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Зерттеулер1 мин

Anthropic начала раздавать API-кредиты подписчикам Claude Max

💸 Anthropic начала раздавать API-кредиты подписчикам Claude Max Теперь в зависимости от уровня подписки пользователям дают отдельный ежемесячный бюджет на API-вызовы Claude — можно тратить его из собственных приложений, скриптов, CI-сборок и других проектов. При этом лимиты…

Telegram — GPT Time | Нейросети | ИИ
Білім базасы1 мин

OpenAI’s “Alien Math” Is Freaking People Out

The math apocalypse has begun. Scott Aaronson calls this the biggest day in mathematical history. A global coalition of mathematicians formed together to oppose AI math. Only humans allowed. No clankers. Now, Anthropic worried that some of these strongly worded letters might…

YouTube — Wes Roth
Білім базасы1 мин

Tesla Powerwall and Car Backup During an Outage

What do you do during a power outage? Power goes out in my neighborhood all the time and I am currently installing Tesla's Powerwall, which allows you to get 1 to 2 days of additional power in case your electricity goes out. You can now use your car as an additional battery…

YouTube — Matthew Berman
Зерттеулер1 мин

Distillation for Incrimination and Distillation for Capabilities

Powerful misaligned AI models might recognize alignment evaluations and strategically behave well on them, rendering direct audits uninformative. However, distilling such a model into a weaker benign student places the teacher in a Distillation Double Bind: if misalignment…

arXiv — cs.AI
Зерттеулер1 мин

How Narrative Wrapping Affects LLM Refusal: A Cross-Language Benchmark and Defense

Safety-aligned language models often refuse a harmful request stated directly but answer the same request inside a role-play or narrative wrapper. We measure this vulnerability across languages and registers: attack success on Qwen3-1.7B is already 89.4% in English and 93.0% in…

arXiv — cs.AI
Зерттеулер1 мин

Plan-and-Patch: Diffusion Language Models for Agentic Planning

Planning is increasingly important for long-horizon agents, where successful execution requires coordinating subgoals, tool use, and intermediate outcomes over many steps. Yet assumptions made during planning may be invalidated by the environment, tools may return unexpected…

arXiv — cs.AI
Зерттеулер1 мин

Self-Supervised Keyframe Discovery for Horizon-Invariant Behavior Cloning

Behavior cloning (BC) in non-Markovian environments is a challenging problem because policies have to reason over contextual information over long horizons. Existing policy architectures rely on recurrent or attention-based mechanisms to capture long-term dependencies. However,…

arXiv — cs.AI
Зерттеулер1 мин

Whose Ground Truth? Embracing Ambiguity in Human-Centered AI

As AI systems increasingly interact with people and make decisions about them, understanding human interpretations becomes an important part of developing human-centered AI. Conventional machine learning and AI systems are largely developed under the assumption that a single…

arXiv — cs.AI
Зерттеулер1 мин

On the Clock: Towards Punctual and Productive Time-Budgeted AI Agents

We study whether small LLM agents can operate effectively under explicit wall-clock time budgets by both respecting the allocated runtime and using available time productively. We evaluate Qwen3.6-27B on five competitions from MLE-Bench Lite and Qwen3-4B on Zork I (Jericho), two…

arXiv — cs.AI
Зерттеулер1 мин

Learning How to Search for Plans with Exponentially Less Space

Heuristic search for a plan can store exponentially many states, even when its heuristic is almost perfect. We instead learn search control, one specification per domain, written as an indexical policy: a generalized policy with registers that hold objects and modes that…

arXiv — cs.AI
Зерттеулер1 мин

Verification and Self-Improvement in Agentic AI: Foundations and Limits

Agentic AI systems can improve by searching longer, receiving additional support, or modifying how they propose and verify outputs. A performance score does not distinguish these mechanisms. We compare these changes through bounded verification with hidden terminal randomness. A…

arXiv — cs.AI
Зерттеулер1 мин

AgentHorizon: Evaluating Agentic Judges for Long-Horizon Computer-Use Tasks

Computer-use agents are capable of completing complex tasks, increasing the use of automatic judges to determine success, either for training or for evaluation without human involvement. Despite their flexibility, their reliability on long tasks spanning multiple applications…

arXiv — cs.AI
Зерттеулер1 мин

Beyond Imitation: A Framework and Benchmark for LLM-Assisted Peer Review

The rapid growth of scientific publishing has strained peer review, particularly in machine learning, raising concerns about declining review quality and increasing reviewer workload. Large language models (LLMs) have been proposed as automated review assistants, yet their…

arXiv — cs.AI
Зерттеулер1 мин

When Interfaces Speak: Data-Aware Generative UI Harness for Active Interaction

Most human-agent interaction today remains text-based. Natural language can impose cognitive overload, ambiguity, information chaos, and slow input for complex tasks; ephemeral generative UIs can present structured information and guide users toward task completion. We propose…

arXiv — cs.AI
Зерттеулер1 мин

What to Admit and How to Present: Governing Persistent Memory in LLM Agents

Persistent memory can improve personalization in LLM agents but can also induce sycophancy and cross-domain leakage. We distinguish two governance decisions: admission, which determines what recalled information enters the working context, and presentation, which determines how…

arXiv — cs.AI
Зерттеулер1 мин

AliO: Output Alignment Matters in Long-Term Time Series Forecasing

Long-term Time Series Forecasting (LTSF) tasks, which leverage the current data sequence as input to predict the future sequence, have become increasingly crucial in real-world applications such as weather forecasting and planning of electricity consumption. However,…

arXiv — cs.AI
Салалық ЖИ1 мин

Үстелдік кеңестер: AI-арқылы жобаланған вирустардың жасаушысымен сөйлесу

2026 жылғы 16 қазан, жұма AI жаңа өмірлік формаларды жобалай алады ма? 2025 жылы Стэнфорд университетінің PhD оқушы Самуэл Кинг генеративті AI моделін пайдаланып, микроскопик вирустар үшін генетикалық жобалаулар ұсынғанда, алдын ала жауап тапты. Бұл әлі AI-арқылы жасалған өмір мысалы емес,…

MIT Technology Review