# VeriMoA: A Mixture-of-Agents Framework for Spec-to-HDL Generation

arXiv: https://arxiv.org/abs/2510.27617 · HTML: https://mlsanity.com/paper/2510.27617/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 31 of its 43 references that are in the map.
- First posted on arXiv 31 Oct 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 30 of 31)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion](https://mlsanity.com/paper/2306.02561/) (2023)
- [Mixture-of-Agents Enhances Large Language Model Capabilities](https://mlsanity.com/paper/2406.04692/) (2024)
- [ChipNeMo: Domain-Adapted LLMs for Chip Design](https://mlsanity.com/paper/2311.00176/) (2023)
- [RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model](https://mlsanity.com/paper/2308.05345/) (2023)
- [AutoChip: Automating HDL Generation Using LLM Feedback](https://mlsanity.com/paper/2311.04887/) (2023)
- [OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection](https://mlsanity.com/paper/2407.16237/) (2024)
- [Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?](https://mlsanity.com/paper/2502.00674/) (2025)
- [MG-Verilog: Multi-grained Dataset Towards Enhanced LLM-assisted Verilog Generation](https://mlsanity.com/paper/2407.01910/) (2024)
- [Revisiting VerilogEval: A Year of Improvements in Large-Language Models for Hardware Code Generation](https://mlsanity.com/paper/2408.11053/) (2024)
- [AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs](https://mlsanity.com/paper/2407.18333/) (2024)
- [MAGE: A Multi-Agent Engine for Automated RTL Code Generation](https://mlsanity.com/paper/2412.07822/) (2024)
- [OpenLLM-RTL: Open Dataset and Benchmark for LLM-Aided Design RTL Generation](https://mlsanity.com/paper/2503.15112/) (2025)
- [HaVen: Hallucination-Mitigated LLM for Verilog Code Generation Aligned with HDL Engineers](https://mlsanity.com/paper/2501.04908/) (2025)
- [ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generation](https://mlsanity.com/paper/2506.05566/) (2025)
- [PyraNet: A Multi-Layered Hierarchical Dataset for Verilog](https://mlsanity.com/paper/2412.06947/) (2024)
- [Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models](https://mlsanity.com/paper/2407.03889/) (2024)
- [HDLCoRe: A Training-Free Framework for Mitigating Hallucinations in LLM-Generated HDL](https://mlsanity.com/paper/2503.16528/) (2025)
- [Large Language Model for Verilog Generation with Code-Structure-Guided Reinforcement Learning](https://mlsanity.com/paper/2407.18271/) (2024)
- [VeriThoughts: Enabling Automated Verilog Code Generation using Reasoning and Formal Verification](https://mlsanity.com/paper/2505.20302/) (2025)
- [ReasoningV: Efficient Verilog Code Generation with Adaptive Hybrid Reasoning Model](https://mlsanity.com/paper/2504.14560/) (2025)
- [CoopetitiveV: Leveraging LLM-powered Coopetitive Multi-Agent Prompting for High-quality Verilog Generation](https://mlsanity.com/paper/2412.11014/) (2024)
- [Paradigm-Based Automatic HDL Code Generation Using LLMs](https://mlsanity.com/paper/2501.12702/) (2025)
- [Abstractions-of-Thought: Intermediate Representations for LLM Reasoning in Hardware Design](https://mlsanity.com/paper/2505.15873/) (2025)
- [Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalance](https://mlsanity.com/paper/2501.11849/) (2025)
- [Classification-Based Automatic HDL Code Generation Using LLMs](https://mlsanity.com/paper/2407.18326/) (2024)
- [Automated Multi-Agent Workflows for RTL Design](https://mlsanity.com/paper/2509.20182/) (2025)

## Built on by (most cited first, 15 of 15)

- [Auditing Multi-Agent LLM Reasoning Trees Outperforms Majority Vote and LLM-as-Judge](https://mlsanity.com/paper/2602.09341/) (2026)
- [TemporalBench: A Benchmark for Evaluating LLM-Based Agents on Contextual and Event-Informed Time Series Tasks](https://mlsanity.com/paper/2602.13272/) (2026)
- [Maestro: Learning to Collaborate via Conditional Listwise Policy Optimization for Multi-Agent LLMs](https://mlsanity.com/paper/2511.06134/) (2025)
- [Defenses Against Prompt Attacks Learn Surface Heuristics](https://mlsanity.com/paper/2601.07185/) (2026)
- [POET: Power-Oriented Evolutionary Tuning for LLM-Based RTL PPA Optimization](https://mlsanity.com/paper/2603.19333/) (2026)
- [Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual Learning](https://mlsanity.com/paper/2603.07972/) (2026)
- [RaMem: Contextual Reinstatement for Long-term Agentic Memory](https://mlsanity.com/paper/2606.22844/) (2026)
- [Attention-MoA: Enhancing Mixture-of-Agents via Inter-Agent Semantic Attention and Deep Residual Synthesis](https://mlsanity.com/paper/2601.16596/) (2026)
- [COEVO: Co-Evolutionary Framework for Joint Functional Correctness and PPA Optimization in LLM-Based RTL Generation](https://mlsanity.com/paper/2604.15001/) (2026)
- [ReM-MoA: Reasoning Memory Sustains Mixture-of-Agents Scaling](https://mlsanity.com/paper/2606.24437/) (2026)
- [Architectural Implications of Agentic AI Workflows](https://mlsanity.com/paper/2608.04458/) (2026)
- [Evidence-Driven LLM Agent for C-to-Synthesizable-C Conversion and Verification](https://mlsanity.com/paper/2606.28409/) (2026)
- [SynCo: Data Synthesis Co-Training for Self-Evolving LLMs via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2610.11345/) (2026)
- [TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation](https://mlsanity.com/paper/2604.26247/) (2026)
- [Beyond Final Accuracy: Auditing Communication in LLM Multi-Agent Systems](https://mlsanity.com/paper/2610.01042/) (2026)

## Abstract (arXiv)

Automation of Register Transfer Level (RTL) design can help developers meet increasing computational demands. Large Language Models (LLMs) show promise for Hardware Description Language (HDL) generation, but face challenges due to limited parametric knowledge and domain-specific constraints. While prompt engineering and fine-tuning have limitations in knowledge coverage and training costs, multi-agent architectures offer a training-free paradigm to enhance reasoning through collaborative generation. However, current multi-agent approaches suffer from two critical deficiencies: susceptibility to noise propagation and constrained reasoning space exploration. We propose VeriMoA, a training-free mixture-of-agents (MoA) framework with two synergistic innovations. First, a quality-guided caching mechanism to maintain all intermediate HDL outputs and enables quality-based ranking and selection across the entire generation process, encouraging knowledge accumulation over layers of reasoning. Second, a multi-path generation strategy that leverages C++ and Python as intermediate representations, decomposing specification-to-HDL translation into two-stage processes that exploit LLM fluency in high-resource languages while promoting solution diversity. Comprehensive experiments on VerilogEval 2.0 and RTLLM 2.0 benchmarks demonstrate that VeriMoA achieves 15--30% improvements in Pass@1 across diverse LLM backbones, especially enabling smaller models to match larger models and fine-tuned alternatives without requiring costly training.