WaveformQA: Benchmarking LLM Temporal Reasoning on Digital Waveforms
Large Language Models (LLMs) have demonstrated strong capabilities in code generation and reasoning, yet their ability to perform temporal reasoning over digital waveform data remains largely unexplored. Although reasoning over digital waveforms is a critical bottleneck in design verification, existing benchmarks primarily evaluate hardware description language (HDL) code generation and use waveforms only as supplementary context. This paper presents WaveformQA, an open-source question-answering benchmark for evaluating LLM temporal reasoning over digital waveforms. The benchmark comprises 360 questions with programmatically generated ground truths across eight categories of varying diffi...
| Authors | Yichuan Liu, Daniel Cummings, Nick Vadlamudi |
|---|---|
| Primary category | cs.AI |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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