GLASS: A Glass-Box, Loop-Verified Method for Analog Circuit Sizing with Symbolic Priors

Agentic analog circuit sizing is now practical, but each candidate design requires an expensive SPICE simulation, and unguided search carries little explicit design knowledge. In controlled comparisons, the failures we observe stem from what we term a cold-start trap: the agent exhausts its simulation budget in electrically degenerate regimes. We propose GLASS, a glass-box, loop-verified method that resolves this trap at initialization and preserves search momentum through stall recovery and numerical warm-starting. GLASS comprises: 1) SIZE-90K, a dataset of 90,858 sizing simulations from six amplifier topologies across four technology platforms; 2) Symbolic-Op, a calibrated behavioral symbolic layer that converts solver-derived candidates into injectable priors and audits the initial prior by re-simulation in the target testbench; and 3) Multi-Task Surrogate, a compact neural network that predicts six specification metrics and screens proposals by predicted cost. On 12 sizing tasks, GLASS reaches 60.0% strict Pass@1 and 81.7% Pass@3, surpassing the strongest evaluated baseline by 36.7 percentage points with same-topology calibration and surrogate training. Under matched proposal budgets on a single topology, the priors raise the share of search chains that meet specification from 26.3% to 56.0% and reduce the median evaluations required from 258 to 148. Across ablations, degenerate designs fall from 29/60 without symbolic guidance to 0/60 under the full operator set. We release our code at https://anonymous.4open.science/r/GLASS-analog-sizing-86CF