LLM4LLM: Bridging Kernel Benchmarks and Real Deployment via Closed-Loop Agentic Optimization

📰 ArXiv cs.AI

Learn how LLM4LLM bridges the gap between kernel benchmarks and real deployment using closed-loop agentic optimization for better language model inference

advanced Published 25 Aug 2026

Action Steps

Identify the benchmark-to-deployment gap in your current language model optimization workflow
Implement closed-loop agentic optimization to bridge this gap
Use LLM4LLM to optimize kernel benchmarks for real deployment scenarios
Test and evaluate the performance and safety of your optimized models
Refine and iterate on your optimization workflow using feedback from real deployment

Who Needs to Know This

ML engineers and researchers working on language model optimization can benefit from this approach to improve the performance and safety of their models

Key Insight

💡 Closed-loop agentic optimization can help bridge the benchmark-to-deployment gap in language model optimization

Full Article

Title: LLM4LLM: Bridging Kernel Benchmarks and Real Deployment via Closed-Loop Agentic Optimization

Abstract:
arXiv:2608.21836v1 Announce Type: new Abstract: Large language models have become increasingly capable agents for low-level code and kernel optimization, but isolated kernel benchmarks provide only a proxy for the deployment behavior that matters in language-model inference. We identify a benchmark-to-deployment gap: candidate kernels that appear correct and fast in standalone harnesses can exhibit different performance, safety, or phase behavior after integration into a real inference workload.

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