ReCoDe: Re-Conditioned Decoding for Generative Logic Synthesis

Generative neural models are a promising approach to logic synthesis, yet on difficult sub-circuits they still lag behind strong classical optimizers such as &deepsyn. How best to spend inference-time compute on these models is an open question: existing search, typically Monte Carlo tree search over decoder tokens, runs from a single fixed encoding of the input, and so can only refine the circuits that one representation makes likely. We show that re-encoding a circuit that is functionally identical but structurally different yields different outputs. Re-Conditioned Decoding (ReCoDe) exploits this by iteratively re-encoding a model's own intermediate outputs as new decoder conditioning, with a population parallelizing the search. We evaluate on several thousand IWLS fanout-free windows at K=8, 10 and 12 inputs, the latter two larger than prior neural synthesis has handled. Under exact equivalence, ReCoDe reduces the size of the hardest windows by 16–22% relative to a single decode at every width, outperforms token-level search given ten times the budget, and is the only neural method to surpass &deepsyn.