REVIEW 3 major objections 6 minor 1 cited by
CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs
T0 review · 3 major / 6 minor · reviewed 2026-08-08 · deepseek-v4-flash
Pith's one-line read Even GPT-4o gets only 48% on a circuit-reasoning benchmark
desk verdict A genuinely useful first benchmark for analog-circuit LLM reasoning, but the unreleased dataset and an unverified homogeneity assumption behind pass@k/n keep the headline 27.45% number from being fully trustworthy. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing design is the template unit test: every template fixes one question, diagram, and netlist, and comes with five numerical setups that differ only in component values such as $V$ and $R$. Correctly answering all five is treated as evidence of topology understanding, just as passing unit tests verifies a program's function. The accompanying metric is $\mathrm{pass@}k/n$, which counts a template as passed when at least $k$ of its $n$ setups are correct, so varying $k$ exposes how consistent a model is across numerical variations. Netlists, with a simplified element vocabulary, are given to models as a text alternative to the diagram.
What would settle it
Audit each template's five setups with human annotators who mark what reasoning each one requires; if a substantial share of templates contain setups that demand qualitatively different circuit behavior, then the $\mathrm{pass@}k/n$ score is not a clean measure of topology understanding. A concrete study would compare the pattern of model failures with independent labels of operating regime; if failures cluster at regime changes, the unit-test interpretation is falsified.
Extended reading notes
Core claim
The central claim is that current LLMs cannot robustly interpret and reason about analog circuit topologies, and that the CIRCUIT benchmark can measure this. With 510 question-answer pairs grouped into 102 unit tests, the authors find that the best model, GPT-4o, answers only 48.04% of questions correctly and passes only 27.45% of templates at the strict 5/5 level. Human evaluation confirms that the main failure mode is reasoning about topology, not arithmetic or formatting; for GPT-4o on netlist questions, reasoning errors occur in roughly 59% of responses and topology errors in roughly 36%. The paper presents this as evidence that LLMs have relevant circuit knowledge and can sometimes use netlists successfully, but lack the consistent topology-level understanding needed before they can be trusted in analog design workflows.
Load-bearing premise
The whole unit-test logic rests on the assumption that the five numerical versions of a template are interchangeable problems, so that failing any of them means the model does not understand the topology rather than that the value change created a different subproblem.
Editorial extensions
If this is right
- Global accuracy overstates competence: a model can answer nearly half the questions yet be reliable on only about a quarter of the circuit topologies.
- LLM inconsistency across component values is the main obstacle: the same topology, with different resistor or source values, defeats otherwise capable models.
- Adding netlists helps somewhat but does not solve the problem; even with netlists, topology and direction errors dominate.
- The template-unit-test design and $\mathrm{pass@}k/n$ metric can be transferred to other reasoning domains where a question family shares a core structure with varied numerical inputs.
- Analog-circuit automation that builds on LLMs must first address basic topology interpretation before it can take on design-level tasks.
Reading between the lines
- Editorial inference: the five numerical setups of a template may not be truly equivalent if a value change moves the circuit into a different operating regime, so the 27.45% pass rate should be read alongside a per-setup difficulty audit rather than as a pure measure of topology understanding.
- Editorial inference: a testable extension would be to vary component values parametrically across the full operating range and check whether a model's failures cluster at regime boundaries; if they do, the benchmark is measuring regime sensitivity as much as topology reasoning.
- Editorial inference: because the problems are adapted from standard textbooks, part of the measured performance may reflect memorized textbook solutions; re-running the benchmark with re-labeled or mirrored topologies would separate recall from genuine circuit reasoning.
- Editorial inference: the same template design could be used to measure whether chain-of-thought or tool-augmented prompting, such as calling a circuit simulator, closes the gap, which would identify whether the deficit is in the model or in the interface.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces CIRCUIT, a benchmark of 510 analog-circuit question-answer pairs organized into 102 templates, each with five numerical setups that share a template question, diagram, and (where applicable) netlist. The authors evaluate GPT-4-turbo, GPT-4o, and Gemini 1.5-pro under four prompt configurations and report global accuracy alongside a newly proposed pass@k/n template-level metric, which treats the five setups as a unit test for topology understanding. The headline results are that GPT-4o reaches 48.04% global accuracy and only 27.45% 5/5 template accuracy, leading the authors to conclude that state-of-the-art LLMs still struggle with analog circuit reasoning and generalization across numerical variability. The paper also includes automatic evaluation, human re-evaluation of a subset, error analysis, and qualitative examples.
