| title | Shepherd: A Runtime Substrate Empowering Meta-Agents with a Formalized Execution Trace |
|---|---|
| authors | Simon Yu, Derek Chong, Ananjan Nandi, Dilara Soylu, Jiuding Sun, Christopher D Manning, Weiyan Shi |
| date | 2026-05-11 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.10913 |
| upvotes | 4 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | We introduce Shepherd, a functional programming model that formalizes meta-agent operations on target agents as functions, with core operations mechanized in Lean. Shepherd records every agent-environment interaction as a typed event in a Git-like execution trace, enabling any past state to be forked and replayed. The system forks the agent process and its filesystem 5times faster than Docker, achieving >95% prompt-cache reuse on replay. We demonstrate the model through three applications. First, in runtime intervention, a live supervisor increases pair coding pass rates from 28.8% to 54.7% on CooperBench. Second, in counterfactual meta-optimization, branching exploration outperforms baselines across four benchmarks by up to 11 points while reducing wall-clock time by up to 58%. Third, in Tree-RL training, forking rollouts at selected turns improves TerminalBench-2 performance from 34.2% to 39.4%. These results establish Shepherd as an efficient infrastructure for programming meta-agents. We open-source the system to support future research. |
cite
@misc{yu2026shepherd,
title={Shepherd: A Runtime Substrate Empowering Meta-Agents with a Formalized Execution Trace},
author={Simon Yu and Derek Chong and Ananjan Nandi and Dilara Soylu and Jiuding Sun and Christopher D Manning and Weiyan Shi},
year={2026},
eprint={2605.10913},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.10913}
}