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#natural language processing Preprint Open access

CktFormalizer: Autoformalization of Natural Language into Circuit Representations

Jing Xiong Qi Han Chenchen Ding He Xiao Zunhai Su Chaofan Tao Xiachong Feng Ngai Wong
Oct 2026
Natural Language Processing

Abstract

Hardware infrastructure is a critical bottleneck for LLM-driven circuit design, limiting what agents can express, compile, and iteratively refine within an agentic loop. To address this bottleneck, we introduce CKTLEAN, a typed hardware infrastructure embedded in Lean. It supports hardware description, compilation to SystemVerilog, and interactive type-checking and proof feedback through a persistent read-eval-print loop (REPL). On this foundation, we build CKTFORMALIZER, an agent framework for hardware generation, repair, optimization, and source-level equivalence proving. We evaluate structural correctness through compilation, functional correctness through RTL and gate-level simulation, and formal correctness through proofs relative to stated specifications. Across VerilogEval, RTLLM, ResBench, and CVDP, CKTFORMALIZER with CKTLEAN achieves compilation rates of 91.1%-99.4%. Among designs that pass RTL simulation, 95.4%-100.0% jointly complete synthesis and place-and-route and pass design-rule and layout-versus-schematic checks. In a separate evaluation on 30 VerilogEval problems, interactive proof-state feedback raises kernel-accepted equivalence proof completion from 53.3% to 63.3%. Hardware evaluation feedback also guides architecture exploration and iterative power, performance, and area (PPA) optimization, with synthesis-area reductions of up to 58.6% in the optimization loop. These results suggest that typed representations and explicit proof-state feedback help structure the agentic loop: compiler diagnostics guide targeted repairs, while proof-state feedback helps agents identify what remains to be proved and determine the next proof step. Project Page: https://ckt-formalizer.github.io/

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