Significance. If the claims hold, CIRCUIT would be a useful first step in evaluating LLMs for analog circuit reasoning, a domain with little existing benchmarking. The template-based unit-test design and pass@k/n metric are a thoughtful contribution that could transfer to other technical domains, and the incorporation of netlists into prompts is a practical innovation. The paper also deserves credit for performing human re-evaluation: the reported ~5% false-positive rate in automatic scoring and the detailed error taxonomy (math, formatting, reasoning, topology, direction) strengthen confidence in the measurements. However, the absence of a public dataset, the lack of statistical uncertainty quantification, and the unverified homogeneity assumption underlying pass@k/n currently limit the paper's contribution to a suggestive rather than fully established benchmark.
major comments (3)
- [Secs. 3.1, 4.1, 6.1.1 and Appendix E] The pass@k/n interpretation treats the five numerical setups of a template as interchangeable trials of the same topology. Section 3.1 asserts that the setups maintain the same structure, template, diagram, and netlist while altering V and R values, but Section 4.1 also says the setups 'aim to cover the comprehensive output range pertinent to the respective circuit.' For templates containing diodes, BJTs, MOSFETs, current mirrors, and op-amps (e.g., IDs 444, 486, and 199 in Appendix E), sweeping component values to cover the output range can move devices between operating regimes (forward/reverse bias, cutoff/triode/saturation, different feedback states). When that happens, the five setups are not five trials of the same skill but different subproblems sharing a schematic, so a model that solves three regimes and fails two others scores 3/5, which the paper interprets as inconsistency rather than as failure on distinct subproblems. This assumption is load-bearing for the headline 27.45% 5/5 result and for the conclusion that models have low generalization across numerical variability. Please add a per-template verification that all five setups require the same solution method and regime, or report and exclude/flag templates where regime changes occur, and qualify the interpretation accordingly.
- [Tables 1 and 2, Sec. 6.1.1] The paper reports many accuracy differences without confidence intervals or significance tests. With 510 questions organized into correlated groups of five per template, differences such as GPT-4-turbo's 0-shot versus 1-shot global accuracy (38.4 vs. 39.2) and GPT-4o's 0-shot-with-netlists versus 1-shot-with-netlists global accuracy (48.0 vs. 43.1) may be within sampling noise. The claim that 'the best-performing prompt varies by model' and that GPT-4o is 'the most consistent and highest-performing model' therefore needs statistical support. Please add bootstrap confidence intervals or significance tests, and account for clustering by template in the analysis.
- [Ethical Considerations] The paper states that the dataset 'will not be shared publicly until informed consent from the authors of sources listed in Appendix A is obtained.' Because CIRCUIT is presented as a benchmark, the central artifact is currently unavailable for reproduction, verification, or extension by other researchers. This is not merely a dissemination issue: benchmark claims cannot be validated or built upon without access to the data. Please provide a concrete release plan, a hosted dataset, or the full set of templates and setups in a form that does not reproduce copyrighted figures, and state the expected availability date.
minor comments (6)
- [Abstract and Sec. 6.1.1] The 27.45% figure is reported to two decimal places in the abstract and text, while Table 1 rounds to one decimal place (27.5); please harmonize the precision throughout.
- [Sec. 2] The statement that 'there are no benchmarks to assess LLMs' reasoning capability about circuits' is too strong and should be qualified as 'to our knowledge, no public benchmark for analog circuit reasoning,' with citations to any recent digital-circuit or general circuit reasoning benchmarks.
- [Table 2] The row for '0-s w/ netlists' has no entries under 'Questions Without Netlists,' which makes the table layout confusing; add explicit em-dashes or restructure the table so the reader can tell which rows apply to which subset.
- [Appendix B] There are grammatical and typographical errors in the netlist instructions: 'Some elements and nodes are have no label/name' and 'descritption' should be corrected.
- [Appendix E.5] For problem ID 265, the response ends at 1.099 and the text says the approximation 'resulted in the final answer being outside the allowed error range'; please state the ground-truth value so the reader can see why 1.099 fails the 0.001 tolerance.
- [Sec. 5.1] Please specify the exact model versions and inference dates more precisely than 'Accessed: 2024-10-01'; model behavior changes over time, and this information is needed for reproducibility.
