# AgenticSizing: A Large Language Model-based Multi-Agent Framework for Analog Circuit Sizing

arXiv: https://arxiv.org/abs/2609.25873 · HTML: https://mlsanity.com/paper/2609.25873/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 22 Sep 2026.

## Placed in

- LLM agents and tool use

## Builds on (most cited first, 13 of 13)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [AnalogCoder: Analog Circuit Design via Training-Free Code Generation](https://mlsanity.com/paper/2405.14918/) (2024)
- [ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models](https://mlsanity.com/paper/2406.18770/) (2024)
- [AmpAgent: An LLM-based Multi-Agent System for Multi-stage Amplifier Schematic Design from Literature for Process and Performance Porting](https://mlsanity.com/paper/2409.14739/) (2024)
- [LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuits](https://mlsanity.com/paper/2411.12930/) (2024)
- [DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks](https://mlsanity.com/paper/2110.00211/) (2021)
- [EEsizer: LLM-Based AI Agent for Sizing of Analog and Mixed Signal Circuit](https://mlsanity.com/paper/2509.25510/) (2025)
- [LLM-USO: Large Language Model-based Universal Sizing Optimizer](https://mlsanity.com/paper/2502.02764/) (2025)
- [AnaFlow: Agentic LLM-based Workflow for Reasoning-Driven Explainable and Sample-Efficient Analog Circuit Sizing](https://mlsanity.com/paper/2511.03697/) (2025)
- [RoSE-Opt: Robust and Efficient Analog Circuit Parameter Optimization with Knowledge-infused Reinforcement Learning](https://mlsanity.com/paper/2407.19150/) (2024)
- [Bayesian Optimization Approach for Analog Circuit Synthesis Using Neural Network](https://mlsanity.com/paper/1912.00402/) (2019)
- [A Large Language Model-based Multi-Agent Framework for Analog Circuits' Sizing Relationships Extraction](https://mlsanity.com/paper/2506.18424/) (2025)

## Abstract (arXiv)

Analog circuit sizing remains a challenging and time-consuming task due to the large design space, strong performance trade-offs, and increasing circuit complexity in scaled technologies. Although recent large language model (LLM)-based methods show promise in improving sample efficiency and interpretability, existing approaches often lack explicit circuit-topology understanding and are mainly evaluated on relatively simple analog building blocks. This paper presents a multi-agent LLM-based framework for complex analog circuit sizing. The proposed framework first analyzes the circuit topology and decomposes the netlist into functional blocks and substructures. It also extracts lightweight design knowledge for reuse. Based on the extracted topology and knowledge, a planner coordinates multiple role-specialized sizing agents to update design variables and achieve global performance specifications. This workflow mimics the collaborative process of an expert analog design team and provides a structured, interpretable, and simulation-driven optimization procedure. The framework was validated on eight circuits, with the largest design containing up to 55 transistors and 60 sizing variables. Notably, for the LDO benchmark, the proposed method achieved a 60% success rate with an average of 83 iterations, where classical optimizers failed to find feasible solutions. Further, ablation studies demonstrate that topology understanding, design-knowledge infusion, and agent specialization provide complementary benefits. The source code is available to support reproducibility.