CircuChain: Disentangling Competence and Compliance in LLM Circuit Analysis

• URL: http://arxiv.org/abs/2602.15037v1
• Date: Thu, 29 Jan 2026 06:13:44 GMT
• Title: CircuChain: Disentangling Competence and Compliance in LLM Circuit Analysis
• Authors: Mayank Ravishankara,
• Abstract summary: We introduce CircuChain, a diagnostic benchmark designed to disentangle instruction compliance from physical reasoning competence in electrical circuit analysis.<n>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.<n>The strongest model evaluated exhibits near-perfect physical reasoning but a high rate of convention violations when Trap conditions deliberately invert natural sign patterns.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• The Emergence of Lab-Driven Alignment Signatures: A Psychometric Framework for Auditing Latent Bias and Compounding Risk in Generative AI [0.0]
  This paper introduces a novel auditing framework to quantify latent trait estimation under ordinal uncertainty.<n>The research audits nine leading models across dimensions including Optimization Bias, Sycophancy, and Status-Quo Legitimization.
  arXiv  Detail & Related papers  (2026-02-19T06:56:01Z)
• Native Reasoning Models: Training Language Models to Reason on Unverifiable Data [16.065264121785294]
  We introduce NRT (Native Reasoning Training), a novel framework that cultivates complex reasoning.<n>NRT reframes the training problem by treating the reasoning process as a latent variable.<n>NRT achieves state-of-the-art performance among verifier-free methods.
  arXiv  Detail & Related papers  (2026-02-12T04:15:46Z)
• Interpretable Logical Anomaly Classification via Constraint Decomposition and Instruction Fine-Tuning [0.17722218114340835]
  We introduce Logical Anomaly Classification (LAC), a task that unifies anomaly detection and fine-grained violation classification in a single inference step.<n>To tackle LAC, we propose LogiCls, a vision-language framework that decomposes complex logical constraints into a sequence of verifiable subqueries.
  arXiv  Detail & Related papers  (2026-02-03T13:48:09Z)
• On the Paradoxical Interference between Instruction-Following and Task Solving [50.75960598434753]
  Instruction following aims to align Large Language Models (LLMs) with human intent by specifying explicit constraints on how tasks should be performed.<n>We reveal a counterintuitive phenomenon: instruction following can paradoxically interfere with LLMs' task-solving capability.<n>We propose a metric, SUSTAINSCORE, to quantify the interference of instruction following with task solving.
  arXiv  Detail & Related papers  (2026-01-29T17:48:56Z)
• CoG: Controllable Graph Reasoning via Relational Blueprints and Failure-Aware Refinement over Knowledge Graphs [53.199517625701475]
  CoG is a training-free framework inspired by Dual-Process Theory that mimics the interplay between intuition and deliberation.<n>CoG significantly outperforms state-of-the-art approaches in both accuracy and efficiency.
  arXiv  Detail & Related papers  (2026-01-16T07:27:40Z)
• SciIF: Benchmarking Scientific Instruction Following Towards Rigorous Scientific Intelligence [60.202862987441684]
  We introduce scientific instruction following: the capability to solve problems while strictly adhering to the constraints that establish scientific validity.<n>Specifically, we introduce SciIF, a multi-discipline benchmark that evaluates this capability by pairing university-level problems with a fixed catalog of constraints.<n>By measuring both solution correctness and multi-constraint adherence, SciIF enables finegrained diagnosis of compositional reasoning failures.
  arXiv  Detail & Related papers  (2026-01-08T09:45:58Z)
• Dictionary-Transform Generative Adversarial Networks [11.62669179647184]
  We introduce emphDictionary-Transform Generative Adversarial Networks (DT-GAN)<n> DT-GAN is a model-based adversarial framework in which the generator is a sparse synthesis dictionary and the discriminator is an analysis transform acting as an energy model.<n>We show that the DT-GAN adversarial game is well posed and admits at least one Nash equilibrium.
  arXiv  Detail & Related papers  (2025-12-25T13:41:14Z)
• Provable Benefit of Curriculum in Transformer Tree-Reasoning Post-Training [76.12556589212666]
  We show that curriculum post-training avoids the exponential complexity bottleneck.<n>Under outcome-only reward signals, reinforcement learning finetuning achieves high accuracy with sample complexity.<n>We establish guarantees for test-time scaling, where curriculum-aware querying reduces both reward oracle calls and sampling cost from exponential to order.
  arXiv  Detail & Related papers  (2025-11-10T18:29:54Z)
• AdvChain: Adversarial Chain-of-Thought Tuning for Robust Safety Alignment of Large Reasoning Models [62.70575022567081]
  We propose AdvChain, an alignment paradigm that teaches models dynamic self-correction through adversarial CoT tuning.<n>Our work establishes a new direction for building more robust and reliable reasoning models.
  arXiv  Detail & Related papers  (2025-09-29T04:27:23Z)
• Sycophancy Mitigation Through Reinforcement Learning with Uncertainty-Aware Adaptive Reasoning Trajectories [58.988535279557546]
  We introduce textbf sycophancy Mitigation through Adaptive Reasoning Trajectories.<n>We show that SMART significantly reduces sycophantic behavior while preserving strong performance on out-of-distribution inputs.
  arXiv  Detail & Related papers  (2025-09-20T17:09:14Z)
• RationAnomaly: Log Anomaly Detection with Rationality via Chain-of-Thought and Reinforcement Learning [27.235259453535537]
  RationAnomaly is a novel framework that enhances log anomaly detection by synergizing Chain-of-Thought fine-tuning with reinforcement learning.<n>We have released the corresponding resources, including code and datasets.
  arXiv  Detail & Related papers  (2025-09-18T07:35:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.