{"ID":2892624,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.16844","arxiv_id":"2507.16844","title":"TD-Interpreter: Enhancing the Understanding of Timing Diagrams with Visual-Language Learning","abstract":"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.","short_abstract":"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 v...","url_abs":"https://arxiv.org/abs/2507.16844","url_pdf":"https://arxiv.org/pdf/2507.16844v1","authors":"[\"Jie He\",\"Vincent Theo Willem Kenbeek\",\"Zhantao Yang\",\"Meixun Qu\",\"Ezio Bartocci\",\"Dejan Ničković\",\"Radu Grosu\"]","published":"2025-07-20T14:52:29Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}