ChipCraftBrain: Validation-First RTL Generation via Multi-Agent Orchestration
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. We present ChipCraftBrain, a framework combining symbolic-neural reasoning with adaptive multi-agent orchestration for automated RTL generation. Four innovations drive the system: (1) adaptive orchestration over six specialized agents via a PPO policy over a 168-dim state (an alter...
| Authors | Cagri Eryilmaz |
|---|---|
| Primary category | cs.AR |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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