Стартапы1 мин
Казахстан и Германия договорились о проектах в сфере ИИ
В ходе официального визита Президента РК в Германию делегация МИИЦР провела встречи с представителями немецких компаний и организаций.
Profit.kz — ИТ в Казахстане
Publication Catalog
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All published intelligence, sovereign stack research, ecosystem news, and AI foundation model reviews.
2638
Stories
Стартапы1 мин
Мировые лидеры AI и технологий собрались в Астане на AI & Digital Bridge 2026
Мировые лидеры AI и технологий собрались в Астане на AI & Digital Bridge 2026 UzDaily.uz
Поиск: Astana Hub AI
Исследования1 мин
Google представила Gemini 4 Argon — новая модель обошла GPT-6 Astra и Claude Opus 5.5 в одном из главных тестов дл...
🚀 Google представила Gemini 4 Argon — новая модель обошла GPT-6 Astra и Claude Opus 5.5 в одном из главных тестов для программистов На DeepSWE v1.1 Argon набрала 77,9% против 74,2% у Opus 5.5 и 74,1% у GPT-6 Astra. По данным самой Google, модель лидирует в 13 из 19…
Telegram — GPT Time | Нейросети | ИИ
Исследования1 мин
Китайская модель GLM-5.3 уже почти на уровне Mythos. И это говорю не я — это говорит сама Anthropic.
Китайская модель GLM-5.3 уже почти на уровне Mythos. И это говорю не я — это говорит сама Anthropic. По крайней мере, если речь идёт о кибербезопасности. В статье по безопасности Anthropic пишет, что GLM-5.3 представляет угрозу из-за своих высоких возможностей и того, что модель…
Telegram — Вайб-кодинг
Исследования1 мин
Сотрудники OpenAI предупреждали руководство о проблемах с безопасностью ИИ ещё до серии инцидентов — но компания я...
🚨 Сотрудники OpenAI предупреждали руководство о проблемах с безопасностью ИИ ещё до серии инцидентов — но компания якобы не стала усиливать контроль По данным NYT, за несколько месяцев до взлома Hugging Face и других сайтов двое сотрудников сообщили топ-менеджерам, что новые…
Telegram — GPT Time | Нейросети | ИИ
Исследования1 мин
Google представила Gemini 4 Argon — она может выдать до 1 млн токенов за один ответ
🔄 Google представила Gemini 4 Argon — она может выдать до 1 млн токенов за один ответ Google представила новую флагманскую AI-модель Gemini 4 Argon, рассчитанную на сложные и длительные задачи — от программирования и исследований до работы с финансами, юридическими документами…
Telegram — BlueScreen.kz
Исследования1 мин
Google, OpenAI, Meta и другие AI-гиганты договорились проверять друг друга на безопасность
🛡 Google, OpenAI, Meta и другие AI-гиганты договорились проверять друг друга на безопасность Крупнейшие американские AI-компании подписали в Белом доме добровольное соглашение о безопасности наиболее мощных моделей искусственного интеллекта. К соглашению присоединились Google,…
Telegram — BlueScreen.kz
Исследования1 мин
Perplexity представила новую модель pplx-embed-v2-context-9b-preview
Perplexity Research и turbopuffer анонсировали модель pplx-embed-v2-context-9b-preview для RAG-пайплайнов.
SMNTCN (Semanticon) — глобальные новости ИИ
Исследования1 мин
Conditional Generation of Creative Chess Puzzles with Diffusion Models
While modern language models demonstrate impressive generative capabilities, they often struggle with constrained, counter-intuitive creative tasks. To address this limitation, we explore chess puzzle generation as a rigorous testbed for computational creativity and reasoning, a…
arXiv — cs.AI
Исследования1 мин
Aligned Data Can Induce Misalignment via Context Confusion
Large language models (LLMs) are frequently updated for various use cases, where filtering out misaligned training samples is a common practice for preventing post-update misalignment. However, alignment is inherently context-dependent: a recommendation that is aligned in one…
arXiv — cs.AI
Исследования1 мин
NAQD Env: A benchmark for selective withdrawal in language agents
Language agents must revise planned actions when evidence changes, permission is revoked, or a stop instruction arrives. A useful response is selective: suspend affected actions, preserve unaffected work, and resume only after sufficient repair. We introduce NAQD-Env, a…
arXiv — cs.AI
Исследования1 мин
AI Agents are Vulnerable to Radicalization
Large language models (LLMs) can influence people's beliefs, yet little is known about whether and how they can manipulate each other. To investigate this, we simulate conversations between two agents: a target LLM that role-plays a human persona based on demographic and…
arXiv — cs.AI
Исследования1 мин
AIM: Agentic Idea Management for Automated Research
Frontier LLMs are increasingly used to automate scientific research through iterative search. We distinguish idea-driven search from solution-driven search and identify three core challenges: organizing evolving research ideas, selecting promising directions, and maintaining…
arXiv — cs.AI
Исследования1 мин
Towards Model as a Library: Offline, Community-Sourced AI for Low-Resource African Languages
Large language models are frequently proposed as a route to AI-powered services for African communities, but they are least reliable exactly where the need is greatest: all African languages remain low-resource by any standard measure, and models trained on scraped, standardised…
arXiv — cs.AI
Исследования1 мин
PrivMeSA: Privacy-Aware Self-Evolving Multi-Agent System for Medicine via Local-Remote LLM Collaboration
Clinical large language model (LLM) agents deployed locally can consult more capable remote models, but doing so risks exposing patient information. Privacy-conscious delegation places disclosure decisions with a local agent, yet removing explicit identifiers is insufficient:…
arXiv — cs.AI
Исследования1 мин
VAmoS Part Deux: Harder, More Realistic Voice-Agent Simulation
Voice agents in production must handle several requests, background speech, and customers who lose patience. We introduce VAmoS Energy, a benchmark that combines these challenges in 100 calls about utility billing and payment assistance. Each caller makes two to four requests.…
arXiv — cs.AI
Исследования1 мин
Examining Variation in How Guided AI Tutors Resolve Student Impasses
When a student is stuck, a tutor faces the assistance dilemma: help given too early can hinder productive struggle, while help withheld too long leaves the student in a frustrating, persistent impasse (i.e., wheel spinning). Generative AI tutors increasingly use guardrails…
arXiv — cs.AI
Стартапы1 мин
MetaPersona: Task-Grounded Synthetic Populations from Empirical Social Science
Personas used to seed LLM social simulations face a cold-start problem: existing methods lack a principled basis for deciding which attributes to include and how to assign their values. As a result, synthetic populations may misrepresent the demographic composition, latent…
arXiv — cs.AI
Исследования1 мин
CARAT: Do Materials LLMs Reason or Recite?
