# TD-Interpreter: Enhancing the Understanding of Timing Diagrams with Visual-Language Learning

arXiv: https://arxiv.org/abs/2507.16844 · HTML: https://mlsanity.com/paper/2507.16844/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2025.

## Abstract (arXiv)

We introduce TD-Interpreter, a specialized ML tool that assists engineers in understanding complex timing diagrams (TDs), originating from a third party, during their design and verification process. TD-Interpreter is a visual question-answer environment which allows engineers to input a set of TDs and ask design and verification queries regarding these TDs. We implemented TD-Interpreter with multimodal learning by fine-tuning LLaVA, a lightweight 7B Multimodal Large Language Model (MLLM). To address limited training data availability, we developed a synthetic data generation workflow that aligns visual information with its textual interpretation. Our experimental evaluation demonstrates the usefulness of TD-Interpreter which outperformed untuned GPT-4o by a large margin on the evaluated benchmarks.