TLDR: QUARCH is the first benchmark designed to evaluate large language models (LLMs) in computer architecture. Comprising 2,671 expert-validated questions, it assesses four key skills: Recall, Analyze, Design, and Implement. Initial evaluations show that while LLMs possess domain knowledge, they struggle significantly with higher-order reasoning, particularly in design and implementation tasks. The benchmark highlights specific failure modes, such as difficulties with code execution semantics, unconventional assumptions, system state tracking, and multimodal input interpretation. QUARCH’s rigorous methodology, including LLM-as-a-judge validation, provides a crucial tool for advancing AI capabilities in computing systems design.
The world of large language models (LLMs) is rapidly expanding, with these powerful AI systems showing incredible capabilities across many fields. However, one crucial area has been largely overlooked in their evaluation: computer architecture. This field, which acts as a bridge between high-level software and low-level hardware, demands a unique blend of knowledge and complex reasoning. To address this gap, a new benchmark called QUARCH (pronounced ‘quark’) has been introduced.
QUARCH is the first benchmark specifically designed to assess how well LLMs understand and reason within computer architecture. It’s a comprehensive collection of 2,671 question-answer pairs, all validated by experts. These questions cover a wide range of topics, including how processors are designed, how memory systems work, and how different parts of a computer connect and communicate.
The initial evaluations using QUARCH have revealed some interesting insights. While advanced LLMs do possess a good amount of domain-specific knowledge, they often struggle with tasks that require deeper, higher-order thinking in computer architecture. For instance, their accuracy on these more advanced questions varies significantly, from a low of 34% to a high of 72%. This highlights persistent challenges in how LLMs perform architectural reasoning, especially when it comes to analyzing problems, designing solutions, and implementing them.
The benchmark is structured around four core competencies that are essential for computer architects: Recall, Analyze, Design, and Implement. Recall questions test basic facts and definitions, like asking what information a branch target buffer stores. Analyze questions require models to deduce, infer, or calculate based on a given scenario, such as comparing the performance of different branch prediction methods. Design questions challenge models to propose or improve architectural features, balancing various trade-offs like power and performance. Finally, Implement questions ask models to translate a design into executable code or simulation scripts, validating a solution.
QUARCH was built using a unique three-pronged approach. It combines synthetically generated questions, contributions from a community of experts, and questions curated from academic exams. Crucially, every single question-answer pair in the benchmark was reviewed and validated by doctoral students with advanced training in computer architecture, ensuring high quality and technical correctness.
The benchmark also covers a diverse range of topics within computer architecture, with processor architecture, memory systems, and interconnection networks being the most prominent. It includes both multiple-choice questions (MCQs) and free-response questions (FRQs), allowing for a broad assessment. Furthermore, QUARCH features both text-only questions and multimodal questions that include images and text, testing the LLMs’ ability to interpret diagrams, schematics, and tables.
When frontier models were tested on QUARCH, a clear pattern emerged: they performed much better on recall-focused questions than on those requiring higher-order reasoning. This suggests that while LLMs can retrieve facts, they struggle to apply that knowledge to complex problem-solving scenarios. For example, a reasoning-enhanced version of GPT-5 showed significantly better performance on analysis, design, and implementation tasks compared to its non-reasoning counterpart.
The research also identified several key areas where LLMs falter. They struggle with understanding the architectural implications of code execution, often failing to predict how high-level code interacts with underlying hardware. Models sometimes make unconventional architectural assumptions if not explicitly guided, such as defaulting to word-level addressing instead of the more common byte-addressable memory. Tracking complex system states and understanding how local actions cascade into system-wide effects also proved challenging. Additionally, LLMs showed varying levels of expertise across different sub-domains of computer architecture and struggled with multimodal questions that required interpreting diagrams and visual information.
To ensure the reliability of the free-form answer evaluations, the researchers employed an “LLM-as-a-judge” methodology, where an external LLM assessed the correctness of responses. This approach was rigorously validated against human domain-expert judgments, showing an impressive agreement rate of 85.35%. This level of agreement is comparable to how often human experts agree with each other, making the LLM-as-a-judge a trustworthy and scalable method for evaluation.
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In conclusion, QUARCH provides a vital foundation for developing and measuring LLM capabilities in computer architecture. By systematically evaluating fundamental and advanced skills, this benchmark can accelerate innovation in computing systems and help build more effective AI agents for system design. You can find more details about this research paper here.