Circularity Check
No circularity: CIRCUIT reports direct benchmark measurements; the central accuracy results are not derived from the metric or from self-citations.
full rationale
The paper makes no first-principles derivation whose conclusion is encoded in its inputs. Section 3.1 describes dataset construction from templates and numerical setups; Section 4.1 defines global accuracy and pass@k/n directly from observed answer correctness; Section 6.1.1 reports measured accuracies (e.g., GPT-4o 48.04% global, 27.45% 5/5 template). The statistic "the model passed only 27.45% of unit tests" is computed from the responses and ground truths, not fitted from a parameter later renamed as a finding. The only self-referential element is Appendix A listing "Author 1 Original problems" as one of ten dataset sources; this is a provenance disclosure and is not load-bearing for the evaluation. The reviewer concern that five numerical setups per template may not always be homogeneous (Section 3.1 says "each maintaining the same structure... altering V and R values") is a validity assumption about what pass@k/n measures, not a circular reduction: even if the assumption fails, the reported percentages would still be direct measurements, merely less interpretable as topology-level robustness. No uniqueness theorem, ansatz, or fitted-input-as-prediction pattern appears. Therefore the circularity score is 0.
Assumptions & free parameters
free parameters (3)
- absolute_answer_tolerance =
0.001
- setups_per_template =
5
- decimal_precision_requirement =
6
assumptions (4)
- domain assumption Circuit analysis laws (Ohm's law, KCL, KVL, and other standard rules) give correct answers.
- domain assumption Source materials are correct and the adaptations preserve correctness.
- ad hoc to paper The five numerical setups in a template uniformly test the same topology.
- domain assumption LLM performance on this dataset reflects analog circuit reasoning capability.
Cite this review
Pith. "Pith review of CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs." pith.science (2026). https://pith.science/paper/CZICXR6B
@misc{pith2026250207980,
author = {Pith},
title = {Pith review of: CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs},
year = {2026},
howpublished = {\url{https://pith.science/paper/CZICXR6B}},
note = {Machine review of arXiv:2502.07980}
}
read the original abstract
The role of Large Language Models (LLMs) has not been extensively explored in analog circuit design, which could benefit from a reasoning-based approach that transcends traditional optimization techniques. In particular, despite their growing relevance, there are no benchmarks to assess LLMs' reasoning capability about circuits. Therefore, we created the CIRCUIT dataset consisting of 510 question-answer pairs spanning various levels of analog-circuit-related subjects. The best-performing model on our dataset, GPT-4o, achieves 48.04% accuracy when evaluated on the final numerical answer. To evaluate the robustness of LLMs on our dataset, we introduced a unique feature that enables unit-test-like evaluation by grouping questions into unit tests. In this case, GPT-4o can only pass 27.45% of the unit tests, highlighting that the most advanced LLMs still struggle with understanding circuits, which requires multi-level reasoning, particularly when involving circuit topologies. This circuit-specific benchmark highlights LLMs' limitations, offering valuable insights for advancing their application in analog integrated circuit design.
Figures
Forward citations
Cited by 1 Pith paper
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NetlistBench: Evaluating LLM Reliability in SPICE Netlist Recognition and Manipulation
Using a deterministic canonical-circuit oracle, NetlistBench finds that LLM accuracy on SPICE netlist tasks drops sharply as structural complexity and edit horizon increase.
Reference graph
Works this paper leans on
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Gray, P. E., Hurst, P. J., Lewis, S. H., & Meyer, R. G. Analysis and Design of Analog Integrated Circuits
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MIT OpenCourseWare: 6.01 Introduction to Electrical Engineering and Computer Science I
Massachusetts Institute of Technology. MIT OpenCourseWare: 6.01 Introduction to Electrical Engineering and Computer Science I
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6.002 Circuits and Electronics, Fall 2021
Massachusetts Institute of Technology. 6.002 Circuits and Electronics, Fall 2021
work page 2021
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Author 1 Original problems
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MIT OpenCourseWare: 6.061/6.690 Introduction to Power Systems
Massachusetts Institute of Technology. MIT OpenCourseWare: 6.061/6.690 Introduction to Power Systems
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Kassakian, J. G. Principles of Power Electronics (2nd ed.)
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[10]
NETLIST_INSTRUCTION_DICTIONARY = {
Razavi, B. Design of Analog CMOS Integrated Circuits . B Netlists If a model is given a netlist in the prompt, we give it NETLIST_INSTRUCTIONS_START to which we concatenate only the relevant explanations from the NETLIST_INSTRUCTION_DICTIONARY. The traditional netlist syntax d...
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[12]
Apply Kirchhoff’s V oltage Law (KVL) around the left loop (containingV1, R1, and R2): V1 − I1R1 − V3 = 0
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[13]
Looking at experiment 6, where GPT was given the 0-shot prompt and the netlist, GPT could not reason about this topology even with a netlist
Apply Kirchhoff’s V oltage Law (KVL) around the right loop (containing V3, R2, V2, and R3): V3 + I1R2 − V2 − I2R3 = 0 ... Looking at experiment 6, where GPT was given the 0-shot prompt and the netlist, GPT could not reason about this topology even with a netlist. In step 2, it...
