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CktFormalizer: Autoformalization of Natural Language into Circuit Representations

arXiv 2605.07782 ↗First posted 8 May 2026cs.CL cs.PLcited by 0

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Abstract (arXiv)

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/

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 43 references that are in the map. First posted on arXiv 8 May 2026.

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References · 17

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  3. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  4. 2023Code Llama: Open Foundation Models for Codecited by 2,105
  5. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,628
  6. 2024StarCoder 2 and The Stack v2: The Next Generationcited by 484
  7. 2023LeanDojo: Theorem Proving with Retrieval-Augmented Language Modelscited by 414
  8. 2020Generative Language Modeling for Automated Theorem Provingcited by 379
  9. 2023VerilogEval: Evaluating Large Language Models for Verilog Code Generationcited by 246
  10. 2023VeriGen: A Large Language Model for Verilog Code Generationcited by 197
  11. 2023RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Modelcited by 143
  12. 2023Chip-Chat: Challenges and Opportunities in Conversational Hardware Designcited by 136
5 more
  1. 2022Benchmarking Large Language Models for Automated Verilog RTL Code Generationcited by 112
  2. 2023ChipGPT: How far are we from natural language hardware designcited by 101
  3. 2020DAVE: Deriving Automatically Verilog from Englishcited by 46
  4. 2025Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verificationcited by 41
  5. 2024CodeV: Empowering LLMs with HDL Generation through Multi-Level Summarizationcited by 38

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