Domain-specialized LangGraph agents close hardware coverage faster and with far fewer tokens than a general Codex agent, while a hole taxonomy exposes hard limits of pure LLM stimulus generation.
GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models
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Spec2Cov uses an LLM agent in a feedback loop with a hardware simulator to generate tests from specs, achieving 100% coverage on simple designs and up to 49% on complex ones across 26 benchmarks.
citing papers explorer
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Understanding Inference-Time Token Allocation and Coverage Limits in Agentic Hardware Verification
Domain-specialized LangGraph agents close hardware coverage faster and with far fewer tokens than a general Codex agent, while a hole taxonomy exposes hard limits of pure LLM stimulus generation.
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Spec2Cov: An Agentic Framework for Code Coverage Closure of Digital Hardware Designs
Spec2Cov uses an LLM agent in a feedback loop with a hardware simulator to generate tests from specs, achieving 100% coverage on simple designs and up to 49% on complex ones across 26 benchmarks.