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[14]
Nodal Analysis: Let’s define the following nodes: - V0 = 0 V - V1 is the potential of node N 1 - V2 is the potential of node N 2 - V3 is the potential of node N 3 - V4 is the potential of node N 4 From the netlist and the given values: - V 1 = 80 V - V 2 = 100 V - V 3 = 40 V -...
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[15]
- V3 = 0 V because it’s connected to the ground
Node V oltages: - V1 is at 80 V because it’s directly connected to V 1. - V3 = 0 V because it’s connected to the ground. - V4 is at -40 V because it’s directly connected to V 3. ... 18 CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs Sometimes...
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[16]
Calculate the equivalent resistance of R2 and R3 in parallel:
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Add R4 in series with R23:
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[18]
Calculate the equivalent resistance of R5 and R6 in parallel:
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[19]
Calculate the equivalent resistance of R7 and R8 in parallel:
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[20]
Add R78 in series with R6:
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[21]
Combine R234 and R678 in parallel:
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Add R1 in series with the equivalent resistance R234678:
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[23]
Use Ohm’s Law to find the current I: ... Final Numerical Answer: 4.342778 19 CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs In experiment 6, GPT was given the 0-shot prompt and the netlist. It misunderstood the resistor network, and it did n...
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[24]
Combine R2 and R4 (parallel combination):
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Combine R24 and R3 (series combination):
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[26]
Combine R243 and R5 (series combination):
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[27]
Combine R7 and R8 (parallel combination):
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[28]
Combine R78 and R6 (series combination):
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[29]
Combine R2435 and R786 (parallel combination):
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[30]
Combine R1 and Rtotal_branch (series combination):
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[31]
Use Ohm’s Law to find the current I: ... Final Numerical Answer: 4.399751 Similar can be observed in experiments 7 and 8, where GPT was given the 1-shot prompt without and with the netlist respectively; however, it did not explicitly use the netlist given in experiment 8. 20 C...
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[32]
- R1 = 10 Ωis connected between N 1 and ground (0)
From the netlist: - A current source U = 0.6 A is connected between ground (0) and node N 1. - R1 = 10 Ωis connected between N 1 and ground (0). - R2 = 20 Ωis connected between N 1 and N 2. - A voltage source V = 30 V is connected between ground (0) and N 2
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[33]
Current U = 0.6 A flows from ground to N 1
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[34]
- IR1 is the current through R1
Apply Kirchhoff’s Current Law (KCL) at node N 1: IU = IR1 + IR2 where - IU is the current from the current source, which is 0.6 A. - IR1 is the current through R1. - IR2 is the current through R2
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[35]
Calculate the current through R1: IR1 = VN 1 R1 = VN 1 10
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[36]
Calculate the current through R2: IR2 = VN 1 − VN 2 R2 = VN 1 − 30 20 Using KCL: 0.6 = VN 1 10 + VN 1 − 30 20
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[37]
Simplify the equation to solve for VN 1: Multiply through by 20 to clear the denominators: 12 = 2VN 1 + VN 1 − 30 12 = 3VN 1 − 30 Add 30 to both sides: 42 = 3VN 1 Divide by 3: VN 1 = 14 V
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[38]
Calculate IR1, the current through R1: IR1 = VN 1 10 = 14 10 = 1.4 A
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[39]
The current I labeled in the diagram is the current through R1, which is IR1. Final Numerical Answer: 1.400000 21 CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs E.3 Misunderstanding directions GPT would sometimes misunderstand element orient...
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[40]
Consider the problem ID 31
From the netlist and the circuit diagram (Figure 134-01): - U = 0.4V - R1 = 4Ω - I1 = 1A (current flowing from N2 to ground through the current source) ... Consider the problem ID 31. In experiment 6, GPT was given a 0-shot prompt and a netlist. However, it misunderstood the l...
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[41]
- V2 is the potential at the node where V2, R1 and R2 are connected
Nodal Analysis: Let’s define the following nodes: - V0 = 0 V - V1 is the potential at node where V1 is connected. - V2 is the potential at the node where V2, R1 and R2 are connected
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[42]
Assign Potential Values: - Node at ground: V0 = 0 V - Node N1: V (N 1) = 20 V from V1 - Node N2 to ground V (N 2, 0) = 40 V from V3 ... 22 CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs E.4 Minor reasoning errors GPT would sometimes correctl...
