# AnaFlow: Agentic LLM-based Workflow for Reasoning-Driven Explainable and Sample-Efficient Analog Circuit Sizing

arXiv: https://arxiv.org/abs/2511.03697 · HTML: https://mlsanity.com/paper/2511.03697/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 5 Nov 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## 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)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning](https://mlsanity.com/paper/2005.00406/) (2020)
- [AnalogCoder: Analog Circuit Design via Training-Free Code Generation](https://mlsanity.com/paper/2405.14918/) (2024)
- [AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs](https://mlsanity.com/paper/2001.01808/) (2020)
- [ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models](https://mlsanity.com/paper/2406.18770/) (2024)
- [AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies](https://mlsanity.com/paper/2503.00205/) (2025)
- [Learning to Design Circuits](https://mlsanity.com/paper/1812.02734/) (2018)
- [Trust-Region Method with Deep Reinforcement Learning in Analog Design Space Exploration](https://mlsanity.com/paper/2009.13772/) (2020)
- [RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL](https://mlsanity.com/paper/2207.06412/) (2022)

## Built on by (most cited first, 11 of 11)

- [TL-GRPO: Turn-Level RL for Reasoning-Guided Iterative Optimization](https://mlsanity.com/paper/2601.16480/) (2026)
- [VLM-CAD: VLM-Optimized Collaborative Agent Design Workflow for Analog Circuit Sizing](https://mlsanity.com/paper/2601.07315/) (2026)
- [LLM-Assisted Op-Amp Behavioral-Level Design via Agentic Human-Mimicking Reasoning](https://mlsanity.com/paper/2601.21321/) (2026)
- [A Self-Calibrating Framework for Analog Circuit Sizing Using LLM-Derived Analytical Equations](https://mlsanity.com/paper/2604.07387/) (2026)
- [Agentic Electronic Design Automation: A Handoff Perspective](https://mlsanity.com/paper/2606.19795/) (2026)
- [AaLLM: An End-to-End Analog Circuit Design Framework from Topology Generation to Sizing Using Large Language Models](https://mlsanity.com/paper/2608.13472/) (2026)
- [Simulation-Aware In-Context Policy Improvement for LLM-Aided Analog Layout Refinement](https://mlsanity.com/paper/2608.13767/) (2026)
- [Spicing up Genetic Netlist Generation with LLMs](https://mlsanity.com/paper/2608.23317/) (2026)
- [AgenticSizing: A Large Language Model-based Multi-Agent Framework for Analog Circuit Sizing](https://mlsanity.com/paper/2609.25873/) (2026)
- [Long-Horizon Analog Design Bench: Benchmarking Agents on Hours-Long Analog and Mixed-Signal Circuit Design Tasks](https://mlsanity.com/paper/2609.33356/) (2026)
- [CktHGVAE: A Symmetric Design/State Heterogeneous Graph Variational Autoencoder for Transistor-level Analog/Mixed-signal Circuit Design and Analysis](https://mlsanity.com/paper/2610.19508/) (2026)

## Abstract (arXiv)

Analog/mixed-signal circuits are key for interfacing electronics with the physical world. Their design, however, remains a largely handcrafted process, resulting in long and error-prone design cycles. While the recent rise of AI-based reinforcement learning and generative AI has created new techniques to automate this task, the need for many time-consuming simulations is a critical bottleneck hindering the overall efficiency. Furthermore, the lack of explainability of the resulting design solutions hampers widespread adoption of the tools. To address these issues, a novel agentic AI framework for sample-efficient and explainable analog circuit sizing is presented. It employs a multi-agent workflow where specialized Large Language Model (LLM)-based agents collaborate to interpret the circuit topology, to understand the design goals, and to iteratively refine the circuit's design parameters towards the target goals with human-interpretable reasoning. The adaptive simulation strategy creates an intelligent control that yields a high sample efficiency. The AnaFlow framework is demonstrated for two circuits of varying complexity and is able to complete the sizing task fully automatically, differently from pure Bayesian optimization and reinforcement learning approaches. The system learns from its optimization history to avoid past mistakes and to accelerate convergence. The inherent explainability makes this a powerful tool for analog design space exploration and a new paradigm in analog EDA, where AI agents serve as transparent design assistants.