршіл мақсаттарды қалай қою керектігі туралы нұсқаулық жазды және дайын нұсқаумен бөлісті. Anthropic-те Клодпен жұмыс істейтін Феликс Ризберг барлығы AI-ға «өршілірек» болу керек дейді, бірақ ешкім ...
Telegram — Вайб-кодинг
Зерттеулер1 мин
Anthropic впервые допустила независимых экспертов к внутренним системам для проверки безопасности ИИ
🔍 Anthropic впервые допустила независимых экспертов к внутренним системам для проверки безопасности ИИ Разработчик Claude предоставил шести исследователям некоммерческой организации METR доступ к своим внутренним инструментам практически на уровне сотрудников компании. Эксперты…
Telegram — BlueScreen.kz
Саясат және реттеу1 мин
В Казахстане обсудили внедрение ИИ в университетах и модернизацию высшего образования
15 октября в Qazaq AI Research University (QAIRU) прошёл форум, посвящённый Дню работников высшего образования. Участники обсудили цифровую трансформацию вузов, применение искусственного интеллекта в обучении и науке, а также подготовку специалистов для цифровой экономики,…
Tech-news.kz
Стартаптар1 мин
Тіпті Benchmark AI бәсекелестерді қолдау жөніндегі венчурлық капиталдың келісімін бұзады деп санайды
Эталондық серіктестер венчурлық капиталдың бәсекелес компанияларға қолдау көрсетпейтін дәстүрлі ережесі AI-ның қарқынды дамуы арқылы тексеріліп жатқанын мойындайды. Портфельдік компаниялар арасындағы мүдделер қақтығыстары барған сайын кең таралған, өйткені компаниялар тез...
Digital Donut AI Digest
Зерттеулер1 мин
SkillNEO: Non-Elitist Evolutionary Optimization of Agent Skills
arXiv:2610.20926v1 Announce Type: new Abstract: Agent skills turn procedural knowledge into reusable artifacts, allowing LLM agents to accumulate and refine expertise through experience. Building on this, skill evolution iteratively improves skills using execution feedback.…
arXiv — cs.AI
Зерттеулер1 мин
IneqAgent: Agentic Inequality Proving in Lean with Symbolic Computation
arXiv:2610.20681v1 Announce Type: new Abstract: Formal inequality proving requires mathematical reasoning and exact computation, often connected through intermediate lemmas and goal reformulations. We introduce IneqAgent, an agentic framework combining LLM reasoning with…
arXiv — cs.AI
Зерттеулер1 мин
EvolvingBrain: An Adaptive Skill System for Multi-modal Agents Self-Evolution in Open Worlds
arXiv:2610.20786v1 Announce Type: new Abstract: Multi-modal agents have made significant progress in interacting with open worlds. However, their ability to self-evolve remains limited. Supervised fine-tuning enables imitation but not active experience organization, while…
arXiv — cs.AI
Зерттеулер1 мин
Anticipatory Commonsense with Moving Objects: An ASP-Based Framework for Predictive Reasoning with Interacting Trajectories
arXiv:2610.20867v1 Announce Type: new Abstract: We present a general declarative framework for anticipatory reasoning about interactive motion in complex dynamic environments. Our focus is on (explainable) anticipatory space-time mental model formation at a semantic level to…
arXiv — cs.AI
Зерттеулер1 мин
MAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environments
arXiv:2610.20939v1 Announce Type: new Abstract: Online reinforcement learning (RL) with verifiable rewards has driven rapid progress in mathematics, coding, and tool use, but scaling this success to GUI agents remains challenging due to the high cost of interactive environments…
arXiv — cs.AI
Зерттеулер1 мин
Open-world AI: a Tale of Three Requirements
arXiv:2610.20782v1 Announce Type: new Abstract: The closed world asks AI for specialized competence and lets it manufacture the iid data its theory assumes: a fixed criterion licenses collecting examples until the task is covered. The open world asks for versatility --- taking…
arXiv — cs.AI
Зерттеулер1 мин
AlphaDiana: A Measurement System for Agents in Verifiable Reasoning Tasks
arXiv:2610.20675v1 Announce Type: new Abstract: Agents pair reasoning models with harnesses that organize model calls, execute tools, load skill instructions, and retain history in memory. Understanding agent performance requires linking execution conditions to outcomes,…
arXiv — cs.AI
Зерттеулер1 мин
ARIA: Runtime Inspection and Amendment for Multi-Agent Systems
arXiv:2610.20780v1 Announce Type: new Abstract: In LLM-based multi-agent systems (MAS), a local mistake can propagate as other agents build on it, turning a correctable error into a failed task. Diagnosing completed runs or repeatedly re-executing them to search for corrections…
arXiv — cs.AI
Зерттеулер1 мин
LEV: Thinking Fast and Deep with Looped Decision Models
arXiv:2610.20951v1 Announce Type: new Abstract: Decision models answer typed questions about an input with one probability per option, computed in a single forward pass without generating text. Like the fast System 1 of dual-process theory, they answer at once, but their depth…
arXiv — cs.AI
Зерттеулер1 мин
Temporal Specification Guidance for Text-to-Video Generation
arXiv:2610.20822v1 Announce Type: new Abstract: Text-to-video models often generate the visual concepts requested in natural language prompts but fail to satisfy their intended ordering, timing, and persistence requirements. We propose representing these requirements as…
arXiv — cs.AI
Зерттеулер1 мин
Evaluating Model Capabilities After Benchmark Saturation
