mlsanityData to 14 Oct 2026

VLM-CAD: VLM-Optimized Collaborative Agent Design Workflow for Analog Circuit Sizing

arXiv 2601.07315 ↗First posted 12 Jan 2026cs.MA cs.AI cs.ARcited by 0

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

also in 2 more
Abstract (arXiv)

Vision Language Models (VLMs) have demonstrated remarkable potential in multimodal reasoning. However, they can have spatial blindness and logical hallucinations when interpreting densely structured engineering content, such as analog circuit schematics. To address these challenges, we propose a Vision Language Model-Optimized Collaborative Agent Design Workflow for Analog Circuit Sizing (VLM-CAD) designed to support step-by-step reasoning over multimodal evidence. VLM-CAD bridges the modality gap by integrating a neuro-symbolic structural parsing module, Image2Net, which transforms raw pixels into explicit topological graphs and structured JSON representations to anchor VLM interpretation in deterministic facts. To ensure the reliability required for engineering decisions, we further propose ExTuRBO, an Explainable Trust Region Bayesian Optimization method. ExTuRBO employs agent-generated semantic seeds to warm-start local searches and uses Automatic Relevance Determination to provide sensitivity evidence for the final design report. Experimental results on 12 sizing tasks covering six circuits and four technology platforms show that VLM-CAD achieves a pooled Strict Pass@1 of 23.3% and a Relaxed Pass@1 of 91.7%, while providing sensitivity evidence for final design reports.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 19 of its 50 references that are in the map. First posted on arXiv 12 Jan 2026.

Builds on

19 of 50 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025AnaFlow: Agentic LLM-based Workflow for Reasoning-Driven Explainable and Sample-Efficient Analog Circuit Sizing5 Nov
  2. 2025CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs11 Feb

Other references · 17

  1. 2025Qwen3 Technical Reportcited by 8,820
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  3. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 374
  4. 2024Vision language models are blind: Failing to translate detailed visual features into wordscited by 147
  5. 2023On the Robustness of Large Multimodal Models Against Image Adversarial Attackscited by 92
  6. 2025HalluLens: LLM Hallucination Benchmarkcited by 92
  7. 2025Vision Language Models are Biasedcited by 66
  8. 2020AutoCkt: Deep Reinforcement Learning of Analog Circuit Designscited by 51
  9. 2024Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Spatial Reasoningcited by 34
  10. 2024AmpAgent: An LLM-based Multi-Agent System for Multi-stage Amplifier Schematic Design from Literature for Process and Performance Portingcited by 29
  11. 2024AMSNet: Netlist Dataset for AMS Circuitscited by 25
  12. 2024LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuitscited by 22
5 more
  1. 2025EEsizer: LLM-Based AI Agent for Sizing of Analog and Mixed Signal Circuitcited by 13
  2. 2025MAPS: Advancing Multi-Modal Reasoning in Expert-Level Physical Sciencecited by 10
  3. 2024AnalogGym: An Open and Practical Testing Suite for Analog Circuit Synthesiscited by 9
  4. 2025ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Chart Understandingcited by 5
  5. 2025Emerging ML-AI Techniques for Analog and RF EDAcited by 1

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Close in wording, no citation either way

  1. 2026AgenticSizing: A Large Language Model-based Multi-Agent Framework for Analog Circuit Sizing
  2. 2025A Large Language Model-based Multi-Agent Framework for Analog Circuits' Sizing Relationships Extraction
  3. 2026AaLLM: An End-to-End Analog Circuit Design Framework from Topology Generation to Sizing Using Large Language Models
  4. 2026CircuitLM: A Multi-Agent LLM-Aided Design Framework for Generating Circuit Schematics from Natural Language Prompts
  5. 2025EEschematic: Multimodal-LLM Based AI Agent for Schematic Generation of Analog Circuit
  6. 2026AnalogRetriever: Learning Cross-Modal Representations for Analog Circuit Retrieval
  7. 2026AnalogAgent: Self-Improving Analog Circuit Design Automation with LLM Agents
  8. 2025LLM-USO: Large Language Model-based Universal Sizing Optimizer