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

• URL: http://arxiv.org/abs/2511.03697v1
• Date: Wed, 05 Nov 2025 18:24:01 GMT
• Title: AnaFlow: Agentic LLM-based Workflow for Reasoning-Driven Explainable and Sample-Efficient Analog Circuit Sizing
• Authors: Mohsen Ahmadzadeh, Kaichang Chen, Georges Gielen,
• Abstract summary: A novel agentic AI framework for sample-efficient and explainable analog circuit sizing is presented.<n>The AnaFlow framework is demonstrated for two circuits of varying complexity and is able to complete the sizing task fully automatically.<n>The inherent explainability makes this a powerful tool for analog design space exploration and a new paradigm in analog EDA.
• Score: 1.2617078020344616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
  Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
  arXiv  Detail & Related papers  (2026-01-29T11:33:49Z)
• ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
  6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
  arXiv  Detail & Related papers  (2026-01-27T13:43:59Z)
• OSIRIS: Bridging Analog Circuit Design and Machine Learning with Scalable Dataset Generation [1.6249267147413524]
  We present OSIRIS, a dataset generation pipeline for analog IC design.<n> OSIRIS produces comprehensive performance metrics and metadata.<n>We release a dataset consisting of 87,100 circuit variations generated with OSIRIS.
  arXiv  Detail & Related papers  (2026-01-27T10:18:46Z)
• Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network [84.22115118596741]
  We propose an end-to-end deep learning (DL) framework with low inference complexity for symbol-level precoding.<n>We show that the proposed framework captures substantial performance gains of optimal SLP, while achieving an approximately 80-times speedup over conventional methods.
  arXiv  Detail & Related papers  (2025-10-02T15:15:50Z)
• Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments [70.42705564227548]
  We propose an automated environment construction pipeline for large language models (LLMs)<n>This enables the creation of high-quality training environments that provide detailed and measurable feedback without relying on external tools.<n>We also introduce a verifiable reward mechanism that evaluates both the precision of tool use and the completeness of task execution.
  arXiv  Detail & Related papers  (2025-08-12T09:45:19Z)
• AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs [40.14708895187017]
  We present AnalogCoder-Pro, a multimodal large language model (LLM) framework that integrates generative and optimization techniques.<n>The framework features a multimodal diagnosis-and-repair feedback loop that uses simulation error messages and waveform images to autonomously correct design errors.<n>On a curated benchmark suite covering 13 circuit types, AnalogCoder-Pro successfully designed 28 circuits and consistently outperformed existing LLM-based methods in figures of merit.
  arXiv  Detail & Related papers  (2025-08-04T15:25:48Z)
• GENIAL: Generative Design Space Exploration via Network Inversion for Low Power Algorithmic Logic Units [1.5845117761091052]
  We introduce GENIAL, a machine learning-based framework for the automatic generation and optimization of arithmetic units.<n>We show that GENIAL is consistently more sample efficient than other methods, and converges faster towards optimized designs.<n>We also demonstrate the versatility of our approach by achieving significant improvements on Finite State Machines.
  arXiv  Detail & Related papers  (2025-07-25T06:34:59Z)
• CROP: Circuit Retrieval and Optimization with Parameter Guidance using LLMs [5.611060564629618]
  We present CROP, the first large language model (LLM)-powered automatic VLSI design flow tuning framework.<n>Our approach includes: (1) a scalable methodology for transforming RTL source code into dense vector representations, (2) an embedding-based retrieval system for matching designs with semantically similar circuits, and (3) a retrieval-augmented generation (RAG)-enhanced LLM-guided parameter search system.<n>Experiment results demonstrate CROP's ability to achieve superior quality-of-results (QoR) with fewer iterations than existing approaches on industrial designs, including a 9.9% reduction in power consumption.
  arXiv  Detail & Related papers  (2025-07-02T20:25:47Z)
• AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generation [6.2730802180534155]
  AUTOCIRCUIT-RL is a novel reinforcement learning-based framework for automated analog circuit synthesis.<n>It generates 12% more valid circuits and improves efficiency by 14% compared to the best baselines.<n>It achieves over 60% success in valid circuits with limited training data, demonstrating strong generalization.
  arXiv  Detail & Related papers  (2025-06-03T17:54:30Z)
• Logic Synthesis Optimization with Predictive Self-Supervision via Causal Transformers [19.13500546022262]
  We introduce LSOformer, a novel approach harnessing Autoregressive transformer models and predictive SSL to predict the trajectory of Quality of Results (QoR)<n>LSOformer integrates cross-attention modules to merge insights from circuit graphs and optimization sequences, thereby enhancing prediction accuracy for QoR metrics.
  arXiv  Detail & Related papers  (2024-09-16T18:45:07Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks [82.33619654835348]
  Intelligent surface (IRS) has been employed to reshape the wireless channels by controlling individual scattering elements' phase shifts. Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity. In this article, we focus on machine learning (ML) approaches for performance in IRS-assisted wireless networks.
  arXiv  Detail & Related papers  (2020-08-29T08:39:43Z)

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.