Simulation-Aware In-Context Policy Improvement for LLM-Aided Analog Layout Refinement

• URL: http://arxiv.org/abs/2608.13767v1
• Date: Thu, 13 Aug 2026 20:46:17 GMT
• Title: Simulation-Aware In-Context Policy Improvement for LLM-Aided Analog Layout Refinement
• Abstract summary: Large Language Models (LLMs) have demonstrated potential in improving the sample efficiency of such simulation-driven tuning.<n>We propose a simulation-aware LLM multi-agent framework that performs in-context policy improvement (ICPI) by iteratively updating layout optimization parameters exposed by an analog layout generator.
• Score: 6.977622121664024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Analog IC layout design remains a labor-intensive iterative process dominated by simulation-driven refinement. Although end-to-end layout generators accelerate initial placement and routing, they still require experts to manually tune layout optimization parameters with repeated post-layout simulations for stringent design specifications. While Bayesian Optimization (BO) is widely adopted for parameter tuning in analog IC design, at the layout level it typically requires hundreds to thousands of evaluations, each involving costly parasitic extraction and post-layout simulation, which makes it impractical. Recently, Large Language Models (LLMs) have demonstrated potential in improving the sample efficiency of such simulation-driven tuning. However, their restricted access to geometric layout context and design-specific heuristics limits their ability to manipulate the layout optimization process. In this paper, we propose a simulation-aware LLM multi-agent framework that performs in-context policy improvement (ICPI) by iteratively updating layout optimization parameters exposed by an analog layout generator through an act-observe-reflect loop on compact structured layout representations. Experiments on real-world analog circuits show that, with only tens of post-layout simulations, our approach improves post-layout performance over the generator's built-in heuristics and BO-based tuning method.

Related papers

• Tuning Quantum MPS [0.0]
  Matrix Product State (MPS) methods are among the most effective approaches for the classical simulation of quantum circuits.<n>We propose a two-stage framework for automatic hyper parameter selection for quantum MPS simulation.<n>We evaluate the approach on multiple scalable circuit families using leave-one-family-out and size-based validation.
  arXiv  Detail & Related papers  (2026-06-22T12:44:50Z)
• New Hybrid Fine-Tuning Paradigm for LLMs: Algorithm Design and Convergence Analysis Framework [52.69678300445233]
  Fine-tuning Large Language Models (LLMs) typically involves either full fine-tuning, which updates all model parameters, or.<n>Efficient Fine-Tuning (PEFT) adjusts a small subset of parameters.<n>We propose a novel hybrid fine-tuning approach that jointly updates both LLMs and PEFT modules using a combination of zeroth-order and first-order optimization methods.
  arXiv  Detail & Related papers  (2026-04-10T22:39:38Z)
• AutoSizer: Automatic Sizing of Analog and Mixed-Signal Circuits via Large Language Model (LLM) Agents [4.28109763423665]
  AutoSizer is a meta-optimization framework that unifies circuit understanding, adaptive search-space construction, and optimization orchestration in a closed loop.<n>It experimentally achieves higher solution quality, faster convergence, and higher success rate across varying circuit difficulties.
  arXiv  Detail & Related papers  (2026-02-02T21:51:55Z)
• TIDE: Tuning-Integrated Dynamic Evolution for LLM-Based Automated Heuristic Design [7.264986493460248]
  TIDE is a Tuning-Integrated Dynamic Evolution framework designed to decouple structural reasoning from parameter optimization.<n> experiments across nine optimization problems demonstrate that TIDE significantly outperforms state-of-the-art tuning methods.
  arXiv  Detail & Related papers  (2026-01-29T04:00:02Z)
• 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)
• Leveraging Importance Sampling to Detach Alignment Modules from Large Language Models [48.15777554876988]
  Traditional alignment methods often require retraining large pretrained models.<n>We propose a novel textitResidual Alignment Model (textitRAM) that formalizes the alignment process as a type of importance sampling.<n>We develop a resampling algorithm with iterative token-level decoding to address the common first-token latency issue in comparable methods.
  arXiv  Detail & Related papers  (2025-05-26T08:53:02Z)
• Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning [4.684022970694239]
  We propose an objective-driven, multi-level, multi-agent Q-learning framework to explore unconventional design space of analog layout.<n>Our approach achieves better variation performance than the state-of-the-art layout techniques.
  arXiv  Detail & Related papers  (2025-03-29T03:13:56Z)
• A Modular Framework for Reinforcement Learning Optimal Execution [68.8204255655161]
  We develop a modular framework for the application of Reinforcement Learning to the problem of Optimal Trade Execution. The framework is designed with flexibility in mind, in order to ease the implementation of different simulation setups.
  arXiv  Detail & Related papers  (2022-08-11T09:40:42Z)
• Proximal Policy Optimization-based Transmit Beamforming and Phase-shift Design in an IRS-aided ISAC System for the THz Band [90.45915557253385]
  IRS-aided integrated sensing and communications (ISAC) system operating in the terahertz (THz) band is proposed to maximize the system capacity. Transmit beamforming and phase-shift design are transformed into a universal optimization problem with ergodic constraints.
  arXiv  Detail & Related papers  (2022-03-21T09:15:18Z)
• An AI-Assisted Design Method for Topology Optimization Without Pre-Optimized Training Data [68.8204255655161]
  An AI-assisted design method based on topology optimization is presented, which is able to obtain optimized designs in a direct way. Designs are provided by an artificial neural network, the predictor, on the basis of boundary conditions and degree of filling as input data.
  arXiv  Detail & Related papers  (2020-12-11T14:33:27Z)
• Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering [53.523517926927894]
  We explore the use of exact per-sample Hessian-vector products and gradients to construct self-tuning quadratics. We prove that our model-based procedure converges in noisy gradient setting. This is an interesting step for constructing self-tuning quadratics.
  arXiv  Detail & Related papers  (2020-11-09T22:07:30Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)

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.