Natural Synthesis: Outperforming Reactive Synthesis Tools with Large Reasoning Models

2026-05-14

AI Summary

Reactive synthesis faces dual challenges of high algorithmic complexity and the difficulty of writing formal specifications. This work proposes a neurosymbolic approach that, for the first time, incorporates natural language specifications into reactive synthesis by leveraging a large reasoning model to generate Verilog circuits and integrating a model checker to provide symbolic feedback for iterative refinement. The method establishes an end-to-end natural synthesis pipeline that outperforms existing specialized tools on benchmarks from the annual synthesis competition. Notably, it achieves performance comparable to hand-crafted formal specifications when using natural language inputs and scales to the synthesis of undecidable parameterized systems.

Abstract

Reactive synthesis, the problem of automatically constructing a hardware circuit from a logical specification, is a long-standing challenge in formal verification. It is elusive for two reasons: It is algorithmically hard, and writing formal specifications by hand is notoriously difficult. In this paper, we tackle both sides of the problem. For the algorithmic side, we present a neuro-symbolic approach to reactive synthesis that couples large reasoning models with model checkers to iteratively repair a synthesized Verilog implementation via sound symbolic feedback. Our approach solves more benchmarks than the best dedicated tools in the annual synthesis competition and extends to constructing parameterized systems, a problem known to be undecidable. On the specification side, we introduce an autoformalization step that shifts the specification task from temporal logic to natural language by introducing a hand-authored dataset of natural-language specifications for evaluation. We demonstrate performance comparable to that of starting from formal specifications, establishing natural synthesis as a viable end-to-end workflow.

Problem

• reactive synthesis
• formal specification
• hardware circuit
• natural language
• algorithmic complexity

Innovation

• neuro-symbolic synthesis
• large reasoning models
• reactive synthesis
• autoformalization
• natural-language specifications