// Unfolding Sub-Agents for Long-Horizon ML Engineering // Watch a single agent work a machine learning engineering task for six hours you s…

// Unfolding Sub-Agents for Long-Horizon ML Engineering // Watch a single agent work a machine learning engineering task for six hours you see issues like context fills with stack traces, dead experiments, and half-finished runs, and the strategic thread disappears under the noise. Matryoshka Agent splits that work across two levels. > An Orchestrator holds compact long-horizon exploration state and issues strategic instructions. > Sub-Agents run concrete solution attempts against the environment through a standardized tool interface and report back. This new paper contributes an efficient training paradigm for the hierarchy, so the structure is learned rather than prompted, with results across diverse model types and scales. Paper: https://arxiv.org/abs/2607.25090 Learn to build effective AI agents in our academy: https://academy.dair.ai/

Source: DAIR.AI (X) | 2026-07-29