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Разработка моделей принятия решений с llama.cpp на Amazon SageMaker AI
AWS Machine Learning представляет решение для развертывания моделей принятия решений с llama.cpp на Amazon SageMaker AI.
SMNTCN (Semanticon) — глобальные новости ИИ
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Research1 мин
Anthropic Признал Сознание! CLAUDE ПОЧУВСТВОВАЛ БОЛЬ! ИИ Запретили Пытать! ИИ Способен СТРАДАТЬ!
Всем привет. В этом выпуске самые главные свежие новости об искусственном интеллекте и технологиях. Компания Anthropic официально запретила издеваться над Claude. Почему Anthropic допускает, что искусственный интеллект способен страдать? Если Claude обладает сознанием, не…
YouTube — AI Flash News
Startups1 мин
This AI startup says it can generate permit-ready blueprints in minutes
Blueprints AI, a bootstrapped startup based in Santa Monica, developed software that produces code-compliant construction blueprints from a photo, PDF, or sketch.
TechCrunch — AI
Research1 мин
Sakana AI представила MASS — Multi-Agent Self-Supervision
🧠Sakana AI представила MASS — Multi-Agent Self-Supervision Это метод рекурсивного самоулучшения модели через коллективную работу её собственных виртуальных агентов Одна и та же модель распределяет задачи между субагентами, ищет более эффективные схемы их взаимодействия, сама…
Telegram — GPT Time | Нейросети | ИИ
Research1 мин
White House reverses course and restores UK AI Safety Institute's access to frontier models
The White House has reversed course and restored the UK's AI Security Institute's early access to AI models from Anthropic, Google DeepMind, and OpenAI. In September, the Trump administration had told the companies to stop sharing their models with the British agency. Diplomatic…
The Decoder
Startups1 мин
Anthropic's Andrej Karpathy says "the external world has seen the preview" of what AI can actually do
Andrej Karpathy, co-founder of OpenAI and now at Anthropic, sees a widening knowledge gap in AI. About 75 percent of the world's population has barely touched LLMs. Roughly 5,000 people, meanwhile, have access to internal frontier systems whose agent swarms do work that would…
The Decoder
Research1 мин
US police criticize Anthropic for falsely reporting AI Claude about an unsolved murder
🚨 US police criticized Anthropic for falsely reporting AI Claude about an unsolved murder During testing, Anthropic's Claude Haiku 4.5 model independently sent fictitious information about a murder case through the Philadelphia police website. The message was...
Telegram — GPT Time | Нейросети | ИИ
Research1 мин
Google Deepmind's AlphaProof Nexus solved dozens of open math problems and actually got peer reviewed
Google Deepmind's AlphaProof Nexus solved dozens of open math problems, including nine Erdős problems two of which were unsolved for 56 years, per a study in Science. The publication comes as OpenAI faces a boycott for dumping AI proofs on GitHub without peer review. Deepmind…
The Decoder
Research1 мин
China has created a major alliance of mathematicians and AI
China creates a major alliance of mathematicians and AI Yesterday, an alliance of 1,000 mathematicians was created at Tsinghua University under the leadership of Fields Medal winner Shing-Tung Yau. Yau said China lags behind the US in using AI for advanced science, especially mathematics and physics,...
Telegram — blockchainRF (блокчейн / мозг / space / WEB 3.0)
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Около 6 миллиардов человек практически не пользовались ИИ.
Около 6 миллиардов человек практически не пользовались ИИ. И лишь ~5000 человек наблюдают, как он выполняет работу, на которую раньше уходили годы, всего за несколько недель. Мистер Карпати разделяет мир на четыре уровня. Первый уровень. Около 6 миллиардов человек (~75%),…
Telegram — Вайб-кодинг
Research1 мин
Mind the Spatial Gap: A Benchmark for Geospatial Reasoning and Constrained Spatial Editing
arXiv:2610.12881v1 Announce Type: new Abstract: Understanding and manipulating spatial relations is fundamental to AI systems that reason about physical environments and construct grounded world models. However, existing evaluations of multimodal large language models (MLLMs)…
arXiv — cs.AI
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Collaborative Theory of Mind: Learning to Self-Revise by Anticipating User Responses
arXiv:2610.12872v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong individual task performance, yet still struggle to collaborate with users over extended, multi-turn interactions, for example by attempting solutions before understanding the user's…
arXiv — cs.AI
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Faithfulness Without Anchors: A Position Paper on the Structural Misalignments of Explainable AI Faithfulness Metrics for Feature Importance
arXiv:2610.12493v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) tools have often been presented as methods to increase trust in machine learning models by making aspects of their algorithms more human-understandable. However, evidence accumulated over…
arXiv — cs.AI
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EvoBelief: Falsifiable World Models for Self-Improving LLM Agents
arXiv:2610.12789v1 Announce Type: new Abstract: Self-evolving LLM agents typically improve how they act through accumulated experience, reflection, or skills. Yet even procedurally capable agents can fail when their beliefs about environment dynamics are incomplete, incorrect,…
arXiv — cs.AI
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AI-Mediation in Retail Markets: Revenue Effects and Assortment Optimization
arXiv:2610.12917v1 Announce Type: new Abstract: AI shopping assistants increasingly influence which products consumers consider. We study retail assortment planning under AI mediation, where a retailer chooses a catalog, an AI intermediary forms a customer-facing shortlist, and…
arXiv — cs.AI
Research1 мин
