MultModLM: A multi-modal benchmark for Large-Language Model based hardware schematic generation
Recently, Large Language models (LLMs) find application in several fields. This extends to hardware definition and synthesis. However, most works at the intersection of LLMs and hardware generation focus on text-based tasks, creating a gap for multi-modal LLMs for RTL design. In this work, we introduce MultModLM, a benchmark for evaluating LLMs on the task of generating hardware schematics from RTL (Register Transfer Level) descriptions. The dataset consists of 99 diverse RTL modules spanning arithmetic, control, and state-based designs. To address the challenges of non-unique schematic representations, we propose a multi-stage evaluation framework combining rubric-based scoring, self-evaluation, cross-model assessment, blind evaluation, and human validation to enable exhaustive evaluation. Through experiments on state-of-the-art LLMs, we observe that while models can generate visually interpretable schematics, their functional correctness remains constrained. Furthermore, we find that LLM-based evaluators exhibit near-zero agreement with human raters, revealing, as a key finding, that LLM-as-a-judge paradigms are unreliable in structurally precise domains. These findings suggest that reliable evaluation of multi-modal hardware outputs remains an open challenge, motivating the need for more robust and domain-aware evaluation methodologies, as well as tools for structural evaluation, so as to enable formal equivalence checkers.