# Preprint tests execution gate for LLM-directed robot teams

_Tuesday, August 25, 2026 at 12:00 AM EDT · Robotics, Science · Latest · Tier 2 — Notable_

A new preprint describes a robot-orchestration architecture that separates a foundation-model planner from a deterministic execution gate. In drone-UGV simulations and two physical trials using Unitree G1 and Go2 interfaces, the authors report that retrieval improved skill grounding from 51% to 96%, but informed planners still sent 23% to 29% of faulted steps. The enforcement layer refused all eight injected faults before motion, compared with six faults causing robot movement without enforcement.

## Sources

- [cs.AI updates on arXiv.org](https://arxiv.org/abs/2608.22657)

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Retrieved: 2026-08-25T13:39:43.157Z
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