AI RESEARCH
ChipCraftBrain: Validation-First RTL Generation via Multi-Agent Orchestration
arXiv CS.LG
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ArXi:2604.19856v1 Announce Type: cross Large Language Models (LLMs) show promise for generating Register-Transfer Level (RTL) code from natural language specifications, but single-shot generation achieves only 60-65% functional correctness on standard benchmarks. Multi-agent approaches such as MAGE reach 95.9% on VerilogEval yet remain untested on harder industrial benchmarks such as NVIDIA's CVDP, lack synthesis awareness, and incur high API costs.