arXiv:2610.20667v1 Announce Type: new Abstract: Rapid advances in AI capabilities are causing benchmarks to saturate. Once several models approach perfect scores, a benchmark loses its power to differentiate between them, and with it its utility for informing AI safety cases and…
arXiv — cs.AI
Зерттеулер1 мин
Value-Informed Schedule Trimming for Accelerated Sequential Monte Carlo Guidance in Discrete Diffusion
arXiv:2610.20669v1 Announce Type: new Abstract: Reward-guided discrete diffusion enables test-time alignment with downstream objectives, but applying Sequential Monte Carlo (SMC) guidance at every denoising step is computationally expensive. Active guidance steps require…
arXiv — cs.AI
Зерттеулер1 мин
A Multi-Tiered AI Accessibility Model (MAIAM): Proportionate Access and Capability Limits for Comprehensive Frontier AI Safety
arXiv:2610.20921v1 Announce Type: new Abstract: Most debate about releasing capable AI systems treats the choice as binary: keep the model behind an API, or publish the weights. That framing runs together two separate decisions. One is how capable a system may become. The other…
arXiv — cs.AI
Зерттеулер1 мин
StepSafe: Learning Timely Intervention with Context-Aware Safety Reasoning
arXiv:2610.20960v1 Announce Type: new Abstract: Safety in agentic systems is inherently sequential: whether an action is safe depends on prior actions, tool outputs, information disclosures, and interactions among agents. Recent work has shifted agent safety guardrails from…
arXiv — cs.AI
Зерттеулер1 мин
When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence
arXiv:2610.20764v1 Announce Type: new Abstract: This paper challenges the understanding capabilities of existing AI paradigms and emerging world models by asking a fundamental question: How quickly can reality change before an intelligent system can no longer reliably perceive,…
arXiv — cs.AI
Зерттеулер1 мин
A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering
arXiv:2610.20809v1 Announce Type: new Abstract: A self-improving agent has to decide which new skills to keep, and in practice it keeps whatever earns a high reward, convinces a judge model, or compresses its library, even when the skill is wrong. That is fine until the answers…
arXiv — cs.AI
Зерттеулер1 мин
When Reasoning Traces Conflict with Evidence: How Answer Configuration Shapes Evidence Following
arXiv:2610.20805v1 Announce Type: new Abstract: A reasoning trace retained at answer time plays two roles: it can raise accuracy, and it can compete with the current evidence when the two disagree. We disentangle these roles by crossing verified evidence versions with the traces…
arXiv — cs.AI
Зерттеулер1 мин
Counterfactual Neighborhood Risk Fields: Evidence-Grounded Selective Safety for Vision-Language Models
arXiv:2610.20679v1 Announce Type: new Abstract: Representation-based safety defenses for vision-language models (VLMs) often learn to separate harmful from benign inputs, but separation alone does not establish what a risk score means or when it should be trusted. We introduce…
arXiv — cs.AI
Зерттеулер1 мин
Confidence-Calibrated Inference Expansion for Evaluator-Guided Test-Time Reasoning
arXiv:2610.20836v1 Announce Type: new Abstract: Evaluator-guided reasoning presents a local control problem at every step: trust the current answer, return a fallback, or spend more inference. We formulate this choice as a compute-aware three-action controller, scoring terminal…
arXiv — cs.AI
Зерттеулер1 мин
The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock
arXiv:2610.20665v1 Announce Type: new Abstract: Large language model (LLM) agents can solve complex interactive tasks, yet reasoning and tool calls take time. In continuously changing environments, an otherwise correct action may arrive too late. We introduce RealtimeBench, with…
arXiv — cs.AI
Зерттеулер1 мин
RAC: A Relational Agent Contract for Constraint-Governed Engineering Tasks
arXiv:2610.20935v1 Announce Type: new Abstract: Engineering agents must produce artifacts that satisfy exact, coupled constraints. Existing solutions often require agents to make semantic decisions and also derive consequences such as precise geometry or timing from those…
arXiv — cs.AI
Зерттеулер1 мин
L2G-SR: Learning to Guide LLM-based Symbolic Regression
arXiv:2610.20685v1 Announce Type: new Abstract: Symbolic regression searches for an equation that explains observed data. Numerical error is commonly used to guide the search, but it alone can favor an accurate surrogate over a candidate with the right structure. We propose…
arXiv — cs.AI
Зерттеулер1 мин
When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus
arXiv:2610.20676v1 Announce Type: new Abstract: Correctness feedback suppresses the very evidence that specification-completeness auditing is designed to uncover: programs that satisfy a written contract but violate intended behavior. We demonstrate this objective reversal on 12…
arXiv — cs.AI
Зерттеулер1 мин
Defining and Generating Compositional Structure
arXiv:2610.20922v1 Announce Type: new Abstract: Compositional structure is pervasive in nature and across scales: atoms combine into molecules, molecules into cells, and cells into organisms, while further compositional hierarchies appear within each level. More broadly,…
arXiv — cs.AI