Graham Schelle | Searcharxiv

arXiv2026

RHO: Your Coding Agent is Secretly a Roboticist

Code-as-Policies (CaP) has shown that large language models (LLMs) can write code to solve robotics tasks by composing perception, planning, and control primitives. Recent CaP systems, however, rely on multi-turn code-generation loops at test time, which is often infeasible for real-time robot control. We introduce Robotics Harness Optimization (RHO), a novel paradigm in which tool-enabled coding agents, at training time, propose and search for interpretable, neurosymbolic multi-file policy repositories (Repositories-as-Policies) that compose these primitives rather than a single prompt, function, or file. RHO searches with reflective feedback from environment reward and execution rather than teleoperation demonstrations. It generalizes to perturbed pick-and-place settings like LIBERO-PRO, where OpenVLA scores 0.0% and $\pi_{0.5}$ averages 12.83%. Using the same low-level primitives, RHO reaches a 45.0% success rate, 2.5x higher than the strongest multi-turn agentic system, and 3.5x higher than $\pi_{0.5}$. On Robosuite, RHO sets a new state-of-the-art of 70.0%, exceeding the prior multi-turn record of 68.29% using single-turn execution with no corrective LLM code edits at deployment. When an LLM is used in the control loop, as on RAI's O3DE benchmark, RHO optimizes the deployed agent's multi-file harness of prompts, tools, and control code, improving held-out success from 23.5% to 44.3% with 20% less wall-clock time and 27% fewer tool calls.

cs.RO↗

arXiv2025

NPUEval: Optimizing NPU Kernels with LLMs and Open Source Compilers

Neural processing units (NPUs) are gaining prominence in power-sensitive devices like client devices, with AI PCs being defined by their inclusion of these specialized processors. Running AI workloads efficiently on these devices requires libraries of optimized kernels. Creating efficient kernels demands expertise in domain-specific C++ with vector intrinsics and in-depth knowledge of the target architecture. Unlike GPU programming, which has had years to mature, NPU programming is new, with smaller and more fragmented developer communities across hardware platforms. This fragmentation poses a challenge when utilizing LLMs to assist in writing NPU kernels, as domain-specific optimized code examples are underrepresented in LLM pre-training data. In this paper we introduce NPUEval -- a benchmark for writing and evaluating NPU kernels, consisting of 102 common operators for machine learning workloads. We evaluate LLM generated code on actual hardware based on both functional correctness and vectorization efficiency using open source compiler tools targeting the AMD NPU. We evaluate a range of state-of-the-art LLMs with a mix of proprietary and open-weight models. Latest reasoning models like DeepSeek R1, show promising results achieving out-of-the-box 50%+ vectorization on select kernels. However, the average score across the entire dataset remains roughly 10% even with compiler feedback and vectorized kernel examples -- showing that this is a challenging dataset even for frontier models. The dataset and evaluation code will be released with a permissive open source license, providing an essential benchmark for advancing research in code generation and NPU kernel optimization.

cs.PL↗