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REVIEW 4 major objections 8 minor 3 cited by

RTLSquad: Multi-Agent Based Interpretable RTL Design

T0 review · 4 major / 8 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read RTLSquad is a multi-agent LLM framework that lifts functionally correct Verilog generation from 60.8 percent to 72.0 percent on one backbone model while logging a human-readable decision path for every design choice.

desk verdict Multi-agent orchestration gives a real Pass@1 boost for LLM-based RTL generation, but the PPA headline rests on a warm-start confound and the numbers need error bars and code. read the letter →

arxiv 2501.05470 v1 pith:B2GK43M5 submitted 2025-01-06 cs.AR cs.AIcs.SE

Bowei Wang , Qi Xiong , Zeqing Xiang , Lei Wang , Renzhi Chen This is my paper · ORCID
classification cs.ARcs.AIcs.SE
keywords ICDesignLargeLanguageModelMulti-AgentSystemRTLcodegenerationPPAoptimizationdecisioninterpretabilityVerilogfunctionalverification
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

RTLSquad organizes RTL design as a three-stage collaboration among specialized LLM agents: a debate of power, performance, and area experts proposes design changes; a programmer and reviewer implement them; and an observer and analyst read EDA reports to grade the result and steer the next round. The paper's claim is that this division of labor makes code generation more correct and more efficient than a single planning LLM, lifting the pass rate on functional verification from 60.8 percent to 72.0 percent on one backbone model and from 47.6 percent to 58.0 percent on a second. The paper also claims that, when started from a verified reference implementation, RTLSquad matches or beats that reference on power, performance, and area in 73.3 percent of designs. Because the agents negotiate in natural language, each exploration commit, code review, and analysis rating becomes a documented decision path, which the authors argue is the missing trust mechanism for putting LLM-generated RTL into a real hardware flow.

What carries the argument

The machinery is the squad-and-budget loop. Three domain experts debate proposed exploration actions in a shared message pool and adopt commits by majority vote; a programmer-reviewer pair implements the accepted plan against a checklist; and an observer-analyst pair parses synthesis and simulation reports, gives the plan a rating $r(k)\in[1,5]$, and updates the next round's exploration budget through $\hat{p}(k)=(\alpha-\beta r)p(k)$ and $p(k+1)=\eta\hat{p}(k)+(1-\eta)p(k)$, with $\alpha=2.375$, $\beta=0.375$, and $\eta=0.4$. This budget makes the search aggressive after failures and conservative after successes, while the message log doubles as the decision path that gives the design human-readable justification.

What would settle it

Rerun Table I with the baseline also initialized on the verified reference code; if RTLSquad no longer matches or beats the reference in most designs, the PPA claim fails. An independent check is to disable the analyst's rating feedback and fix the exploration budget: if functional correctness and power/performance/area gains persist unchanged, the adaptive exploration loop is not carrying the result.

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Extended reading notes

Core claim

The central discovery is that a multi-agent squad, not a single LLM, should own the RTL design loop. RTLSquad splits the work into exploration, implementation, and verification/evaluation stages, with separate agent roles—specialists for power, performance, and area; a programmer and reviewer; an observer and analyst—and the agents' natural-language conversation serves as both the coordination mechanism and the explanation. Under the paper's evaluation on a 50-design benchmark, this loop raises functional correctness Pass@1 from 60.8 to 72.0 on one backbone and from 47.6 to 58.0 on another, and when the verified reference design is given as the starting point, the system's synthesized results match or beat the reference power/performance/area metrics in 73.3 percent of cases. The interpretability result is that the decision path—who proposed what, who objected, what the EDA reports showed, and how the analyst rated the plan—is produced as a side effect of the collaboration rather than a separate explanation step.

Load-bearing premise

The central claim leans on the assumption that the power, performance, and area comparison is fair—that the baseline also starts from the same raw specification rather than generating from scratch while RTLSquad is handed the already-verified reference code to optimize; if the starting points differed, the reported 73.3 percent advantage could be measuring the head start.

