Papers, modelos e datasets em alta no Hugging Face, além do blog oficial — com leitura editorial em português.
SelfCompact is a scaffolding approach that enables models to autonomously determine optimal compaction timing and methods for managing long agent traces, achieving better performan…
A novel RL training approach for terminal agents achieves superior performance using a simplified recipe and expanded dataset, enabling effective training with fewer parameters tha…
Language models should assist causal discovery workflows by providing contextual support and explanations rather than generating causal conclusions, as demonstrated through a platf…
PhoneBuddy combines real and mock app environments to improve training of open models for phone use, demonstrating enhanced task success rates through mixed reinforcement learning…
UniverSat introduces a Universal Patch Encoder for Vision Transformers that enables robust, sensor-agnostic spatial feature extraction across diverse Earth Observation data types.
A principled synthesis engine generates high-quality terminal-agent tasks through multi-dimensional capability taxonomy and evidence-guided research, creating a distilled dataset t…
DR-MV3D presents a map-grounded learning framework with dense rewards to improve multi-view 3D visual question answering through global map construction, view-trajectory planning,…
Pre premature commitment in long-horizon LLM agents leads to silent failures where agents defend early interpretations without considering alternatives, and hidden-state convergenc…
Vera is a layered diffusion framework that preserves video content during editing by generating edit layers and alpha mattes through a Mixture-of-Transformers architecture.
Computer-use agents frequently expose inappropriate information across applications, prompting the creation of AgentCIBench to evaluate and mitigate privacy risks in cross-applicat…
LLMs are stateless by default. Agent memory fixes that. This guide breaks down all 7 types — working, semantic, episodic, procedural, retrieval, parametric, and prospective. It covers what each stores, where it lives, and when to build it. Includes a comparison table and working Python code. The post The 7 Types of Agent Memory: A Technical Guide for AI Engineers appeared first on MarkTechPost .