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

ArXi:2512.03053v2 Announce Type: replace-cross We show for invertible problems that transform data from a source domain (for example, Logic Condition Tables (LCTs)) to a destination domain (for example, Hardware Description Language (HDL) code), an approach of using Large Language Models (LLMs) as a lossless encoder from source to destination followed by as a lossless decoder back to the source, comparable to lossless compression in information theory, can mitigate most of the LLM drawbacks of hallucinations and omissions.