LongHorizon

AI that stays on track for days: ML-Master 2.0

AI is great at quick tasks—but stumbles on week-long projects. This paper tackles that ultra-long-horizon gap. Meet ML-Master 2.0, an autonomous agent for machine learning engineering that stays strategically coherent over days. Its core idea, Hierarchical Cognitive Caching (HCC), treats memory like a multi-level cache and a lab

Task-Decoupled Planning: Fewer Mistakes, Faster LLM Agents

TL;DR Long-horizon LLM agents fail when one small error snowballs. Task-Decoupled Planning (TDP) breaks big jobs into a DAG of sub-goals, so planning and fixes stay local—making agents both sturdier and cheaper. How it works: * Supervisor decomposes the task into a directed acyclic graph of sub-goals. * Planner and