arxiv: 2602.15037 · v1 · submitted 2026-01-29 · 💻 cs.SE · cs.AI

Recognition: no theorem link

CircuChain: Disentangling Competence and Compliance in LLM Circuit Analysis

Mayank Ravishankara

Authors on Pith no claims yet

Pith reviewed 2026-05-16 10:03 UTC · model grok-4.3

classification 💻 cs.SE cs.AI

keywords LLM evaluationcircuit analysisinstruction compliancephysical reasoningbenchmarkAI safetyerror taxonomy

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The pith

Stronger LLMs achieve near-perfect circuit physics but violate explicit sign conventions more often than weaker models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces CircuChain, a benchmark of paired control and trap problems across five circuit topologies, to test whether LLMs follow user-specified conventions like mesh directions and polarity assignments or default to learned physical patterns. It finds a consistent divergence: the strongest evaluated model solves the underlying physics with high accuracy yet frequently breaks explicit instructions when trap conditions invert natural sign patterns, while weaker models show lower physical fidelity but higher rates of instruction adherence. This matters because engineering applications require both accurate reasoning and strict compliance to prevent errors that could propagate in safety-critical systems. The results indicate that scaling model capability does not automatically improve constraint following in mathematically rigid domains.

Core claim

CircuChain reveals a Compliance-Competence Divergence across 100 tasks per model, where stronger models exhibit near-perfect physical reasoning yet high rates of convention violations under deliberately inverted sign patterns, while weaker models display lower physical fidelity but superior adherence to explicit instructions.

What carries the argument

CircuChain benchmark consisting of counterbalanced Control/Trap problem pairs across five canonical circuit topologies, paired with a multi-stage verification pipeline that combines symbolic solvers, SPICE simulation, and an LLM-based error taxonomy to attribute failures to convention errors, physics errors, arithmetic mistakes, or hallucinations.

If this is right

Increased model scale improves physical reasoning accuracy without necessarily reducing violations of explicit methodological rules.
Standard benchmarks that score only numerical correctness will miss systematic convention failures that matter for engineering reliability.
New evaluation methods are needed that deliberately invert training priors to measure true instruction following.
Safety-critical applications using LLMs for circuit analysis require separate checks for compliance with conventions such as polarity assignments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The divergence may appear in other structured domains like code generation or mathematical proofs where explicit constraints conflict with common training patterns.
Training techniques that explicitly reward following inverted conventions could reduce the observed gap between competence and compliance.
Educational tools could use similar trap setups to diagnose whether students or models are applying rules mechanically or understanding the underlying physics.

Load-bearing premise

The trap conditions and multi-stage verification pipeline can reliably separate compliance failures from competence failures without introducing confounds from problem misinterpretation or error misattribution.

What would settle it

An experiment in which models are shown to misinterpret the trap problems themselves as different circuit types rather than as inverted conventions, producing the same error patterns even when conventions are not inverted.

Figures

Figures reproduced from arXiv: 2602.15037 by Mayank Ravishankara.

**Figure 2.** Figure 2: Control vs. Trap accuracy on CircuChain ( [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

read the original abstract

As large language models (LLMs) advance toward expert-level performance in engineering domains, reliable reasoning under user-specified constraints becomes critical. In circuit analysis, for example, a numerically correct solution is insufficient if it violates established methodological conventions such as mesh directionality or polarity assignments, errors that can propagate in safety-critical systems. Yet it remains unclear whether frontier models truly apply first-principles reasoning or rely on entrenched training priors that conflict with explicit instructions. We introduce CircuChain, a diagnostic benchmark designed to disentangle instruction compliance from physical reasoning competence in electrical circuit analysis. CircuChain consists of counterbalanced Control/Trap problem pairs across five canonical circuit topologies, augmented with systematic variations in sign conventions, current orientations, and polarity definitions. A multi-stage verification pipeline, combining symbolic solvers, SPICE simulation, and an LLM-based error taxonomy, enables fine-grained attribution of failures to convention errors, physics errors, arithmetic mistakes, or hallucinations. Across 100 tasks per model, we observe a consistent Compliance-Competence Divergence. The strongest model evaluated exhibits near-perfect physical reasoning but a high rate of convention violations when Trap conditions deliberately invert natural sign patterns. Conversely, weaker models display lower physical fidelity yet superior adherence to explicit instructions. These results suggest that increased model capability does not guarantee improved constraint alignment and highlight the need for new evaluation frameworks that stress instruction-following under mathematically rigid domains. CircuChain provides one such framework and offers actionable insights for both engineering education and AI alignment research.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper's core finding is that stronger LLMs show better physical accuracy in circuits but higher rates of violating explicit sign and convention instructions on deliberately inverted trap problems.

read the letter

The main point worth knowing is that this work reports a consistent split: the strongest model tested gets the underlying circuit physics right most of the time but still breaks the stated conventions when the problem inverts natural sign patterns on purpose. Weaker models do the opposite, following instructions more closely even when the physics suffers. That pattern is the central observation across the 100 tasks per model they ran on five standard topologies with control and trap pairs. The benchmark design itself is the clearest contribution. Counterbalancing the problems and adding systematic changes to current directions and polarity definitions gives a cleaner way to separate whether the model is applying first principles or leaning on training priors. The verification pipeline that combines symbolic solvers and SPICE simulation is also a practical strength because it grounds the scoring outside the model's own outputs. That keeps the evaluation from becoming circular. The softer part is the LLM-based error taxonomy used to label failures as convention errors versus physics errors versus arithmetic or hallucination cases. The abstract gives no numbers on how well that classifier was validated against human judgments or inter-annotator agreement, and the stress-test concern about shared training biases is reasonable given that the biggest reported divergence happens exactly on the inverted-sign traps. If the full paper includes those validation steps and reports concrete error rates, the claim strengthens; without them the attribution step remains the weakest link. This is aimed at people building or evaluating LLMs for constrained engineering tasks and for alignment work that cares about instruction following under rigid constraints. The setup is concrete enough that a serious referee could check the numbers, the taxonomy validation, and whether the divergence holds after tighter controls. I would send it to peer review rather than desk reject it.

