Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs
Accepted at NeurIPS'25,
Abstract
Matryoshka Pilot proposes a framework that uses lightweight LLMs to “drive” black-box LLMs on complex reasoning and agentic tasks. By decomposing control decisions across nested layers of increasing capability, the pilot models progressively refine prompts, tool calls, and plans handed to the frontier black-box model. The method achieves strong improvements in task success and cost efficiency across a suite of reasoning and decision-making benchmarks.
Materials
BibTeX
@inproceedings{li2025matryoshka,
title={Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs},
author={Changhao Li and Yuchen Zhuang and Rushi Qiang and Haotian Sun and Hanjun Dai and Chao Zhang and Bo Dai},
booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
year={2025}
}