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arxiv: 2601.21321 · v2 · submitted 2026-01-29 · 💻 cs.AI

LLM-Assisted Op-Amp Behavioral-Level Design via Agentic Human-Mimicking Reasoning

Zihao Chen , Ziyi Sun , Jiayin Wang , Ji Zhuang , Jinyi Shen , Xiaoyue Ke , Li Shang , Xuan Zeng
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Fan Yang
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Pith reviewed 2026-05-16 10:19 UTC · model grok-4.3

classification 💻 cs.AI
keywords LLM agentop-amp designbehavioral modelingsymbolic reasoningwhite-box optimizationanalog circuit parameter tuningcausality-driven refinement
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The pith

White-Op uses LLM agents for symbolic op-amp design that keeps 8.52 percent average error and works after transistor mapping on every topology tested

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

The paper presents White-Op, a framework in which large language model agents perform step-by-step symbolic reasoning to set behavioral-level parameters for operational amplifiers. The method decouples this reasoning from numerical solving by turning design heuristics into explicit mathematical constraints on transfer functions, poles, and zeros, then solves the resulting white-box problem programmatically. A causality-driven refinement loop traces any simulation-theory mismatch back to the exact symbolic step that caused it and corrects only that step. Experiments across nine op-amp topologies show that the resulting designs remain functional after transistor-level mapping, while black-box baselines lose functionality in five to seven cases.

Core claim

White-Op achieves interpretable behavioral-level designs with an average of 8.52% theoretical prediction error and retains circuit functionality after transistor-level mapping for all topologies, whereas black-box baselines fail in 5 to 7 topologies.

What carries the argument

The symbolic reasoning-numerical solving decoupled paradigm, in which the agent formulates a white-box optimization problem from formalized human heuristics and refines it through a causality-driven loop that links simulation mismatches to specific reasoning steps.

If this is right

  • Every successful design remains interpretable because each parameter derives from an explicit symbolic step that can be inspected.
  • The same agentic loop can be reused on new op-amp topologies without retraining the underlying model.
  • Transistor-level mapping succeeds because the behavioral parameters already satisfy the circuit equations rather than relying on post-hoc fitting.
  • Black-box baselines lose functionality because they cannot trace or correct the specific symbolic error that produced an invalid pole or zero placement.

Where Pith is reading between the lines

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

  • The refinement loop may generalize to other analog design tasks where symbolic equations can be written down even if closed-form solutions are unavailable.
  • Because the method produces executable programs from the symbolic steps, it could be inserted into existing schematic capture tools to generate initial parameter sets automatically.
  • If the same agent is given access to measured silicon data instead of simulation, the loop could close the gap between behavioral models and real fabrication variation.

Load-bearing premise

The LLM agent can carry out transfer-function simplification, pole-zero extraction, and regulation without introducing errors that the refinement loop cannot locate and fix.

What would settle it

Apply the same nine topologies plus two additional ones; if more than one topology loses functionality after transistor mapping or if average prediction error exceeds 20 percent, the central performance claim does not hold.

Figures

Figures reproduced from arXiv: 2601.21321 by Fan Yang, Jiayin Wang, Jinyi Shen, Ji Zhuang, Li Shang, Xiaoyue Ke, Xuan Zeng, Zihao Chen, Ziyi Sun.

Figure 1
Figure 1. Figure 1: Behavioral-level modeling. Fig. 1a is an MZC topology example (loads omitted). Fig. 1b shows the small-signal model. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The overall workflow of White-Op [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
read the original abstract

This paper proposes White-Op, an operational amplifier (op-amp) behavioral-level parameter design framework assisted by the human-mimicking reasoning of large language model agents. A symbolic reasoning-numerical solving decoupled paradigm is adopted: the agent performs step-by-step symbolic reasoning and formulates the design as a white-box optimization problem, which is then solved programmatically, verified via simulation, and refined iteratively. To guide this symbolic design process, implicit human reasoning mechanisms are formalized into explicit steps of introducing hypothetical constraints during transfer function simplification, pole-zero extraction and position regulation, converting design heuristics into mathematical formulations. A programming mapping protocol then standardizes the translation from symbolic designs to executable programs. Finally, a causality-driven refinement loop enables the agent to trace simulation-theory mismatches back to specific symbolic reasoning steps and make targeted corrections iteratively until convergence. Experiments on 9 op-amp topologies demonstrate that White-Op achieves interpretable behavioral-level designs with an average of 8.52\% theoretical prediction error and retains circuit functionality after transistor-level mapping for all topologies, whereas black-box baselines fail in 5 to 7 topologies. White-Op is open-sourced at https://github.com/zhchenfdu/whiteop.

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.

