Mitigating hallucinations and omissions in LLMs for invertible problems: An application to hardware logic design automation

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

A new method leverages Large Language Models (LLMs) as lossless encoders and decoders for invertible problems, specifically applied to hardware logic design automation. This approach mitigates LLM hallucinations and omissions by transforming Logic Condition Tables (LCTs) into Hardware Description Language (HDL) code and then reconstructing the LCTs from the generated HDL. By comparing the original and reconstructed LCTs, the system can detect errors in LLM generation and even identify subtle design specification flaws. The method was tested on a 2D Network-on-Chip (NoC) router design, comprising 13 units and 1500-2000 lines of code, using seven different off-the-shelf LLMs. Results show significant productivity improvements, with two LLMs achieving 100% success and others requiring minimal fixes, demonstrating the viability of this closed-loop verification for complex hardware designs.

Key takeaway

For research scientists developing automated hardware design tools, this invertible LLM-based design flow offers a robust method to detect and correct LLM-generated logic errors. You should consider integrating this encoder-decoder verification loop into your workflows to enhance design correctness and identify specification ambiguities, thereby accelerating development and verification cycles for complex logic designs.

Key insights

An invertible LLM-based encoder-decoder loop verifies hardware logic designs, mitigating hallucinations and omissions.

Principles

• Invertible problems allow round-trip verification.
• Input specification serves as ground truth.
• Modular design improves LLM generation success.

Method

Specify logic using LCTs, apply LLM to generate HDL, apply LLM inverse transform to reconstruct LCTs, then compare original and reconstructed LCTs to identify errors.

In practice

• Use LCTs for precise hardware logic specification.
• Break down complex designs into smaller LCTs.
• Employ different LLMs for forward and inverse transforms.

Topics

• Invertible LLM Transform
• Hardware Logic Design Automation
• Logic Condition Tables
• Hardware Description Language
• LLM Hallucination Mitigation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.