Research1 мин
Claude сможет подключаться к банковскому счёту и анализировать, куда уходят ваши деньги
Claude сможет подключаться к банковскому счёту и анализировать, куда уходят ваши деньги.
Telegram — GPT Time | Нейросети | ИИ
Publication Catalog
All stories
All published intelligence, sovereign stack research, ecosystem news, and AI foundation model reviews.
2201
Stories
Research1 мин
Hundreds of songs by Russian performers were found in foreign datasets for training AI - copyright holders claim that...
🎵 Hundreds of songs by Russian performers were found in foreign datasets for training AI - the copyright holders claim that permission was not given. Analysts from the National Federation of the Music Industry studied two open databases - Laion-Disco-12M and Sleeping-D 9M, containing in total...
Telegram — GPT Time | Нейросети | ИИ
Research1 мин
OpenAI Показала Нечто ОПАСНЕЕ AGI. Больше Чем ASI. Новая Угроза Человечества! Запущен Новый Цикл ИИ!
Всем привет. В этом выпуске самые главные свежие новости об искусственном интеллекте и технологиях. И приближается к новому рубежу. Модели начинают помогать создавать более совершенные версии самих себя. И агенты теперь могут доносить на другие нейросети, если замечают…
YouTube — AI Flash News
Research1 мин
Совет на ближайшие годы — идите в Data Science и ML
Совет на ближайшие годы — идите в Data Science и ML.
GitHub Разработчика
Policy & regulation1 мин
Microsoft AI CEO says AI threats are real, and Anthropic is making it worse
Today, I’m talking with Mustafa Suleyman, the CEO of Microsoft AI. As you’re no doubt aware, the biggest story in tech right now is the spiraling debate about AI safety and regulation. It should come as no surprise that Mustafa has strong opinions on how AI should be built and…
The Verge — AI
Research1 мин
Anthropic’s IPO Makes No Sense
that Anthropic at the same time is saying, "Hey, we're building something that first we have no plan, current plan on how to control it, has the potential to destroy all of humanity, and by the way, in a few weeks we're going to IPO." Like, can you help me understand that logic?…
YouTube — Matthew Berman
Research1 мин
Они это сделали: Anthropic объединяет Claude Cowork, Claude Design и обычный чат в один Claude 🎉
Они это сделали: Anthropic объединяет Claude Cowork, Claude Design и обычный чат в один Claude 🎉.
Telegram — Вайб-кодинг
Telegram — Вайб-кодинг
Telegram — Вайб-кодинг
Research1 мин
SAGE: Governed Artifact Generation from Enterprise Guidelines
Enterprise guideline documents mix narrative text, complex tables, and embedded images, and converting them into structured work artifacts still takes two to three days of manual effort each. Current language and vision-language models extract from such documents but offer no…
arXiv — cs.AI
Research1 мин
Imitation Learning for Autonomous Driving in CARLA
Behavioral cloning trains a policy offline on expert demonstrations, but deployment is closed loop: each action affects the observations the policy receives next. We study how much closed-loop driving competence a compact multimodal policy can acquire from offline demonstrations…
arXiv — cs.AI
Research1 мин
Contiguity, Not Importance: Budgeted Repair of Stale KV Caches After Document Edits
KV-cache reuse can reduce inference cost in retrieval-augmented generation and agentic systems, but cached contexts may become stale when retrieved knowledge, working memory, or user state is edited. Under causal self-attention, even a local edit can affect downstream KV states.…
arXiv — cs.AI
Research1 мин
SNOMED CT Concept Recommendation from Masked Clinical Context
Standardizing clinical language to SNOMED CT supports interoperability, analytics, and reusable phenotyping, but concept recommendation remains difficult when relevant concepts are rare or absent from training data. We present a masked-concept recommendation benchmark using the…
arXiv — cs.AI
Research1 мин
EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents
Large language model (LLM) trading agents can combine market data, news, and executable analysis, but their behavior is often controlled by static hand-written tool-use policies that are fixed before deployment. This limits their ability to adapt how they gather evidence, invoke…
arXiv — cs.AI
Research1 мин
Collaborative Memory for Multi-Agent VLM Systems
Vision-language model (VLM) agents combine specialized perception, tools, and reasoning to address complex visual tasks. In multi-agent settings, different agents inspect different image regions, video frames, or visual representations, so collaboration extends beyond…
arXiv — cs.AI
Research1 мин
Learning Heterogeneous Preferences
Learning from human feedback has become a central paradigm for training modern AI systems, where models of human utility are used as reward models in policy learning. Existing methods typically assume a \emph{universal utility} function shared across a population and treat…
arXiv — cs.AI
Research1 мин
Physics-Constrained Digital Twins for Sensor Integrity in Urban Pedestrian Flow: Detecting Stealthy False Data Injection with Conformal Guarantees
City pedestrian counting systems now feed economic indicators, planning decisions and safety operations, yet the twins built on top of them treat the incoming stream as ground truth. We study what happens when it is not. We formalise stealthy false data injection for city-scale…
