RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems
Existing AI optimizations are predominantly profiling-based. Profiling-bound feedback confines the search to the capabilities and performance of an existing software stack, preventing a fundamentally better architecture of LLM inference systems from being identified. To enable the AI-driven LLM inference system architecting loop, we argue that a general workload representation, a verifiable mutation space, and an implementation-independent evaluator are required.
- ▪Existing AI optimizations are predominantly profiling-based.
- ▪Profiling-bound feedback confines the search to the capabilities and performance of an existing software stack, preventing a fundamentally better architecture of LLM inference systems from being identified.
- ▪To enable the AI-driven LLM inference system architecting loop, we argue that a general workload representation, a verifiable mutation space, and an implementation-independent evaluator are required.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2609.12551 |
| Publication time | Mon, 14 Sep 2026 21:06:23 +0000 |
| Retrieval time | 2026-09-14T21:26:51.485Z |
| Last seen | 2026-09-14T21:26:51.485Z |
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Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2609.12551 (cs) [Submitted on 11 Sep 2026] Title:RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems Authors:Ziyue Yang, Yuting Jiang, Lei Qu, Peng Cheng View a PDF of the paper titled RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems, by Ziyue Yang and 3 other authors View PDF HTML (experimental) Abstract:AI is beginning to make substantive contributions to LLM inference optimization. Existing AI optimizations are predominantly profiling-based. Profiling-bound feedback confines the search to the capabilities and performance of an existing software stack, preventing a fundamentally better architecture of LLM inference systems from being identified.
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