SizingAgent: Incentivizing Physics-Grounded Reasoning in LLM-Based Analog Circuit Sizing

Large Language Models (LLMs) are increasingly adapted for analog circuit sizing, yet existing methods often treat them as direct parameter generators and defer all validation to SPICE. We observe that this leads to many risky parameter updates that sound plausible in language but contradict the local physical response of the circuit. In this paper, we introduce SizingAgent, a training-free LLM framework that empowers LLM agents with physics-grounded reasoning for sizing optimization in analog design automation. Instead of relying on the LLM to directly generate numerical design parameters, SizingAgent structures optimization as a verifiable sizing loop consisting of three modules: (1) Physics Initializer (PI), which extracts small-signal equations (S-Equations) from the netlist to provide circuit-specific physical priors; (2) Physics-Guided Reasoner (PGR), which analyzes specification gaps and derives targeted sizing updates through a physics-guided reasoning chain; and (3) Sizing Verifier (SV), which checks the physical consistency of the proposed updates using a sensitivity matrix. We further release, to the best of our knowledge, the largest open-source benchmark for LLM-based analog circuit sizing. Under a shared Gemini 2.5 Flash backbone, SizingAgent achieves 74.8% Pass@1, surpassing the strongest LLM baseline by 32.8 percentage points and dramatically outperforming classical optimizers, whose Pass@1 reaches only 16.6% for Genetic Algorithms and 26.2% for Bayesian Optimization. Replacing the backbone with GPT-5.5 further improves performance to 93.1% Pass@1. The framework also generalizes across LLM families, with commodity backbones achieving 69.6–76.1% Pass@1 and Qwen3-32B improving from 10.1% to 59.1% over zero-shot prompting. Code and benchmark are available at https://github.com/artifact-repro/sizingagent.