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Research1 мин

Did Google just dethrone OpenAI and Anthropic?

Last week, gas changed the world forever. Twice a Tesla finally unveiled the Roadster and its ability to fly by farting cold gas. But while Elon was busy pulling off his greatest magic trick, Google was cooking up a noble gas of its own, Gemini 4 Argon, which is the most…

YouTube — Fireship AI
Research1 мин

Build a Better Shopping Experience with OpenAI | Shopping Agent Demo

I've logged into Vasper and Alder, a fictitious homegoods app. I'm going to use their shopping agent to restyle my room. Let's give this a shot. I'm hosting some people for the holidays. And could you give me some ideas to make this corner of my room warmer? I'm thinking a…

YouTube — OpenAI Official
General1 мин

Spotify has removed more than 50 million AI tracks from its recommendations since the beginning of the year.

🎵 Spotify has removed more than 50 million AI tracks from recommendations since the beginning of the year. The streaming service has stepped up the fight against music spam and low-quality compositions created by neural networks. Another 75 million AI tracks were completely removed for violating platform rules. To identify...

Telegram — GPT Time | Нейросети | ИИ
Research1 мин

LightCRS: Lightweight Multi-Domain Conversational Recommendation System

arXiv:2610.26948v1 Announce Type: new Abstract: Conversational recommender systems have recently adopted vision-language models to match user dialogue intents with multimodal product information. However, extending these methods to many product domains faces a dilemma: deploying…

arXiv — cs.AI
Research1 мин

DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution

arXiv:2610.26924v1 Announce Type: new Abstract: General-purpose agents are increasingly expected to perform professional analytics work, which requires sustained reasoning, iterative investigation, and reliable evidence. Training these capabilities requires diverse and…

arXiv — cs.AI
Research1 мин

Fix, Not Just Resample: Bounded Compiler-Guided Repair in Lean 4

arXiv:2610.26942v1 Announce Type: new Abstract: Automated theorem provers often rely on sampling language models, but proof assistants like Lean reject each failed step with a precise error message --- information that pure sampling discards. To overcome this, we introduce a…

arXiv — cs.AI
Research1 мин

SPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents

arXiv:2610.26995v1 Announce Type: new Abstract: LLM-based agents increasingly rely on reusable skills. However, scaling skill libraries makes efficient and accurate skill use difficult. Full skill loading incurs high token and runtime overhead. In contrast, flat semantic…

arXiv — cs.AI
Research1 мин

Does Multi-Agent Collaboration Reduce Hallucination in Clinical Tasks?

arXiv:2610.27027v1 Announce Type: new Abstract: Multi-agent LLM systems improve performance on clinical tasks, and that improvement is often read as evidence of a more trustworthy system. However, the two are not the same claim. A clinician acts on the stated reasoning and not…

arXiv — cs.AI
Research1 мин

Teaching AI Agent to Play Poker Strategically Through Preference Optimization

arXiv:2610.27028v1 Announce Type: new Abstract: A poker agent can make a legal move and still make a strategically poor one. We ask whether compact AI agents can exhibit strategic behavior under hidden information and whether preference optimization can improve beyond static and…

arXiv — cs.AI
Research1 мин

M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement

arXiv:2610.27032v1 Announce Type: new Abstract: Large language model (LLM)-based agents have recently demonstrated outstanding performance in single-turn tasks, yet they continue to struggle with long-horizon tasks, particularly in environments without prior training. A key…

arXiv — cs.AI
Research1 мин

PORA: Learning When More Reasoning Pays Off for LLM Agents

arXiv:2610.27119v1 Announce Type: new Abstract: LLM agents perform tasks over multiple steps and revise their actions based on feedback. As the total reasoning budget is often limited and must support future steps, the agent must choose between short and long reasoning before…

arXiv — cs.AI
Research1 мин

PROVISO: Provenance-Informed Action Selection for Secure LLM Agents

arXiv:2610.27115v1 Announce Type: new Abstract: LLM agents act on the external world through tools and third-party skills, so a single executed action can cause irreversible harm. Existing defenses judge a proposed action without tracking whether its content comes from the user…

arXiv — cs.AI
Research1 мин

EDMA: Entropy-Driven Multimodal Answering

arXiv:2610.27120v1 Announce Type: new Abstract: Multimodal question answering (MMQA) is based on integrating heterogeneous data sources, selectively leveraging relevant modalities, and ignoring distractors. Existing approaches based on Multimodal Large Language Models (MLLMs)…

arXiv — cs.AI
Research1 мин

MoSAIK: Motion Search with Artificial Intelligence in K-space

arXiv:2610.27156v1 Announce Type: new Abstract: Object: Motion artefacts remain a major limitation of routine two-dimensional T2-weighted turbo spin-echo (TSE) brain MRI, reducing diagnostic confidence and often necessitating repeated acquisitions. MoSAIK (Motion Search with AI…

arXiv — cs.AI
Research1 мин

Sparse RL Relaxation for LLM Reasoning

arXiv:2610.27220v1 Announce Type: new Abstract: Recent work has explored continuous representations in place of discrete tokens during Chain-of-Thought (CoT) reasoning, often referred to as soft CoT. This is motivated by the idea that a mixture of token embeddings can represent…

arXiv — cs.AI
Research1 мин

A general-purpose System One decision model for clinical records

arXiv:2610.27099v1 Announce Type: new Abstract: Clinical care, health-system operations and biomedical discovery depend on extracting reliable answers from patient records, yet applying diverse clinical questions across entire populations remains computationally expensive. Here…

arXiv — cs.AI
Research1 мин

Physical AI: Concepts, Techniques, and Challenges

arXiv:2610.27045v1 Announce Type: new Abstract: In recent years, physical artificial intelligence (physical AI) has experienced rapid development, capturing significant attention in academia and industry. However, the field has yet to reach consensus on its definition, technical…

arXiv — cs.AI