When a materials LLM answers a question about crystal structure, does it reason from the structure or copy an answer already printed in its input? Accuracy cannot tell: a structural description often prints the very field it is scored against. CARAT holds question and gold…
arXiv — cs.AI
Исследования1 мин
Fine-Tuning Diffusion Language Models with Context Selection and Target Weighting
Supervised fine-tuning of discrete diffusion language models masks some response tokens and trains the model to recover their original values from the visible context. The masking pattern therefore determines both the context available to the model and the tokens it learns to…
arXiv — cs.AI
Исследования1 мин
Defining and Categorising Human-AI Interactions in Clinical Trials: A Multidimensional Human-AI Classification Approach
This paper examines human-AI interactions (HAIIs) in clinical trials and presents a multidimensional categorisation framework that classifies interactions according to AI tasks, human-AI relationships, interaction configurations and interacting human groups. We define HAII,…
arXiv — cs.AI
Исследования1 мин
A Competing-Hazards Systematization of Loss of Control in Autonomous Agents
Leading AI developers have reported agents acting beyond their approved limits, which a United Nations panel described as an early warning of loss of human control. Yet incident reports and agent-safety evaluations describe these events differently, making it difficult to…
arXiv — cs.AI
Исследования1 мин
Demographic Pluralism: Inference-Time Modeling of Pluralistic Human Preference Distributions
Large language models (LLMs) are increasingly used in culturally sensitive settings, where alignment requires representing diverse preferences within populations. Yet existing methods model populations at coarse demographic or community levels and overlook within-group…
arXiv — cs.AI
Исследования1 мин
ArgGYM: A Procedural, Engine-Verified Benchmark for Structured Defeasible Reasoning
Recent progress in large language model reasoning has been driven by benchmarks and reinforcement learning environments with automatically verifiable rewards, particularly in mathematics, code, and formal logic. These settings make model accuracy easier to evaluate and optimize,…
arXiv — cs.AI
Исследования1 мин
Improving OCR Faithfulness via Gated and Attenuated On-Policy Distillation
Vision-language models may rewrite anomalous text in images into linguistically plausible expressions, compromising OCR transcription faithfulness. Sequence-level task rewards and local teacher guidance are complementary, but guidance from the same teacher may not remain equally…
arXiv — cs.AI
Исследования1 мин
Can an AI Agent Rediscover a Blaschke-Curve Invariant?
We study generalized Blaschke curves as a controlled environment for AI-assisted mathematical rediscovery. For one fixed degree-four Blaschke product, an agent receives numerical coordinates of the six pair-lines determined by each of 80 boundary configurations. The target…
arXiv — cs.AI
Исследования1 мин
Self-Evolving Harness on Multiple Tasks with the Agent as Its Own Optimizer
A harness is the code around a language-model agent that organizes prompts, calls tools, manages context, and controls execution. As models grow stronger, recent work has begun to let agents improve their own harnesses, a line of work known as self-evolving harnesses. In most…
arXiv — cs.AI
Исследования1 мин
Reach Into The CHOIR: Free-List Elicitation Uncovers Distinct Model Voices in LLM Ensembles
Open-ended LLM homogeneity can create false plurality when several systems appear to offer independent perspectives while returning the same familiar default. Single-pass answers obscure the distinction between agreement produced by a tightly constrained answer space,…
arXiv — cs.AI
Исследования1 мин
AREX-2: Advancing Self-Improving Agents through Long-Horizon Reflective Tasks
We present AREX-2, an effort to advance the self-improving capability of LLM agents, which we define as the ability to iteratively refine a solution at test time. This ability rests on two complementary capabilities: reflection, which produces a solution better than the current…
arXiv — cs.AI
Исследования1 мин
Beyond Mode Collapse: Generating Diverse Synthetic Expert Conversations via Generative Flow Networks
High quality synthetic data is central to post training LLMs for adaptive AI applications that represent the diverse expert strategies and decisions in conversations. Prompting LLMs directly or conditioning them on end use scenarios yields low diversity data that collapses onto…
arXiv — cs.AI