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[43]
Given Data: - V1 = 10V -
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Identify Series and Parallel Connections: - V oltage sourceV1 is connected to R1 and the combination of R2 and R3
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[45]
Calculating Equivalent Resistance: Since R2 and R3 are connected in parallel, we calculate their equivalent resistance (R23): 1 R23 = 1 R2 + 1 R3
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[46]
Total Resistance in the Circuit: Now, calculate the total resistance in the circuit, which is the sum of R1 and R23: Rtotal = R1 + R23
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[47]
Total Current itotal: Using Ohm’s Law, the total current itotal supplied by the voltage source V1 is: itotal = V1 Rtotal
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V oltage drop acrossR1: VR1 = itotal × R1 VR1 = 0.750000 × 5 = 3.750000V Hence, voltage across R2 (which is same as VR3): VR2 = Vtotal − VR1 VR2 = 10 − 3.750000 = 6.250000V
Current through R2 and R3 (Parallel Combination): The voltage drop across the parallel combination of R2 and R3 is the same as the voltage across R1. V oltage drop acrossR1: VR1 = itotal × R1 VR1 = 0.750000 × 5 = 3.750000V Hence, voltage across R2 (which is same as VR3): VR2 =...
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[49]
Occasionally, GPT also made small errors in equation manipulation and computation
Current i3 through R2: Using Ohm’s law again for R2: i3 = VR2 R2 i3 = 6.250000 10 i3 = 0.625000A Final Numerical Answer: 0.625000 23 CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs E.5 Math errors Most math errors GPT made were approximation ...
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[50]
Identify the operating principles and small-signal parameters
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Construct the small-signal model
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Step 1: Operating Principles and Small-Signal Parameters - M1 is an NMOS transistor
Calculate the small-signal gain. Step 1: Operating Principles and Small-Signal Parameters - M1 is an NMOS transistor. - M2 and M3 are PMOS transistors used in current mirror configuration. - gm represents the transconductance of the transistor M1. Step 2: Small-Signal Model Th...
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Small-Signal Equivalent Circuit: -
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Small-Signal Resistance at Drain of M3:
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Small-Signal Current through M1:
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Current Mirror (PMOS): The current through M2 is mirrored by M3
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voltage drop across the 100 Ω resistor is not needed since Vp can be assumed to be at virtual ground if Vn = Vp
Small-Signal V oltage Gain: The voltage gain Av = Vout Vin can be computed as: Av = −gm1 · RL Given: gm1 = 10 mS = 0.01 S and RL = 100 Ω we have: Av = −(0.01 S) · (100 Ω) Simplifies to: Av = −1 Final Numerical Answer: F inalN umericalAnswer: −1.000000 26 CIRCUIT: A Benchmark f...
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- The inverting input of the op-amp (Vn) is connected to node Vn
Identify V oltages and Nodes: - The non-inverting input of the op-amp (Vp) is connected to node N 1 through a 100 Ω resistor and a 10 V source. - The inverting input of the op-amp (Vn) is connected to node Vn. - Node N 3 is the output node of the op-amp
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- The input differential voltage is zero, so Vn = Vp
Op-Amp Ideal Assumptions: - For the ideal op-amp, the voltage at the inverting input (Vn) is equal to the voltage at the non-inverting input (Vp). - The input differential voltage is zero, so Vn = Vp
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[60]
- V oltage drop across the 100Ω resistor is not needed since Vp can be assumed to be at virtual ground if Vn = Vp
Calculate Vp: - Since the non-inverting input (Vp) is connected to the junction of the 100 Ω resistor and the 10 V source, we need to find this voltage. - V oltage drop across the 100Ω resistor is not needed since Vp can be assumed to be at virtual ground if Vn = Vp
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V oltage atVp: - Given Vp directly after the resistor is simply 10 V considering ideal conditions of the circuit
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Calculate Vn: - Given Vn = Vp = 10 V
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- Current through R1 is calculated by IR1 = Vn R1
Use Ohm’s Law to determine current through R1: - Vn is at 10 V , and one side of resistorR1 is at 0 V (GND). - Current through R1 is calculated by IR1 = Vn R1 . Calculation: IR1 = Vn R1 = 10 V 2 Ω = 5 A Final Numerical Answer: Final Numerical Answer: 5.000000 In the same exper...
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[2024]
Kunal Settaluri, Ameer Haj-Ali, Qijing Huang, Keshab Hakhamaneshi, and Borivoje Nikolic
URL https://arxiv.org/abs/2406.06608. Kunal Settaluri, Ameer Haj-Ali, Qijing Huang, Keshab Hakhamaneshi, and Borivoje Nikolic. Autockt: Deep reinforce- ment learning of analog circuit designs. In 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), pages 49...
2020 arXiv
Reviewed August 8, 2026 · model on record in the stance chip above.
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