CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs

Clarifications on Dataset Scope and Design, Technical Contributions, and Model Performance Analysis

Thank you for your thoughtful comments and questions. We address your points as follows: **Questions:** 1. **Different stages of analog circuit design:** While our dataset does not address higher-level design tasks, it establishes a foundational framework for evaluating topology understanding. Our benchmark reveals that LLMs struggle significantly with understanding basic topologies, which we consider a precursor to performing any complex design task. Without this foundational understanding, LLMs are unlikely to perform reliably on higher-level tasks. In future work, we aim to first address these fundamental challenges and then expand the dataset with more advanced analog design tasks. The structure of our dataset enables straightforward expansions for scalable and reliable automatic testing of LLMs’ capabilities in more advanced design stages. We view our work as a necessary first step, focusing on understanding topologies, which underpins all subsequent design processes. We will work on emphasizing these points in the revision. 2. **Per-level accuracies:** We acknowledge the importance of analyzing performance across levels of complexity and provide the following breakdown of GPT-4o's per-level accuracies. These can be included in the revision: | Dataset Subset | Prompt | | GPT 4o Accuracy (%) per level | | |------------------------|-------------------|:-------------:|:-------------:|:-------------:| | | | 1 | 3 | 5 | | Questions With Netlists| 0-s w/ netlists | **49.4** | 31.2 | 18.5 | | | 1-s w/ netlists | 45.0 | 28.2 | 9.2 | | Questions W/O Netlists | 1-s | 85.0 | 60.0 | 48.0 | Further insights can be drawn from Appendix E which contains an analogous table of per-category accuracies. We will also include a discussion of these results in the revision. **Weaknesses:** 1. **Technical contributions:** While prompt engineering was an important aspect of our work, a key contribution lies in the dataset design itself, which enables an automated, unit-test-like evaluation of reasoning tasks. By abstracting circuit reasoning into structured templates, we ensured a reliable and scalable evaluation process. Additionally, our pass@k/n metric provides nuanced insights into model capabilities, demonstrating how reasoning tasks can be reliably evaluated, without human or LLM involvement, offering deeper insights. These aspects set a foundation for future work on scalable, reliable, and comprehensive automatic evaluation of reasoning tasks. We will make sure to emphasize these points in the revision. 2. **Dataset scope:** We agree that analog circuit design involves more complex stages than what is addressed in our benchmark. However, our dataset was intentionally designed as a starting point, focusing on topology understanding. Initial results reveal that LLMs struggle significantly with understanding basic topologies, which we consider a precursor to performing any complex design task. In future work, we aim to first address these fundamental challenges and then expand the dataset with more advanced analog design tasks. Our revision will include a clarification of our design choices and how the dataset can be extended to more complex tasks. 3. **Reported accuracies:** We recognize the value of detailed performance analysis but find that objectively defining complexity is challenging. Currently, we rely on levels and categories, as shown earlier. Qualitative analysis reveals that models perform well only on simpler topologies. Topology complexity can be described using graph-theoretic approaches. However, question complexity may not always align with topology complexity, as the reasoning required to solve a problem can depend on factors beyond the circuit's structural intricacy. Addressing this discrepancy and developing a robust definition of question complexity remains an open challenge for future work. 4. **Limited insights:** The benchmark results highlight specific limitations in LLM reasoning, which must be addressed before these models can contribute meaningfully to design automation. Our methods for improving LLM capabilities yielded only modest gains. Nevertheless, our analysis revealed more fundamental challenges LLMs face in reasoning tasks. Addressing these foundational issues, for general and circuit-related applications, is crucial before tackling advanced design tasks and shows a promising direction for future work. We hope this clarifies your questions and addresses your concerns. If you have any further questions or feedback, we would be happy to address them.

