Multi-LLM RTL Generation Problem

• Multi-LLM RTL Generation Problem is a research area focused on orchestrating diverse LLM agents to generate correct, synthesizable RTL code from high-level descriptions.
• Agentic and ensemble architectures utilize specialized generation, review, and verification agents to boost pass rates and minimize manual intervention.
• Challenges such as achieving cycle-accurate semantics, mitigating error propagation, and balancing cost-performance drive the development of adaptive, evolutionary correction strategies.

The Multi-LLM RTL Generation Problem centers on orchestrating multiple LLMs, often organized as specialized agents, to produce functionally correct, synthesizable, and verification-ready register-transfer level (RTL) code from high-level specifications. While single-LLM zero-shot prompting yields syntactically plausible outputs, it frequently fails to achieve utility-grade correctness, as RTL design demands cycle-accurate semantics, precise logic, and strict adherence to design and testbench constraints. Recent research converges on agentic architectures, multi-agent prompt systems, ensemble dispatching, and evolutionary strategies to coordinate diverse models or model instances, extracting complementary strengths and mitigating sources of error propagation, degeneration, and model-specific bias.

1. Formal Problem Definition and Motivation

The Multi-LLM RTL Generation Problem can be formalized as follows. Given a set of RTL tasks $T = \{t_1, t_2, \ldots, t_{|T|}\}$ (each specifying logic in natural language and/or port interface) and a set of LLMs $M = \{m_1, m_2, \ldots, m_{|M|}\}$, design an orchestration—typically an agentic or ensemble protocol—that, for each task, produces an RTL module $r$ such that

• it is synthesizable (compiles),
• it passes functional simulation against a generated or gold-standard testbench,
• it adheres to secondary metrics such as PPA (power, performance, area).

The objective is to maximize aggregate pass rates (e.g., pass@k), maintain or raise design quality with minimal manual intervention, and—optionally—control cost by efficiently allocating queries across commercial and open-source models (Islam et al., 21 Nov 2024, Zhao et al., 10 Dec 2024, Wang et al., 27 Nov 2025).

Key challenges that drive the multi-LLM design include:

• The semantic gap between loosely-structured specs and cycle-level RTL code;
• Diverging model strengths (e.g., language reasoning vs. hardware detail);
• Propagation of errors or degenerate corrections in monolithic or naive multi-agent flows;
• Cost–performance tradeoffs across model APIs (Mi et al., 15 Dec 2024, Zhao et al., 10 Dec 2024, Wang et al., 27 Nov 2025).

2. Architectural Patterns: Agentic and Ensemble Schemes

State-of-the-art frameworks employ agentic or pipeline architectures where distinct LLM agents are assigned specialized roles:

• Generation Agents produce candidate RTL code, often accompanied by a testbench.
• Review/Syntax Agents analyze compiler or simulation logs, parse errors, and generate corrective prompts.
• Verification/Simulation Agents run simulation against the testbench, extract functional errors, and issue targeted repair prompts.
• Meta-agents or dispatchers route subtasks to the agent (LLM) best aligned with the subproblem, sometimes employing ensemble or voting mechanisms (Islam et al., 21 Nov 2024, Mi et al., 15 Dec 2024, Wang et al., 27 Nov 2025, Zhao et al., 10 Dec 2024).

In the AIvril2 system, for instance, Code, Review, and Verification Agents interact via structured prompts; each agent can be powered by different LLMs (e.g., GPT-4o, Claude 3.5, Llama3), and ensembles (via voting or confidence-based fusion) are permissible at each agent (Islam et al., 21 Nov 2024). MAGE expands this to multi-agent feedback loops with explicit testbench generation, high-temperature candidate sampling, and targeted debugging via state checkpointing (Zhao et al., 10 Dec 2024).

CoopetitiveV introduces parallel "learner" agents competing on code repair while sharing error-analysis via a dedicated "teacher" agent. This reduces both degeneration (repeated self-correction error) and error propagation, measurable as absolute improvements of 20–40 percentage points in pass rate compared to single-LLM and naive cooperative baselines (Mi et al., 15 Dec 2024).

