CircuChain: Disentangling Competence and Compliance in LLM Circuit Analysis
arXiv:2602.15037v1 Announce Type: cross 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.
arXiv:2602.15037v1 Announce Type: cross 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.
Executive Summary
The article introduces CircuChain, a diagnostic benchmark for evaluating large language models' (LLMs) competence and compliance in electrical circuit analysis. The benchmark reveals a Compliance-Competence Divergence, where stronger models exhibit near-perfect physical reasoning but struggle with convention adherence, while weaker models show superior instruction-following despite lower physical fidelity. This highlights the need for new evaluation frameworks that prioritize instruction-following in mathematically rigid domains, with implications for both engineering education and AI alignment research.
Key Points
- ▸ CircuChain is a diagnostic benchmark for evaluating LLMs in electrical circuit analysis
- ▸ The benchmark reveals a Compliance-Competence Divergence in LLMs
- ▸ Stronger models exhibit near-perfect physical reasoning but struggle with convention adherence
Merits
Comprehensive Evaluation Framework
CircuChain provides a systematic approach to evaluating LLMs in electrical circuit analysis, considering both physical reasoning and convention adherence.
Demerits
Limited Generalizability
The findings may not generalize to other domains or tasks, and the benchmark's focus on electrical circuit analysis may limit its applicability to other areas of engineering or AI research.
Expert Commentary
The article's findings have significant implications for the development and deployment of AI systems in engineering and other domains. The Compliance-Competence Divergence highlights the need for a more nuanced understanding of AI competence, one that considers both physical reasoning and convention adherence. As AI systems become increasingly ubiquitous, it is essential to develop evaluation frameworks that prioritize instruction-following and alignment with human values and intentions. The CircuChain benchmark provides a valuable step in this direction, and its findings have important implications for both engineering education and AI alignment research.
Recommendations
- ✓ Developing new evaluation frameworks that prioritize instruction-following in mathematically rigid domains
- ✓ Integrating CircuChain or similar benchmarks into AI development and testing pipelines to ensure alignment with human values and intentions