Editorial extensions

If this is right

  • Functional correctness becomes a systems property: the same backbone LLM produces more verified RTL inside RTLSquad than it does with self-planning, so the framework can be wrapped around existing models.
  • EDA feedback is no longer just a stop signal; synthesis numbers steer the next design proposal, so every failed or successful experiment changes the exploration budget for the following round.
  • The decision-path documentation makes each generated design auditable, letting a human engineer review the reasoning behind a code change before accepting it into a design flow.
  • Because the pipeline is model-agnostic, the same three-stage squad structure could be applied to other hardware design steps where traceable decisions matter, such as logic synthesis or physical design choices.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: the reported 73.3 percent PPA advantage should be read with the starting point in mind—the paper gives RTLSquad the verified reference code while the baseline generates from the raw specification, so the comparison may partly credit the head start rather than the multi-agent loop.
  • Inference: a decision path is a rationale, not a proof; an LLM agent can produce a confident-sounding justification for a wrong design, so interpretability here should be understood as transparency of process rather than a guarantee of correctness.
  • Inference: the smoothing rule in the exploration budget is a hand-set heuristic, and the paper does not ablate it; a fixed-budget control would show whether the adaptive scoring rule, rather than the debate structure, carries the Pass@1 and PPA gains.
  • Inference: retaining rejected design decisions and their analysis across projects could let RTLSquad avoid repeating known failures; the paper's 'Analysis Memories' are per-run, so cross-run learning is an untested extension.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 8 minor

Summary. The paper proposes RTLSquad, a multi-agent LLM system for RTL code generation and optimization. The system is organized into exploration, implementation, and verification/evaluation squads; agents collaborate through natural-language messaging, and the resulting transcripts are presented as decision paths that provide interpretability. The authors evaluate functional correctness via Pass@1 on the RTLLM V2.0 benchmark with DeepSeek-V2.5 and LLaMA 3.1-70B backbones, and evaluate PPA via synthesis of a subset of designs, reporting improvements over a Self-Planning baseline and over reference implementations. The central claims are that RTLSquad improves functional correctness over Self-Planning, achieves competitive or better PPA, and provides interpretable design decisions.

Significance. If the claims hold, RTLSquad would be a useful step toward practical LLM-assisted RTL design, particularly because it combines collaboration among specialized agents with explicit decision documentation. The functional-correctness results are verified against external testbenches, which is a genuine strength and avoids circularity. However, the PPA comparison is confounded by unequal starting conditions, and the functional-correctness numbers are reported without statistical uncertainty despite stochastic sampling. The interpretability contribution is illustrated with case studies rather than measured. These issues make the current evidence insufficient to support the paper's strongest claims, but the framework itself is plausible and worth building upon.