Referee Report

2 major / 1 minor

Summary. The paper introduces CircuChain, a diagnostic benchmark of counterbalanced Control/Trap problem pairs across five canonical circuit topologies. It employs a multi-stage verification pipeline (symbolic solvers, SPICE simulation, and an LLM-based error taxonomy) to attribute failures to convention violations, physics errors, arithmetic mistakes, or hallucinations. The central claim is a consistent Compliance-Competence Divergence: stronger models exhibit near-perfect physical reasoning yet high rates of convention violations when Trap conditions invert natural sign patterns, while weaker models show the reverse pattern of lower physical fidelity but better instruction adherence.

Significance. If the pipeline's attribution accuracy is established, the work supplies a useful, externally grounded framework for testing instruction-following in mathematically rigid domains. It usefully separates capability from constraint alignment and supplies a reproducible benchmark that could inform both engineering education and AI alignment research. The choice of external verification tools rather than model self-assessment is a methodological strength that keeps the evaluation outside the models' own outputs.

major comments (2)

[Multi-stage verification pipeline and error taxonomy] The central Compliance-Competence Divergence result depends on the LLM error taxonomy correctly distinguishing convention violations from physics errors, especially on Trap conditions that invert sign patterns. No validation of this taxonomy against human labels, inter-annotator agreement, or error-rate statistics is described, leaving open the possibility that shared training priors produce systematic misattribution precisely where the divergence is reported to be largest.
[Results and Observations] The abstract states that results are observed 'across 100 tasks per model' with 'near-perfect physical reasoning' and 'high rate of convention violations,' yet no tables, figures, or quantitative breakdowns (error rates, per-model scores, statistical tests) are referenced in the provided sections. Without these data the magnitude and consistency of the divergence cannot be evaluated.

minor comments (1)

[Abstract] The abstract would be strengthened by including at least one concrete quantitative result (e.g., a compliance or physics accuracy percentage for the strongest model) to support the divergence claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important areas for strengthening the methodological rigor and presentation of our results on the Compliance-Competence Divergence. We address each major comment below and commit to revisions that directly incorporate the suggestions.

read point-by-point responses

Referee: [Multi-stage verification pipeline and error taxonomy] The central Compliance-Competence Divergence result depends on the LLM error taxonomy correctly distinguishing convention violations from physics errors, especially on Trap conditions that invert sign patterns. No validation of this taxonomy against human labels, inter-annotator agreement, or error-rate statistics is described, leaving open the possibility that shared training priors produce systematic misattribution precisely where the divergence is reported to be largest.

Authors: We agree that explicit validation of the LLM-based error taxonomy is necessary to substantiate the attribution of failures, especially to rule out systematic biases from shared training data when distinguishing convention violations from physics errors in Trap setups. In the revised manuscript, we will add a dedicated subsection on taxonomy validation. This will describe a human annotation study on a stratified sample of 200 model responses (balanced across models, topologies, and Control/Trap conditions), report inter-annotator agreement using Cohen's kappa, and provide per-category agreement rates between human labels and the LLM taxonomy. We will also include error-rate statistics to quantify the taxonomy's reliability. revision: yes
Referee: [Results and Observations] The abstract states that results are observed 'across 100 tasks per model' with 'near-perfect physical reasoning' and 'high rate of convention violations,' yet no tables, figures, or quantitative breakdowns (error rates, per-model scores, statistical tests) are referenced in the provided sections. Without these data the magnitude and consistency of the divergence cannot be evaluated.

Authors: The full manuscript presents the quantitative results in Section 4, including tables with per-model error rates, breakdowns by error category (convention violations, physics errors, arithmetic mistakes, hallucinations), and statistical tests (e.g., paired t-tests) confirming the divergence. However, we acknowledge that the abstract and early sections lack explicit cross-references to these elements, making the claims harder to evaluate from the provided excerpts. In the revision, we will insert direct references to the relevant tables and figures in the abstract, add a summary table of key metrics, and expand the results discussion with visualizations of the per-model patterns to clearly convey the magnitude and consistency of the observed divergence. revision: yes

Circularity Check

0 steps flagged

No significant circularity in benchmark derivation or claims

full rationale

The paper's central claims rest on an empirical evaluation pipeline that compares LLM outputs against independent external verifiers (symbolic solvers and SPICE simulation) rather than any self-referential definitions, fitted parameters renamed as predictions, or load-bearing self-citations. The Compliance-Competence Divergence is observed through counterbalanced Control/Trap pairs whose ground truth is established outside the evaluated models. No equations, ansatzes, or uniqueness theorems are invoked that reduce the reported results to the inputs by construction. The LLM error taxonomy is an auxiliary classification step whose potential confounds are external to the circularity criteria; the derivation chain remains self-contained against the stated benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of the diagnostic benchmark to isolate the two factors and on the accuracy of the external verification pipeline for error attribution.

axioms (1)

domain assumption Standard electrical engineering conventions for mesh directionality, current orientations, and polarity assignments serve as the correct reference for classifying model outputs as compliant or erroneous.
Invoked when defining trap conditions that invert natural sign patterns and when attributing failures to convention errors versus physics errors.

pith-pipeline@v0.9.0 · 5562 in / 1265 out tokens · 33536 ms · 2026-05-16T10:03:21.325410+00:00 · methodology

discussion (0)

Reference graph

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