3. Algorithmic and Mathematical Foundations

Fundamental to the agentic multi-LLM flow is the tight coupling of code generation, error detection, and iterative refinement. A generic iterative correction algorithm is expressed as follows (Islam et al., 21 Nov 2024):

$\begin{aligned} & c^{(0)} \leftarrow \mathrm{CodeAgent}(\mathrm{prompt}),\ t \leftarrow \mathrm{TestbenchGen}(\mathrm{prompt}) \ & \text{for } i=0,\dots,N_s-1: \ &\quad \ell_s^{(i)} \leftarrow \mathrm{Compile}(c^{(i)}),\ E_s^{(i)} \leftarrow \parseError(\ell_s^{(i)}) \ &\quad \text{if } |E_s^{(i)}| \le \tau_s\ \text{then break} \ &\quad c^{(i+1)} \leftarrow \mathcal{C}(c^{(i)}, E_s^{(i)}) \ & \text{for } j=0,\dots,N_f-1: \ &\quad \ell_f^{(j)} \leftarrow \mathrm{Simulate}(c^{(i^*)}, t),\ E_f^{(j)} \leftarrow \parseError(\ell_f^{(j)}) \ &\quad \text{if } |E_f^{(j)}| \le \tau_f\ \text{then success} \ &\quad c^{(i^*+j+1)} \leftarrow \mathcal{C}(c^{(i^*+j)}, E_f^{(j)}) \end{aligned}$

Where error-parsing and correction functions can be realized by interchangeable LLMs, coordinated via structured messaging or prompt protocols.

In the ensemble/dispatching paradigm, the dispatch function maximizes the expected probability of at least one model in a subset $S_t \subseteq M$ successfully solving a given task $t$, subject to invocation cost constraints (Wang et al., 27 Nov 2025):

$$\max_{S_1, \dots, S_{|T|}} \sum_{t \in T} \left[1 - \prod_{m \in S_t}(1 - Q(m, t))\right]$$

$$\text{subject to} \;\; \sum_{t \in T} \sum_{m \in S_t} c_m \leq C_{\max}$$

Where $Q(m, t)$ is the binary success of model $m$ on task $t$, and $c_m$ is its per-invocation cost.

Evolutionary approaches, as in REvolution, define a fitness function combining PPA objectives for each individual (code candidate) and apply dual-population algorithms with adaptive prompt-strategy selection to evolve the candidate pool (Min et al., 24 Oct 2025):

$$F_{\rm gen} = \alpha\frac{P_{\rm ref}-P_{\rm gen}}{P_{\rm ref}} + \beta\frac{A_{\rm ref}-A_{\rm gen}}{A_{\rm ref}} + \gamma\frac{T_{\rm ref}-T_{\rm gen}}{T_{\rm ref}}$$

4. Representative Frameworks and Empirical Results

A spectrum of frameworks has operationalized the multi-LLM RTL generation paradigm with significant, quantifiable gains:

Framework	Core Mechanism	Key Result	Reference
AIvril2	Agentic, LLM-agnostic, correction loops	$3.4\times$ improved functional pass rate	(Islam et al., 21 Nov 2024)
MAGE	Multi-agent, high-temp sampling, checkpoint debug	95.7% end-to-end correct rate on VerilogEval-Human v2	(Zhao et al., 10 Dec 2024)
CoopetitiveV (PromptV)	Coopetitive parallel learning/repair	99.2% pass@10 (VerilogEval Machine)	(Mi et al., 15 Dec 2024)
Spec2RTL-Agent	Multi-stage, stepwise plan-to-code and reflection	75% fewer human interventions	(Yu et al., 16 Jun 2025)
RTLSquad	Decision-logged, specialist agents, PPA-focused	+10–12pp Pass@1, −18% power	(Wang et al., 6 Jan 2025)
REvolution	Evolutionary multi-strategy, dual-population	+24pp Pass@1 gain on RTLLM-2.0	(Min et al., 24 Oct 2025)
VeriDispatcher	Pre-inference difficulty prediction dispatch	+18% accuracy on RTLLM, −60% API cost	(Wang et al., 27 Nov 2025)

Empirically, agentic and ensemble systems consistently outpace single-LLM pipelines—AIvril2 reports functional pass rates of 77% (Verilog, Claude 3.5), MAGE achieves 95.7% on VerilogEval-Human v2, CoopetitiveV+GPT-4 attains 99.1% pass@10 (Human), and REvolution drives DeepSeek-V3 from 64% to 88% (+24pp) on RTLLM-2.0 (Islam et al., 21 Nov 2024, Zhao et al., 10 Dec 2024, Mi et al., 15 Dec 2024, Min et al., 24 Oct 2025, Wang et al., 27 Nov 2025).

5. Failure Modes, Error Mitigation, and Best Practices

Key error modes in multi-agent flows include degenerative self-correction (repetition and overfitting on own errors), error propagation through purely cooperative agent chains, and bias amplification. CoopetitiveV addresses this by separating error analysis (teacher agent) from independent learners, reducing feedback loops that reinforce model mistakes (Mi et al., 15 Dec 2024).

