LeCun's startup AMI releases H-JEPA, a hierarchical world model that stacks JEPA layers to plan at multiple time scales, achieving 73% success on Visual AntMaze versus 18% for single-layer models, with code and pre-trained weights fully open-sourced.
Yann LeCun's ECCV 2026 keynote argues pixel prediction is fundamentally impossible, advocating JEPA which predicts in abstract representation spaces rather than pixels, and contends that language-only models cannot achieve human-level intelligence without world models learned from visual and sensorimotor data.
The article analyzes the limitations of reactive VLA models for long‑term robotic tasks and presents τ0‑VLA, a hierarchical “slow‑thinking, fast‑execution” system that integrates world‑model‑guided test‑time computation, large‑scale real‑world data, and a unified 40‑dimensional action space, achieving significantly higher success rates on multi‑step tasks.
ColaVLA replaces explicit text‑based reasoning with latent‑space inference and a hierarchical parallel planner, achieving lower trajectory error, reduced collision rates and up to ten‑fold faster inference while preserving safety and real‑time performance in autonomous driving benchmarks.
This article provides a comprehensive guide to AI Agent planning modules, covering their core responsibilities, architectural designs, major planning paradigms such as ReAct, Plan‑and‑Execute, Hierarchical Planning and Reflexion, detailed prompt engineering, execution frameworks, and practical case studies in data analysis and intelligent customer service.
Eko is an open‑source, cross‑platform agent framework that lets developers combine natural‑language prompts with programming code to create production‑grade virtual employees, featuring mixed agentic representation, environment‑aware architecture, hierarchical planning, visual‑interactive perception, and a multi‑level hook system for safe, controllable automation.