major comments (4)
  1. [V-C, Table I] The PPA comparison is confounded. Section V-C states that RTLSquad "took the reference implementations from the dataset as input" and optimized them, while the Deepseek-SP baseline generates RTL from the specification using Self-Planning. A design that starts from a verified, human-written reference and is then optimized will typically have a PPA advantage over a design generated from scratch, independent of the multi-agent mechanism. Thus Table I and the "73.3%" claim do not isolate RTLSquad's contribution. The authors should provide a matched comparison in which both methods start from the same reference code, or alternatively run RTLSquad from scratch and compare with the baseline from scratch.
  2. [V-B, Table II; Abstract] The functional-correctness improvement is reported inconsistently and without variance. The abstract says Pass@1 improves by 7.2%, but Table II shows improvements of 10.4 percentage points for LLaMA (47.6 to 58.0) and 11.2 percentage points for DeepSeek (60.8 to 72.0). If 7.2% is a relative figure or an average, the definition is missing. More importantly, the experiments use temperature 0.8 and single runs, so the reported Pass@1 numbers may be within sampling noise. The authors should report multiple seeds or confidence intervals, and clarify how Pass@1 is computed when only one sample per design appears to be evaluated.
  3. [III-C, Eqs. (1)-(2)] The exploration-point update is a hand-set heuristic with no supporting derivation or sensitivity analysis. The constants alpha = 2.375, beta = 0.375, and eta = 0.4 are introduced without justification, and the rating r(k) is the LLM analyst's subjective judgment, not an independently validated measure of PPA quality. Because this mechanism controls how many exploration actions are available in each round, the claimed PPA improvements could depend on these particular constants and on the analyst's rating behavior. The authors should either derive the update from a principled objective, validate that r(k) correlates with measured PPA, or at least report a sensitivity study over the constants.
  4. [IV, Case Study] The decision-interpretability claim is supported only by selected excerpts from the system's own transcripts. No evidence is provided that these transcripts are actually understandable or useful to hardware engineers, nor is there any comparison with existing interpretability mechanisms such as chain-of-thought or MCTS-based result reporting. Since interpretability is a central stated contribution, the paper should include a user study, a rubric-based evaluation, or another quantitative measure; otherwise the claim that RTLSquad provides "sufficient, understandable justification" overstates what the data show.
minor comments (8)
  1. [Abstract] The 7.2% Pass@1 improvement claim should be reconciled with the 10.4 and 11.2 percentage-point gains in Table II, or the metric used should be explicitly defined.
  2. [IV-C] The section heading contains a typo: "Exporation Interpretability" should be "Exploration Interpretability."
  3. [I, Contributions] The phrase "greater practical practical value" contains a duplicated word and should read "greater practical value."
  4. [III-C] The text contains an apparent formatting artifact: "The squad SV eriin" is not readable and should be corrected to refer properly to the verification squad.
  5. [Table I] The caption does not explain the parenthesized performance values (e.g., slack values) or the meaning of the "Deepseek" column entries, and several rows have missing entries for both the Deepseek baseline and RTLSquad; the missing-data policy should be stated.
  6. [V-A] The paper says the RTLLM V2.0 dataset contains 50 designs, but Table I reports only 11 designs and Table II gives aggregate results only; the number of designs used for each evaluation and the selection criterion should be specified.
  7. [V-A] The authors state that the optimized testbench dataset "will be made publicly available," but no mention is made of releasing the RTLSquad code or the full decision-path transcripts; such artifacts would substantially increase reproducibility.
  8. [I, Related Work] The claim of being "the first to be able to generate RTL code while providing decision paths" is too strong without a broader comparison of prior multi-agent systems that also emit natural-language rationales; it should be qualified.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found; the main correctness result is externally verified and the PPA comparison asymmetry is a validity threat, not a circularity.

full rationale

Walking the claimed derivation chain, the functional-correctness claim (Pass@1) is grounded in external testbenches and compares RTLSquad and Self-Planning from the same specification inputs, so no fitted input or self-citation makes the result equivalent to its inputs. The PPA section (V-C) states 'RTLSquad took the reference implementations from the dataset as input, optimized them to evaluate the framework's capability in optimizing PPA metrics,' while the SP baseline is generated from the specification; this unequal starting point is a real threat to the fairness of Table I as a comparison of generation methods, but it is not a circularity: optimizing a known-correct reference can still degrade it, so the reported PPA values are not identical to the input by construction. The exploration-point rule (Eqs. 1-2, Section III-C) is explicitly a heuristic with hand-set coefficients (alpha=2.375, beta=0.375, eta=0.4); the paper does not claim it is derived from first principles, and the inverse relationship between analyst score and exploration budget is a definitional design choice, not a hidden prediction. The interpretability claim is supported by transcribed decision paths; whether those paths are 'sufficient' or 'understandable' is not independently measured, but that is an evaluation gap, not a circular reduction. There are no load-bearing self-citations or imported uniqueness theorems. Accordingly, no step in the paper reduces to its own inputs by construction.