Clarifications on Contributions and Novelty of Our Work

Thank you for your follow-up and for recognizing our efforts in characterizing the reasoning ability of LLMs in circuit design. We would like to address your concerns in detail: **The findings:** While it may seem intuitive that LLMs, primarily trained on text, would struggle with tasks involving circuit topologies—a domain that requires multi-modal reasoning—our work provides the first empirical evidence quantifying the extent and nature of these challenges. This was not a foregone conclusion, as state-of-the-art LLMs have demonstrated unexpected capabilities in other reasoning tasks. By systematically identifying specific shortcomings, our study moves beyond intuition to offer actionable insights for targeted improvements. **Insights into improving LLM reasoning:** We recognize the importance of improving reasoning capabilities, and while our focus was on benchmarking, we did attempt strategies for enhancement. Specifically, we incorporated netlists and explored prompt engineering, which led to modest but measurable improvements. These methods were deliberately chosen for their practicality and applicability, ensuring they are easy for circuit designers to implement without requiring labor-intensive annotations or descriptions. These results indicate that progress is possible, but they also reveal underlying limitations in LLM reasoning that require deeper changes—an important direction for future work. **Key contributions of our work:** Beyond characterizing LLM limitations in reasoning about analog circuit topologies, our work introduces a scalable, unit-test-inspired framework for automatically evaluating reasoning tasks. This structure ensures reliability, minimizes/removes human intervention, and provides a foundation for building more complex datasets and benchmarks. Our evaluation framework is the product of significant effort to abstract complex reasoning challenges into a scalable and reliable evaluation structure, laying the groundwork for advancing reasoning evaluation in various domains, including circuit design. We envision this framework inspiring further research into evaluation techniques for reasoning tasks. We hope this response clarifies the novelty and significance of our contributions. We appreciate your feedback and welcome further discussion.

Clarifications on Benchmark Scope, Dataset Diversity, and Future Directions

Thank you for your thoughtful comments and questions. We address your points as follows: **Questions:** 1. **How the benchmark results help LLMs for EDA-aided design:** Our results demonstrate that LLMs currently have a very limited understanding of simple circuit topologies. This fundamental limitation must be addressed as a prerequisite to integrating LLMs into the EDA-aided design process. The poor performance observed highlights that LLMs’ usefulness in such workflows would currently be limited and unreliable. Addressing these basic shortcomings in topology understanding is crucial before advancing to more complex design tasks. We shall work on emphasizing these points in our paper in the revision. 2. **Dataset scope:** While our dataset does not address higher-level design tasks, it establishes a foundational framework for evaluating topology understanding. This structure can be extended to construct datasets for scalable and reliable automatic testing of LLMs’ capabilities in more advanced design stages. We view our work as a necessary first step, focusing on topology comprehension, which underpins all subsequent design processes. Addressing LLMs’ topology understanding limitations and expanding our dataset to more complex design tasks are exciting directions for future work. **Weaknesses:** 1. **Dataset scope:** We reiterate the points made above. We acknowledge that our dataset does not address the full analog circuit design process. However, it reveals that LLMs struggle significantly with understanding basic topologies, which we consider a precursor to performing any complex design task. Without this foundational understanding, LLMs are unlikely to perform reliably on higher-level tasks. In future work, we aim to first address these fundamental challenges and then expand the dataset with more advanced analog design tasks. 2. **Dataset diversity:** Our dataset focuses on topology understanding as a starting point and is intentionally limited in scope to address this specific area. We believe the dataset’s structure can be reused to reliably automatically evaluate LLMs on more complex tasks and to expand into areas such as circuit structure design and device selection. These are promising directions for future work. 3. **Evaluation of circuit performance:** We recognize that the benchmark does not evaluate the overall performance of circuits. This was a deliberate decision to focus on reasoning and topology comprehension, which form the foundation for such evaluations. Expanding the dataset to incorporate performance metrics and end-to-end design tasks is an important future direction, but we believe the fundamental understanding tested in our work must first be established for LLMs to contribute meaningfully to circuit design. We hope this clarifies your questions and addresses your concerns. If you have any further questions or feedback, we would be happy to address them.