arXiv — cs.AI
Research1 мин
GVD: Governed Versioning and Deduplication for Document Repositories
Document repositories evolve continuously. Guidelines and policies are revised, superseded, and re-uploaded, so the same content recurs in different wording and newer versions refine or contradict earlier ones. These inconsistencies belong to the growing collection rather than…
arXiv — cs.AI
Research1 мин
Measuring AI Leadership: Development and Validation of a Multidimensional Measure for AI-Native Organizations
AI is changing what leaders must judge, explain, learn, and coordinate, yet existing measures do not capture these behaviors at the level needed to study leadership in AI-enabled work. We develop the AI Leadership Battery, which organizes 36 behaviorally specific subdimensions…
arXiv — cs.AI
Research1 мин
FairCompressAgent: An Agentic Framework for Fairness-Aware Model Compression for FPGA Deployment
Fairness-aware model compression requires selecting methods and configurations that balance accuracy, fairness, and deployment cost. These decisions become more difficult when compression methods are composed or the user's requirements change. In this paper, we propose…
arXiv — cs.AI
Research1 мин
A Four-Stage Decomposition of Word-Problem Solving and Mechanistic Fragility in LLM Math Reasoning
Large language models solve grade-school math word problems with high accuracy, yet a single irrelevant clause inserted into the problem can collapse it. We reconcile these observations with a mechanistic account. We show that the model's internal computation decomposes into a…
arXiv — cs.AI
Research1 мин
What You Can't See Is Still What You Learn: A Preregistered Sixty-Society Confirmation That Evidence Masking Drives Compositional Generalization
Restricting what a module can read may improve what a system learns to compute. We test this in a preregistered confirmation with sixty four-cell systems sharing a frozen language-model backbone and communicating through learned continuous packets. Five conditions vary evidence…
arXiv — cs.AI
Research1 мин
ERPBench: A State-Grounded Evaluation Paradigm for Computer-Use Agents in Enterprise Software
Computer-use agents that operate through screenshots and simulated actions are advancing rapidly, yet their evaluation remains anchored to general desktop and web tasks. Enterprise Resource Planning (ERP) systems run the finance, procurement, inventory, and customer operations…
arXiv — cs.AI
Research1 мин
Memory Has Geometry: Non-Uniform Geometric Memory for Long-Horizon Personalized AI
Long-term memory is becoming a core substrate for personalized AI, yet most systems still represent personalization as discrete records in a largely static latent space, accessed under one global similarity notion. For data mining, this creates a mismatch: the evidence is a…
arXiv — cs.AI
Research1 мин
Making AI-Assisted Claims Independently Challengeable: Publication Authority and a Protocol for Falsifiable Publication Records
AI-assisted claims can appear authoritative when evidence, analysis, human authorization, presentation, and correction history refer to different states. Provenance, attestation, and transparency expose history but alone do not specify the publication transition examined here.…
arXiv — cs.AI
Research1 мин
OBC-Prune: Outcome-Based Calibration for Large Reasoning Model Pruning
Large reasoning models (LRMs) generate long chain-of-thought traces before answering, creating significant inference overhead. Pruning can reduce this cost, but its effectiveness depends on the calibration data used to estimate parameter importance. Recent work calibrates on the…
arXiv — cs.AI
Research1 мин
NeMo Data Designer: An Extensible Framework for Multimodal Synthetic Data Generation
We present NeMo Data Designer (NDD), an open-source, general-purpose framework for multi-modal synthetic data generation (SDG). Designed to be intuitive to use, NDD provides a declarative configuration format in which human and/or agent users define each dataset column, with…
arXiv — cs.AI
Research1 мин
The Inference Engineering Pareto Atlas: Which Optimizations Dominate the Cost, Quality, and Latency Frontier?
LLM inference optimizations report speedups on different models, GPUs, prompts, and quality metrics, making them hard to compare or combine. We build a cost, quality, and latency Pareto atlas to identify the best configurations for different deployment constraints. Since…
arXiv — cs.AI
Research1 мин
A Systematic Evaluation of the COTQ Provincial Land Cover Product: Structural Consistency, Spectral Separability, and Relative Positioning Against ESA, ESRI, and Google Products
High-resolution land use and land cover (LULC) products derived from Sentinel-2 imagery are widely used for environmental monitoring and land management, yet their performance can vary across regions with complex ecological gradients and heterogeneous surface conditions. In…
arXiv — cs.AI
Research1 мин
One Color Preprocessing Improves DSATUR
The Graph Coloring Problem (GCP) is NP-hard and DSATUR stands as one of the fastest heuristics for it despite producing colorings that typically use more colors than state-of-the-art coloring algorithms. We propose SSLD (Semidefinite Spectral Learning with DSATUR), which…
arXiv — cs.AI