mlsanityData to 7 Oct 2026

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

arXiv 2601.21321 ↗First posted 29 Jan 2026cs.AIcited by 0

On the map: LLM reasoning → Reasoning for agents, planning and code

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 15 of its 35 references that are in the map. First posted on arXiv 29 Jan 2026.

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15 of 35 references.

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Same direction, newest first

  1. 2025AnaFlow: Agentic LLM-based Workflow for Reasoning-Driven Explainable and Sample-Efficient Analog Circuit Sizing5 Nov

Other references · 14

  1. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
  2. 2023Faith and Fate: Limits of Transformers on Compositionalitycited by 496
  3. 2024Large Language Models for Mathematical Reasoning: Progresses and Challengescited by 283
  4. 2023MathPrompter: Mathematical Reasoning using Large Language Modelscited by 236
  5. 2022Large Language Models Meet NL2Code: A Surveycited by 135
  6. 2020GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learningcited by 89
  7. 2020AutoCkt: Deep Reinforcement Learning of Analog Circuit Designscited by 51
  8. 2024ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Modelscited by 44
  9. 2024AmpAgent: An LLM-based Multi-Agent System for Multi-stage Amplifier Schematic Design from Literature for Process and Performance Portingcited by 29
  10. 2024LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuitscited by 22
  11. 2025EEsizer: LLM-Based AI Agent for Sizing of Analog and Mixed Signal Circuitcited by 12
  12. 2025LLM-USO: Large Language Model-based Universal Sizing Optimizercited by 11
2 more
  1. 2025LLM-based AI Agent for Sizing of Analog and Mixed Signal Circuitcited by 6
  2. 2025EasySize: Elastic Analog Circuit Sizing via LLM-Guided Heuristic Searchcited by 5

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  5. 2026AgenticSizing: A Large Language Model-based Multi-Agent Framework for Analog Circuit Sizing
  6. 2025AMS-IO-Bench and AMS-IO-Agent: Benchmarking and Structured Reasoning for Analog and Mixed-Signal Integrated Circuit Input/Output Design
  7. 2026RFAmpDesigner: A Self-Evolving Multi-Agent LLM Framework for Automated Radio Frequency Amplifier Design ↗
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