Clarifications on Error Types and Rates and Dataset Design and Imbalance

Thank you for your thoughtful comments and questions. We address your points as follows: **Questions:** 1. **Differentiating errors:** LLMs have not demonstrated a clear lack of background knowledge about circuits in our dataset, so we have not differentiated between the two types of errors in our evaluation. Errors are categorized as math, formatting, or reasoning errors. Topology errors are a subtype of reasoning errors, and direction errors are a subtype of topology errors. Most reasoning errors are topology errors; LLMs display sufficient background knowledge but struggle with its application. For further details on error types, please see Appendix D. Additionally, examples of reasoning errors (including topology and direction errors) can be found in Appendices F.1, F.3, F.4, and F.7. 2. **Error rate calculation:** Error rates are calculated as the ratio of data points with a specific error type to the total data points in the subset. For example, for questions with netlists, the subset consists of 79 templates × 5 setups = 395 data points. Each question is analyzed for all error types, and a single question may fall into multiple categories. For example, a question with a math and a direction error is counted under math, reasoning, topology, and direction errors. Thus, the percentages in Table 4 do not sum to 100% because errors can overlap. Constraints are: math, format, reasoning ≤ 100%, topology ≤ reasoning, and direction ≤ topology. **Weaknesses:** 1. **Sources of problems:** We agree that incorporating modern challenges is vital for broader analysis. Our dataset’s design allows for scalability to include advanced tasks, while textbook-derived problems provide a well-defined and reliable foundation for this initial work. Initial results show that LLMs struggle even with basic topology understanding, highlighting the need to first address these fundamental challenges before moving to more challenging circuits. Both are exciting directions for future work, and we shall highlight that in the revised discussion. 2. **Category and level imbalance:** We acknowledge the imbalance and agree that additional analysis would be useful. Table 5 in Appendix E provides category accuracies. In the revision, we can include a discussion of these results and an analogous table of per-level accuracies: | Dataset Subset | Prompt | | GPT 4o Accuracy (%) per level | | |------------------------|-------------------|:-------------:|:-------------:|:-------------:| | | | 1 | 3 | 5 | | Questions With Netlists| 0-s w/ netlists | **49.4** | **31.2** | **18.5** | | | 1-s w/ netlists | 45.0 | 28.2 | 9.2 | | Questions W/O Netlists | 1-s | 85.0 | 60.0 | 48.0 | Due to our level definition, we expect a high correlation between levels and categories. Addressing the challenge of achieving more balanced levels and category diversity is an exciting direction for future work. We hope this clarifies your questions and addresses your concerns. If you have any further questions or feedback, we would be happy to address them.

Clarifications on Dataset Scope, Relevance, and Benchmark Utility

Thank you for your thoughtful comments and questions. We address your points as follows: **Dataset size:** We acknowledge the dataset's limited size but emphasize that it serves as a carefully curated starting point for evaluating LLMs in analog circuit reasoning. Importantly, the dataset is designed to be scalable, enabling future expansion to address more complex tasks. Initial results show that LLMs struggle even with basic topology understanding, highlighting the need to address these fundamental challenges before moving to advanced analog design tasks. **Relevance to analog circuit design:** Our benchmark focuses on interpreting and understanding circuit topologies, which are foundational to the analog design process. This task represents an essential building block for more complex design stages. LLMs seem to struggle with this basic task, and we believe that addressing this limitation is a necessary first step before integrating LLMs into the analog design process, as their usefulness and versatility in performing more complex tasks would be limited and minimal. **Benchmark Utility:** Our benchmark design is flexible and scalable, making it suitable for evaluating hybrid approaches that combine LLMs with other models or tools. The unit-test-like methodology ensures that if a model passes all setups for a given template, it reliably demonstrates an understanding of the topology, regardless of the specific reasoning process. This approach offers a consistent and objective evaluation framework that can be extended to hybrid models by integrating their outputs into the evaluation pipeline. We acknowledge the limitation that automatic evaluation focuses on the final answer and may overlook errors in intermediate reasoning, such as hallucinations or mathematical inaccuracies that do not propagate to the final output. Future work could include automatic intermediate-step evaluation strategies to address this limitation. We hope this clarifies your questions and addresses your concerns. If you have any further questions or feedback, we would be happy to address them.