Referee Report

2 major / 2 minor

Summary. The manuscript presents White-Op, an LLM-agent framework for behavioral-level op-amp parameter design that decouples symbolic reasoning (transfer-function simplification, pole-zero extraction/regulation via formalized human heuristics) from numerical solving. Designs are verified by simulation and iteratively refined in a causality-driven loop that traces mismatches to specific symbolic steps. Experiments on nine topologies report an average 8.52% theoretical prediction error, 100% retention of functionality after transistor-level mapping, and consistent outperformance over black-box baselines that fail on 5–7 topologies. The code is open-sourced.

Significance. If the reported performance and interpretability claims hold, the work is significant for AI-assisted EDA. It supplies a reproducible, falsifiable pipeline that yields white-box designs grounded in standard circuit theory rather than opaque fitting, and the open-sourced implementation plus explicit mapping protocol are concrete strengths that enable follow-on research.

major comments (2)
  1. [Experimental results] Experimental results section: the superiority claim over black-box baselines (failure on 5–7 topologies) is load-bearing yet unsupported by any description of baseline implementations, prompt strategies, model sizes, or training details. Without these, the performance contrast cannot be evaluated.
  2. [Method (causality-driven refinement)] Refinement-loop description: the manuscript states that the causality-driven loop corrects mismatches until convergence, but provides no quantitative data on average iterations per topology, correction success rate, or cases where the loop failed to resolve LLM symbolic errors. This directly affects assessment of the weakest assumption (reliable symbolic reasoning).
minor comments (2)
  1. [Abstract] Abstract and results: the precise definition of 'theoretical prediction error' (e.g., which performance metrics, relative vs. absolute) is not stated, making the 8.52% figure difficult to interpret or reproduce.
  2. [Method] The programming-mapping protocol is described at a high level; a short pseudocode listing or concrete example of symbolic-to-executable translation would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive assessment and constructive feedback. We address the major comments point-by-point below and will make the necessary revisions to the manuscript.

read point-by-point responses

  1. Referee: [Experimental results] Experimental results section: the superiority claim over black-box baselines (failure on 5–7 topologies) is load-bearing yet unsupported by any description of baseline implementations, prompt strategies, model sizes, or training details. Without these, the performance contrast cannot be evaluated.

    Authors: We agree with this observation. The current manuscript lacks sufficient details on the black-box baselines, which weakens the comparison. In the revised manuscript, we will expand the Experimental Results section to include a detailed description of the baseline methods. Specifically, we will specify the LLM models (such as GPT-4), the prompt strategies (e.g., direct behavioral design prompts without symbolic decoupling), model parameters, and any other implementation details. This will allow readers to fully evaluate the performance contrast. revision: yes

  2. Referee: [Method (causality-driven refinement)] Refinement-loop description: the manuscript states that the causality-driven loop corrects mismatches until convergence, but provides no quantitative data on average iterations per topology, correction success rate, or cases where the loop failed to resolve LLM symbolic errors. This directly affects assessment of the weakest assumption (reliable symbolic reasoning).

    Authors: We acknowledge the importance of quantitative evaluation of the refinement loop. Although the manuscript describes the loop's operation, it does not report specific metrics. In the revision, we will add quantitative data from our experiments, including the average number of iterations required per topology, the correction success rate, and any cases where symbolic errors were not fully resolved by the loop. This will be presented in a new table to support the reliability of the symbolic reasoning component. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper's derivation chain consists of LLM-driven symbolic reasoning on standard circuit-theoretic operations (transfer-function simplification, pole-zero extraction and regulation), formulation of an explicit white-box optimization problem, programmatic solution, simulation-based verification, and a causality-driven iterative refinement loop. These steps are grounded in external, independently verifiable circuit theory and simulation tools rather than any self-referential definition or fitted parameter that is then relabeled as a prediction. The reported 8.52% average theoretical prediction error is computed by direct comparison to simulation outputs, and the 100% topology success rate is established through explicit transistor-level mapping and testing; neither quantity is forced by construction from the method's own inputs. No self-citation load-bearing steps, uniqueness theorems, or ansatz smuggling appear in the described framework, so the central claims remain externally falsifiable and self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the assumption that current LLMs can execute guided symbolic circuit reasoning without persistent hallucinations that the refinement loop cannot fix; no new physical entities or fitted constants are introduced beyond standard optimization and simulation tools.

axioms (1)
  • domain assumption LLMs can reliably execute step-by-step symbolic reasoning for circuit design problems when guided by formalized human mechanisms.
    The entire pipeline depends on the LLM correctly performing transfer-function simplification, pole-zero extraction, and constraint introduction.

pith-pipeline@v0.9.0 · 5535 in / 1325 out tokens · 48742 ms · 2026-05-16T10:19:22.040540+00:00 · methodology

discussion (0)

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