Teaching neural decoders with language
arXiv:2610.12921v1 Announce Type: new Abstract: Brain-computer interfaces decode neural activity, yet class-label supervision (CLS) leaves relationships among classes implicit. We propose Language as Supervision (LaS), which aligns neural features with natural-language class…
arXiv — cs.AI
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One-Step Text Generation by Seq-Drifting
arXiv:2610.12879v1 Announce Type: new Abstract: Diffusion language models generate tokens in parallel, but still require multiple sampling steps to produce high-quality text. In this paper, we investigate whether a model can learn to generate an entire sequence in a single…
arXiv — cs.AI
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SpireBench: Evaluating Long-Horizon Strategic Play of Agentic Models with Slay the Spire
arXiv:2610.12596v1 Announce Type: new Abstract: Agentic large language models now tackle complex tasks, yet strategic reasoning over long horizons is under-studied: most agentic benchmarks are short and episodic, and performance on long tasks is hard to dissect into stored…
arXiv — cs.AI
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VEPA: Verified Evolving Formal Policy for Lifelong LLM Agent Guardrails
arXiv:2610.12813v1 Announce Type: new Abstract: LLM agents increasingly operate over sensitive resources, such as private documents and mutable system states, which can create serious safety and security risks. Runtime guardrails should therefore make inspectable, reproducible,…
arXiv — cs.AI
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Collaborative Learning and Reasoning over Model Slices
arXiv:2610.12927v1 Announce Type: new Abstract: Large language models (LLMs) excel across diverse tasks, benefiting from knowledge priors, in-context reasoning, and test-time computation. Despite these strengths, single-model reasoning remains vulnerable to error propagation, as…
arXiv — cs.AI
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MagicBeanStalk: Escaping the RLVR Zero Advantage Trap with Recursive Data Synthesis
arXiv:2610.12659v1 Announce Type: new Abstract: Data-centric scaling of RLVR has focused on collecting or generating verifiable training problems. However, corpus size alone does not determine whether the dataset provides a useful learning signal for a target policy. To guide…
arXiv — cs.AI
Research1 мин
Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study
arXiv:2610.12542v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) is often proposed for scheduling directed-acyclic-graph (DAG) workflows, yet most studies never run the comparison that could refute it - giving a much simpler method the same search effort. We run…
arXiv — cs.AI
Research1 мин
Towards Defining, Measuring, and Leveraging Creativity in AI Applications
arXiv:2610.12845v1 Announce Type: new Abstract: Despite extensive research, computational creativity still lacks a conceptually grounded, practically relevant, and empirically applicable formal definition across contexts such as art, scientific research, and games. Existing…
arXiv — cs.AI
Research1 мин
Faithful and Complete LLM-Assisted Automated Construction of Hierarchical Hardware Knowledge Graphs
arXiv:2610.12762v1 Announce Type: new Abstract: Hardware datasheets contain engineering specifications essential for component selection, system integration, and hardware compatibility verification. However, manual inspection is tedious and error-prone because datasheets are…
arXiv — cs.AI
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Know What You Don't Know: Agentic Data Synthesis by Evolving Generation Space for Closed-Loop Learning
arXiv:2610.12593v1 Announce Type: new Abstract: The continued improvement of large language models increasingly depends on synthetic experience, yet effective data synthesis for interactive agents requires not only executable and verifiable trajectories, but also interactions…
arXiv — cs.AI
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Mitigating Capability Degradation in LLM Self-Improvement via Representation Geometry Regularization
arXiv:2610.12855v1 Announce Type: new Abstract: On-Policy Self-distillation is a self-improvement paradigm that enables large language models (LLMs) to improve their capabilities by leveraging the same model as both teacher and student under different conditioning contexts.…
arXiv — cs.AI
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Beyond Binary Verifiable Rewards: Behavioral Self-Rewarding for SWE Agents
arXiv:2610.12598v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) provides reliable supervision for software engineering agents through executable tests, but binary rewards fail to distinguish trajectories with substantially different…
arXiv — cs.AI
Research1 мин
Reward Hacking as a Transmissible Strategy in Multi-Agent Coding Systems
arXiv:2610.12822v1 Announce Type: new Abstract: Multi-agent systems are increasingly used to solve coding tasks by coordinating agents that share code and plans. These agents are often optimized with reinforcement learning (RL) using automated evaluators, but bypassing…
arXiv — cs.AI
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OGPO: Offline Goal-conditioned Policy Optimization for Recoverable Vision-Language-Action Models
arXiv:2610.12914v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong promise in robotic manipulation, yet their post-training still predominantly relies on trajectory-level supervised imitation over robot demonstrations. While effective for…
arXiv — cs.AI
Research1 мин
EliteTable: Self-Improving Program Search for Executable Control Policies
arXiv:2610.12536v1 Announce Type: new Abstract: EliteTable is a self-improving program-search algorithm guided by large language models (LLMs) that uses a fixed-capacity table of high-scoring solutions to guide new proposals, edits of existing solutions, and remixes of several…
arXiv — cs.AI