mlsanityData to 7 Oct 2026

ChipCraftBrain: Validation-First RTL Generation via Multi-Agent Orchestration

arXiv 2604.19856 ↗First posted 21 Apr 2026cs.AR cs.AI cs.LGcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

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 alternative world-model MPC planner is also evaluated); (2) a hybrid symbolic-neural architecture that solves K-map and truth-table problems algorithmically while specialized agents handle waveform timing and general RTL; (3) knowledge-augmented generation from a 321-pattern base plus 971 open-source reference implementations with focus-aware retrieval; and (4) hierarchical specification decomposition into dependency-ordered sub-modules with interface synchronization. On VerilogEval-Human, ChipCraftBrain achieves 97.2% mean pass@1 (range 96.15-98.72% across 7 runs, best 154/156), on par with ChipAgents (97.4%, self-reported) and ahead of MAGE (95.9%). On a 302-problem non-agentic subset of CVDP spanning five task categories, we reach 94.7% mean pass@1 (286/302, averaged over 3 runs), a 36-60 percentage-point lift per category over the published single-shot baseline; we additionally lead three of four categories shared with NVIDIA's ACE-RTL despite using roughly 30x fewer per-problem attempts. A RISC-V SoC case study demonstrates hierarchical decomposition generating 8/8 lint-passing modules (689 LOC) validated on FPGA, where monolithic generation fails entirely.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 17 of its 34 references that are in the map. First posted on arXiv 21 Apr 2026.

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17 of 34 references.

Closest ancestors · 4

Same direction, newest first

  1. 2026ACE-RTL: When Agentic Context Evolution Meets RTL-Specialized LLMs10 Feb
  2. 2025Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verification17 Jun
  3. 2025VFlow: Discovering Optimal Agentic Workflows for Verilog Generation30 Mar
  4. 2024VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Tool15 Aug

Other references · 13

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2022Competition-Level Code Generation with AlphaCodecited by 1,427
  3. 2022CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learningcited by 342
  4. 2023VerilogEval: Evaluating Large Language Models for Verilog Code Generationcited by 246
  5. 2025Mercury: Ultra-Fast Language Models Based on Diffusioncited by 225
  6. 2023VeriGen: A Large Language Model for Verilog Code Generationcited by 197
  7. 2023ChipNeMo: Domain-Adapted LLMs for Chip Designcited by 168
  8. 2023Chip-Chat: Challenges and Opportunities in Conversational Hardware Designcited by 136
  9. 2023AutoChip: Automating HDL Generation Using LLM Feedbackcited by 80
  10. 2024OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflectioncited by 56
  11. 2024CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repaircited by 41
  12. 2025ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generationcited by 22
1 more
  1. 2026ChipBench: A Next-Step Benchmark for Evaluating LLM Performance in AI-Aided Chip Designcited by 7

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  1. 2024MAGE: A Multi-Agent Engine for Automated RTL Code Generation
  2. 2026VeriAgent: A Tool-Integrated Multi-Agent System with Evolving Memory for PPA-Aware RTL Code Generation
  3. 2024EDA-Aware RTL Generation with Large Language Models
  4. 2025PEFA-AI: Advancing Open-source LLMs for RTL generation using Progressive Error Feedback Agentic-AI
  5. 2026ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation
  6. 2025PRO-V-R1: Reasoning Enhanced Programming Agent for RTL Verification
  7. 2026VeriGraphi: A Multi-Agent Framework of Hierarchical RTL Generation for Large Hardware Designs
  8. 2025Automated Multi-Agent Workflows for RTL Design