Best practices extracted from multiple studies include:

• Assign clear, minimal agent roles: code generation, log parsing, simulation, and error correction should be decoupled (Islam et al., 21 Nov 2024, Zhao et al., 10 Dec 2024, Mi et al., 15 Dec 2024).
• Employ parallel repair and competitive selection to inject diversity and avoid local minima (Mi et al., 15 Dec 2024).
• Route subtasks by difficulty-prediction or agent expertise (meta-agent dispatch) to optimize accuracy/cost (Wang et al., 27 Nov 2025, Yu et al., 16 Jun 2025).
• Interleave syntax and functional correction loops; functional correctness must gate PPA optimization (Islam et al., 21 Nov 2024, Wang et al., 6 Jan 2025).
• Record explicit decision logs and natural-language rationales for transparency and adaptation (Wang et al., 6 Jan 2025).
• Limit the number of iterative repairs per code/testbench pair to minimize compute waste (Mi et al., 15 Dec 2024).

6. Benchmarks, Evaluation Metrics, and Comparative Results

The field has converged on a suite of standardized benchmarks and metrics for comparative assessment:

• VerilogEval-Human and RTLLM: Human-authored and protocol-rich functional verification benchmarks.
• VerilogEval-v2, VerilogEval-Machine: Synthetic and variant test suites for pass@k evaluation.
• TuRTLe: An automated, unified evaluation framework integrating four public Verilog/HDL benchmarks and scoring syntax, function, synthesis, and PPA (Garcia-Gasulla et al., 31 Mar 2025).
• ArchXBench: Hierarchically complex, SoC-level benchmarks exposing higher-level agentic limitations (Purini et al., 8 Aug 2025).

Evaluation pivots on metrics such as pass@k (for syntax and function), coverage rates, PPA scores, and human intervention counts. Across frameworks, the gap between syntax and function remains pronounced: for example, TuRTLe finds a ∼34% overall drop from syntax to function pass rates (Garcia-Gasulla et al., 31 Mar 2025).

7. Future Directions and Open Challenges

Research directions highlighted include:

• Hierarchical and dynamic meta-agent allocation for complexity scaling (Islam et al., 21 Nov 2024, Yu et al., 16 Jun 2025, Wang et al., 27 Nov 2025).
• More robust integration of formal verification engines and assertion synthesis (Islam et al., 3 Sep 2024, Garcia-Gasulla et al., 31 Mar 2025).
• Advanced ensemble strategies, e.g., voting over candidate sets or confidence-weighted fusion (Islam et al., 21 Nov 2024, Wang et al., 27 Nov 2025).
• Improved in-loop cost–performance control for hybrid commercial/open-source deployment (Wang et al., 27 Nov 2025).
• Benchmark expansion to multi-module, multi-clock, and mixed-signal tasks (Garcia-Gasulla et al., 31 Mar 2025, Purini et al., 8 Aug 2025).
• Tighter interleaving of PPA-guided optimization and in-simulation/coverage feedback (Wang et al., 6 Jan 2025, Min et al., 24 Oct 2025).

The limitations of current models remain severe above moderate complexity: ArchXBench finds all models uniformly failing on pipelined signal processing, image-processing, and ML blocks, underscoring the need for further domain adaptation and agentic innovation (Purini et al., 8 Aug 2025).

• (Islam et al., 21 Nov 2024) EDA-Aware RTL Generation with LLMs
• (Zhao et al., 10 Dec 2024) MAGE: A Multi-Agent Engine for Automated RTL Code Generation
• (Islam et al., 3 Sep 2024) AIvril: AI-Driven RTL Generation With Verification In-The-Loop
• (Mi et al., 15 Dec 2024) CoopetitiveV: Leveraging LLM-powered Coopetitive Multi-Agent Prompting for High-quality Verilog Generation
• (Yu et al., 16 Jun 2025) Spec2RTL-Agent: Automated Hardware Code Generation from Complex Specifications Using LLM Agent Systems
• (Garcia-Gasulla et al., 31 Mar 2025) TuRTLe: A Unified Evaluation of LLMs for RTL Generation
• (Wang et al., 6 Jan 2025) RTLSquad: Multi-Agent Based Interpretable RTL Design
• (Min et al., 24 Oct 2025) REvolution: An Evolutionary Framework for RTL Generation driven by LLMs
• (Wang et al., 27 Nov 2025) VeriDispatcher: Multi-Model Dispatching through Pre-Inference Difficulty Prediction for RTL Generation Optimization
• (Purini et al., 8 Aug 2025) ArchXBench: A Complex Digital Systems Benchmark Suite for LLM Driven RTL Synthesis