arxiv: 2604.28018 · v1 · submitted 2026-04-30 · 💻 cs.CE · cs.AI

Recognition: unknown

Design Structure Matrix Modularization with Large Language Models

Jianxi Luo, Shuo Jiang

Pith reviewed 2026-05-07 05:21 UTC · model grok-4.3

classification 💻 cs.CE cs.AI

keywords design structure matrixmodularizationlarge language modelscombinatorial optimizationsemantic alignmentengineering design

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The pith

Large language models can partition design structure matrices into modules near reference quality using only prompting.

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

The paper demonstrates that large language models can perform Design Structure Matrix modularization, the partitioning of system elements into cohesive modules, by treating it as a prompted combinatorial task. This extends prior LLM work on DSM sequencing to modularization across multiple cases and model backbones. The approach reaches solution quality close to traditional reference methods within 30 iterations and requires no specialized optimization code. However, providing domain knowledge to the LLM reduces performance on more complex cases, which the authors attribute to semantic misalignment between the model's functional understanding and the structural objective. Ablation experiments identify effective prompt designs and representations for real-world engineering use.

Core claim

LLMs prompted with DSM data can solve the module partitioning problem to near-reference quality without custom solvers. Domain knowledge impairs results on complex DSMs because of misalignment between learned functional priors and the purely structural goal, leading to the semantic-alignment hypothesis. Ablations show best practices for input format, objective, and solution management.

What carries the argument

The prompting strategy for LLM-based DSM modularization that iteratively generates and refines module partitions from matrix data.

If this is right

Engineers can apply LLMs to DSM modularization without developing or running dedicated optimization algorithms.
High-quality partitions emerge quickly, within about 30 iterations, supporting fast design iterations.
Domain knowledge should be withheld from prompts for complex structural tasks to avoid performance degradation.
Careful choices of input representation and objective formulation, identified via ablations, enhance practical results.
The semantic-alignment hypothesis offers a way to predict when external knowledge will aid or hinder LLM optimization.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The approach may generalize to other combinatorial engineering tasks such as clustering or layout optimization.
LLMs might benefit from additional training to better distinguish semantic content from structural objectives in design problems.
Testing the method on proprietary DSMs from industry could reveal if the near-reference performance holds outside the studied cases.
Hybrid LLM-traditional optimizer systems could mitigate any remaining quality gaps.

Load-bearing premise

That an LLM prompted with DSM data can reliably identify good module partitions for the combinatorial task.

What would settle it

If the LLM outputs on the five cases do not approach reference quality after 30 iterations or if adding domain knowledge improves performance on complex DSMs instead of impairing it.

Figures

Figures reproduced from arXiv: 2604.28018 by Jianxi Luo, Shuo Jiang.

**Figure 1.** Figure 1: LLM-based combinatorial optimization framework for DSM modularization. (a) The iterative loop consists view at source ↗

**Figure 2.** Figure 2: Convergence behavior of LLM-CO across three LLMs and five DSM cases. Each panel shows Gap% (mean view at source ↗

**Figure 3.** Figure 3: Modularization quality with and without domain knowledge. Each bar shows final Gap% for view at source ↗

**Figure 4.** Figure 4: Ablation convergence behavior (UCAV, Claude, view at source ↗

read the original abstract

Design Structure Matrix (DSM) modularization, the task of partitioning system elements into cohesive modules, is a fundamental combinatorial challenge in engineering design. Traditional methods treat modularization as a pure graph optimization, without access to the engineering context embedded in the system. Building on prior work on LLM-based combinatorial optimization for DSM sequencing, this paper extends the method to modularization across five cases and three backbone LLMs. Our method achieves near-reference quality within 30 iterations without requiring specialized optimization code. Counterintuitively, domain knowledge, beneficial in sequencing, consistently impairs performance on more complex DSMs. We attribute this to semantic misalignment between the LLM's functional priors and the purely structural optimization objective, and propose the semantic-alignment hypothesis as a testable condition governing knowledge effectiveness with LLMs. Ablation studies identify the most effective input representation, objective formulation, and solution pool design for practical deployment. These findings offer practical guidance for deploying LLMs in engineering design optimization.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

LLM prompting reaches decent DSM modularization results on five cases but single-run tests leave the reliability claim shaky.

read the letter

This paper extends the authors' prior LLM sequencing work to the modularization task. They run prompting experiments on five DSM cases with three backbone models, report that results approach reference quality inside 30 iterations, and observe that domain knowledge hurts performance on the harder instances. They frame the latter as a semantic-alignment issue between the model's functional priors and the pure structural objective, and they back the claim with ablation results on input format, objective wording, and solution-pool handling. That last part is the most immediately usable: the ablations give other groups a short recipe for trying the same approach without writing optimization code from scratch. The counter-intuitive domain-knowledge finding is also worth noting, even if the hypothesis itself is still interpretive rather than tightly measured. The main weakness is the missing variance data. LLMs are stochastic, yet the paper does not show repeated trials per case or any error bars on the quality scores. A single successful partition per instance does not establish that the method will reliably hit near-reference performance when an engineering team tries it on a new DSM. The abstract's claim of near-reference quality therefore rests on thinner evidence than the headline suggests. The work is aimed at systems engineers and design teams who already use DSMs and want a code-light option for partitioning. It is not a theoretical advance, but the empirical setup is concrete enough that a referee could verify the numbers against the supplied cases. I would send it to peer review, with the main request being added multi-run statistics and clearer baseline definitions so the practical claims can be judged properly.

