arXiv AI June 9, 2026 By Jiajie Li, Erwei Wang, Zhiru Zhang, Samuel Bayliss

From Human Guidance to Autonomy: Agent Skill System for End-to-End LLM Deployment on Spatial NPUs

arXiv:2606. 07586v1 Announce Type: cross Abstract: Spatial neural processing units (NPUs) provide an energy-efficient platform for edge LLM inference, but efficiently deploying an LLM end-to-end on such hardware remains labor-intensive.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 7

NKI-Agent: Domain-Specific Fine-Tuning and Agentic Tool Use for Neuron Kernel Generation

arXiv:2607. 04395v1 Announce Type: new Abstract: Recent agentic approaches to LLM-based kernel generation have achieved impressive results on CUDA.

By Junjie Tang, Jun Huan, Hao Zhou, Yuhao Zhang, Lin Wang

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Sep 16

Ave: Guiding Agentic GPU Optimization Using Data-Flow Invariants

arXiv:2604.18616v2 Announce Type: replace-cross Abstract: LLM coding agents can generate correct GPU kernels, but their performance still trails expert libraries. Reaching peak throughput requires co...

By Haohui Mai, Xiaoyan Guo, Xiangyun Ding, Daifeng Li, Qiuchu Yu, Chenzhun Guo, Cong Wang, Jiacheng Zhao, Christos Kozyrakis, Binhang Yuan

llms rag agents reinforcement-learning efficiency benchmarks safety

arXiv AI

Jul 24

InferenceBench: A Benchmark for Open-Ended LLM Inference Optimization by AI Agents

arXiv:2607. 20468v1 Announce Type: new Abstract: AI agents are increasingly used to automate research and development tasks, yet existing benchmarks typically evaluate them on prescribed workflows or narrow action spaces.

By Jehyeok Yeon, Ben Rank, Maksym Andriushchenko

llms agents efficiency benchmarks

arXiv Machine Learning

Jul 7

XS-VLA: Coupling Coarse-grained Spatial Distillation with Latent Flow Matching for Lightweight Robotic Control

arXiv:2607. 04171v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong multimodal understanding and spatial grounding, but their computational cost limits real-time robotic control.

By Lei Iok Tong, Qingchen Xie, Wei Huang, Ying Jie Yap, Yujie Zhang, Qianzhi Li, Xiaolong Liu, Zhidong Deng

llms diffusion robotics fine-tuning efficiency multimodal benchmarks

arXiv AI

Jun 12

Arbor: Tree Search as a Cognition Layer for Autonomous Agents

arXiv:2606. 12563v1 Announce Type: new Abstract: Arbor is a multi-agent framework that introduces structured tree search as a cognition layer for autonomous agents operating in large, stateful action spaces.

By Neha Prakriya, Chaojun Hou, Zheng Gong, Huasha Zhao, Xi Zhao, Mou Li, Zhenyu Gu, Emad Barsoum

llms agents efficiency

arXiv AI

Jul 28

Real2Sim2Real for Vision-Language-Action Manipulation: An AMD ROCm-Based Pipeline

arXiv:2607. 22997v1 Announce Type: cross Abstract: Physical AI -- the integration of large vision-language-action (VLA) models with embodied agents that act in the real world -- has emerged as the next major frontier for AI, echoed by industry leaders such as Jensen Huang (``the next big thing is Physical AI, AI with a body,'' GTC Paris, June 2025) and Dr.

By Qing Yang, Xun Wang, Ziguan Wang, Zhenjiang Li, Hongqiang Wang, Dongdong Weng

agents reinforcement-learning robotics multimodal

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NKI-Agent: Domain-Specific Fine-Tuning and Agentic Tool Use for Neuron Kernel Generation

Ave: Guiding Agentic GPU Optimization Using Data-Flow Invariants

InferenceBench: A Benchmark for Open-Ended LLM Inference Optimization by AI Agents

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