Papers, modelos e datasets em alta no Hugging Face, além do blog oficial — com leitura editorial em português.
Dataset em destaque no Hugging Face — 871 downloads. Trace Commons — Agent Traces Trace Commons is one open, public dataset of coding-agent sessions — the back-and-forth between a developer and an AI…
A year ago at NVIDIA GTC Paris at VivaTech, France laid out plans to advance local AI — from new AI factories and national compute capacity to open frontier models and industrial platforms. Now, that AI infrastructure is coming online. AI agents are running in production, startups are deploying applications and the French AI ecosystem […]
Dataset em destaque no Hugging Face — 1.1 mil downloads. Open-SWE-Traces: Advancing Distillation for Software Engineering Agents Data Overview Open-SWE-Traces is an agentic instruction tuning dataset designe…
S-Agent is a spatial reasoning framework that enhances visual language models with temporal memory and hierarchical spatial tools to enable continuous 3D world understanding from m…
Multi-LCB addresses the limitation of LiveCodeBench by providing a multi-language benchmark for evaluating LLMs across twelve programming languages while maintaining contamination…
Reinforcement learning approaches for improving LLM reasoning capabilities are enhanced by a Bayesian Manifold Curriculum framework that structures problem sampling based on task m…
Uniform 4-bit training with RHT-based quantization outperforms E2M1-based methods by eliminating shrinkage bias and improving training stability across large language model archite…
LEDGERAGENT is a method for customer service agents that maintains task states in a separate ledger to improve policy adherence and state management during tool calling.
Go-with-the-Track unifies motion control and reference image compositing in video generation by using point-track embeddings with spatial-aware encoding and video diffusion transfo…
A self-supervised transfer learning approach for parking spot occupancy recognition that achieves high accuracy with minimal labeled data through two-stage training and deployment…