WaveformQA: Benchmarking LLM Temporal Reasoning on Digital Waveforms

2019-01-29 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Electronic Design Automation · Depth: Expert, extended

Summary

WaveformQA is an open-source question-answering benchmark designed to evaluate Large Language Model (LLM) temporal reasoning over digital waveform data, a critical bottleneck in hardware design verification. The benchmark features 360 programmatically generated questions across eight categories and 24 subcategories, derived from real hardware traces of five open-source RISC-V processor implementations. It uses 13 VCD traces ranging from 338 to 4,185 signals, organized into 15 complexity bins based on signal and transition counts (5,000 to 60,000 transitions). Evaluation of frontier LLMs, including Claude Sonnet 4.6 and Gemini 2.5 Pro, revealed that while models perform reasonably on simple queries, accuracy degrades with context window limitations and complex temporal or multi-step questions. Notably, an event-time JSON representation of waveforms improved LLM reasoning accuracy by 37-53% compared to the VCD format, despite a 15-30% token overhead. Performance also declined 8-12% as transition counts increased from 5k to 30k.

Key takeaway

For Machine Learning Engineers developing AI assistants for hardware verification, you should prioritize structured event-time JSON for waveform data input, as it significantly boosts LLM reasoning accuracy despite higher token consumption. When selecting LLMs, opt for models with larger context windows to handle complex traces. Focus your development efforts on enhancing LLM capabilities for temporal ordering and multi-signal identification, as these remain critical challenges for current models.

Key insights

LLMs struggle with precise temporal reasoning on digital waveforms, but structured JSON improves accuracy significantly.

Principles

• Context window size is a primary performance driver.
• Longer temporal sequences degrade reasoning performance.
• Event sequencing and multi-signal identification are hardest.

Method

WaveformQA generates questions by assigning signal types, sampling time points, computing ground truth, and instantiating natural-language questions via Python f-strings.

In practice

• Use event-time JSON for waveform data input to LLMs.
• Prioritize LLMs with larger context windows for waveform analysis.
• Focus on improving LLM capabilities for temporal ordering tasks.

Topics

• LLM Temporal Reasoning
• Digital Waveform Analysis
• Hardware Verification
• Electronic Design Automation
• WaveformQA Benchmark
• JSON Data Format

Code references

• tenstorrent/waveformqa
• darklife/darkriscv
• gtkwave/gtkwave
• olofk/serv
• lowRISC/ibex

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.