Assumptions & free parameters 5 free parameters · 6 assumptions · 2 invented entities

The system rests on the unverified assumption that LLM agents can turn design discussions into correct code, that hand-set exploration dynamics help, and that transcripts are genuine explanations. No code, prompts, or data are released, and the PPA comparison starts from reference designs. These are the main unpaid-for elements.

free parameters (5)
  • alpha, beta (score-to-exploration scaling) = alpha=2.375, beta=0.375
    Hand-set in Eq. (1); no derivation, sensitivity analysis, or ablation; they control how analyst ratings r(k) rescale exploration points.
  • eta (smoothing factor) = 0.4
    Hand-set in Eq. (2) for updating exploration points; no justification.
  • Initial exploration points p(0) = unspecified
    The point budget that limits expl/opt actions is never given an initial value, so the exploration behavior is underdetermined.
  • LLM sampling temperature = 0.8
    Chosen for agent generation; no sensitivity analysis, and stochastic sampling without repeated seeds undermines Pass@1 stability.
  • Exploration action costs = expl=2 points, opt=1 point
    Hand-set costs for exploration vs optimization actions; no ablation.
assumptions (6)
  • domain assumption LLM agents can translate natural-language exploration plans into functionally correct RTL code.
    The whole implementation stage assumes the programmer/reviewer agents reliably realize design decisions; Section III-B.
  • ad hoc to paper The analyst's rating r(k) and termination judgment correctly reflect PPA quality and the optimum.
    Section III-C; no validation that ratings correlate with EDA outcomes or that the flat/declining trend criterion stops at the optimum.
  • domain assumption Transcripts of agent communication are faithful and sufficient explanations of design decisions.
    Section IV treats excerpts as decision interpretability without a user study or comparison to the actual causal process.
  • domain assumption RTLLM V2.0 testbenches adequately verify functional correctness.
    Section V-A; correctness is judged only by these testbenches, with no additional formal verification.
  • domain assumption Self-Planning is a fair baseline for PPA comparison.
    Section V-C; the baseline appears to generate from scratch while RTLSquad optimizes the dataset's reference implementation, so the comparison may not be apples-to-apples.
  • ad hoc to paper Eq. (1)-(2) with hand-set constants improve exploration.
    Presented as the mechanism but no proof or ablation that dynamic point adjustment outperforms fixed budgets.
invented entities (2)
  • Exploration points p(k)
    purpose: Budget limiting the number and type of exploration/optimization actions per round.
    Internal heuristic defined in Eq. (1)-(2); no external measure validates that it tracks design quality.
  • Analysis Memories
    purpose: Historical records the analyst uses to rate exploration plans and justify decisions.
    Introduced in Sections III-C and IV-B; faithfulness and usefulness are not independently evaluated.

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Cite this review

Pith. "Pith review of RTLSquad: Multi-Agent Based Interpretable RTL Design." pith.science (2026). https://pith.science/paper/B2GK43M5

@misc{pith2026250105470,
  author       = {Pith},
  title        = {Pith review of: RTLSquad: Multi-Agent Based Interpretable RTL Design},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B2GK43M5}},
  note         = {Machine review of arXiv:2501.05470}
}
read the original abstract

Optimizing Register-Transfer Level (RTL) code is crucial for improving hardware PPA performance. Large Language Models (LLMs) offer new approaches for automatic RTL code generation and optimization. However, existing methods often lack decision interpretability (sufficient, understandable justification for decisions), making it difficult for hardware engineers to trust the generated results, thus preventing these methods from being integrated into the design process. To address this, we propose RTLSquad, a novel LLM-Based Multi-Agent system for interpretable RTL code generation. RTLSquad divides the design process into exploration, implementation, and verification & evaluation stages managed by specialized agent squads, generating optimized RTL code through inter-agent collaboration, and providing decision interpretability through the communication process. Experiments show that RTLSquad excels in generating functionally correct RTL code and optimizing PPA performance, while also having the capability to provide decision paths, demonstrating the practical value of our system.

Figures

Figures reproduced from arXiv: 2501.05470 by the authors.

Figure 1
Figure 1. The overview of RTLSquad. The workflow consists of three stages: [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 4
Figure 4. The exploration stage of RTLSquad. characteristics of the indicators based on the current and past analysis records of the current code. The analyst will give a rating score r (k) ∈ [1, 5] on the exploration plan, representing the effectiveness of the plan in optimizing PPA performance, with a higher score indicating greater effectiveness. The rating results will be used to adjust the exploration points p (k+1) for … view at source ↗
Figure 3
Figure 3. The Verification & Evaluation stage of RTLSquad. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

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