QuArch: A Benchmark for Evaluating LLM Reasoning in Computer Architecture
This work addresses the problem of evaluating LLM reasoning in computer architecture for researchers and practitioners, representing an incremental step by creating a new benchmark for a previously unassessed domain.
The authors tackled the lack of evaluation benchmarks for large language models (LLMs) in computer architecture by introducing QuArch, a benchmark with 2,671 expert-validated question-answer pairs, and found that frontier models achieve accuracies ranging from 34% to 72% on advanced questions, revealing gaps in higher-order reasoning.
The field of computer architecture, which bridges high-level software abstractions and low-level hardware implementations, remains absent from current large language model (LLM) evaluations. To this end, we present QuArch (pronounced 'quark'), the first benchmark designed to facilitate the development and evaluation of LLM knowledge and reasoning capabilities specifically in computer architecture. QuArch provides a comprehensive collection of 2,671 expert-validated question-answer (QA) pairs covering various aspects of computer architecture, including processor design, memory systems, and interconnection networks. Our evaluation reveals that while frontier models possess domain-specific knowledge, they struggle with skills that require higher-order thinking in computer architecture. Frontier model accuracies vary widely (from 34% to 72%) on these advanced questions, highlighting persistent gaps in architectural reasoning across analysis, design, and implementation QAs. By holistically assessing fundamental skills, QuArch provides a foundation for building and measuring LLM capabilities that can accelerate innovation in computing systems. With over 140 contributors from 40 institutions, this benchmark represents a community effort to set the standard for architectural reasoning in LLM evaluation.