Referee Report

2 major / 2 minor

Summary. The paper claims that large language models (LLMs) can perform Design Structure Matrix (DSM) modularization—a combinatorial partitioning task—by direct prompting with DSM data. Extending prior LLM sequencing work, experiments across five cases and three LLMs reportedly reach near-reference quality within 30 iterations without specialized optimization code. Domain knowledge is found to impair performance on complex DSMs (opposite to sequencing), which the authors attribute to semantic misalignment between LLM functional priors and structural objectives; they propose the semantic-alignment hypothesis as a governing condition. Ablation studies identify optimal input representations, objective formulations, and solution-pool designs for deployment.

Significance. If the results hold, the work supplies practical guidance for applying LLMs to engineering design optimization without custom solvers, with actionable ablation findings on prompting. The counterintuitive domain-knowledge effect and the proposed testable hypothesis could stimulate research on when and why LLMs succeed or fail at structural combinatorial tasks. Multi-case, multi-LLM evaluation is a strength, as is the focus on reproducible deployment conditions.

major comments (2)

Results section (five cases, three LLMs): the claim of reliable near-reference quality within 30 iterations is load-bearing for the central contribution, yet no repeated trials, error bars, or variance statistics are reported. Given LLM stochasticity, single-run or best-case outcomes per configuration cannot establish consistency or reproducibility, directly weakening the assertion that the method can be deployed without specialized code.
Ablation studies: the identification of 'most effective' input representation, objective formulation, and solution pool design lacks any indication of multiple runs per variant or statistical significance testing. Without these, the ranking of designs and the practical recommendations rest on unquantified differences.

minor comments (2)

Abstract: the phrase 'near-reference quality' is used without defining the reference method, the quantitative metric (e.g., modularity score or partition similarity), or baseline values, making the headline claim difficult to evaluate at first reading.
The semantic-alignment hypothesis is offered as an interpretive explanation rather than a quantity derived from the fitted parameters; a short clarifying sentence distinguishing interpretation from data would improve precision.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback emphasizing reproducibility and statistical rigor in our LLM-based DSM modularization experiments. We address each major comment below and commit to revisions that strengthen the evidence for our claims without altering the core findings or methodology.

read point-by-point responses

Referee: Results section (five cases, three LLMs): the claim of reliable near-reference quality within 30 iterations is load-bearing for the central contribution, yet no repeated trials, error bars, or variance statistics are reported. Given LLM stochasticity, single-run or best-case outcomes per configuration cannot establish consistency or reproducibility, directly weakening the assertion that the method can be deployed without specialized code.

Authors: We agree that the stochasticity of LLMs necessitates repeated trials and variance reporting to substantiate consistency claims. The original experiments used single runs per configuration to efficiently explore a broad space of prompting strategies across five cases and three models while managing API costs. Performance trends were stable in these runs, supporting the reported near-reference quality. In the revised manuscript, we will conduct multiple independent trials (minimum of five per key case-LLM pair) for the primary results, reporting mean performance metrics with standard deviations and error bars. This will directly address reproducibility and bolster the deployment assertion. revision: yes
Referee: Ablation studies: the identification of 'most effective' input representation, objective formulation, and solution pool design lacks any indication of multiple runs per variant or statistical significance testing. Without these, the ranking of designs and the practical recommendations rest on unquantified differences.

Authors: We concur that the ablation rankings would be more robust with repeated runs and statistical validation. The ablations systematically compared input formats, objective phrasings, and pool designs, with selected variants demonstrating consistent advantages across the evaluated cases. To quantify these differences, the revised version will rerun each ablation variant with multiple trials and incorporate statistical tests (such as paired comparisons with confidence intervals) to confirm the superiority of the recommended configurations. This will provide stronger justification for the practical guidance offered. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical LLM prompting experiments

full rationale

The paper presents results from prompting experiments that extend prior LLM-based DSM sequencing work to the modularization task across five cases and three backbone models. All central claims—near-reference quality within 30 iterations, counterintuitive effects of domain knowledge, and the semantic-alignment hypothesis—are grounded in reported ablation outcomes and observed performance rather than any mathematical derivation, fitted parameter, or self-referential definition that reduces to the inputs by construction. The citation to the authors' earlier sequencing paper supplies methodological context but does not bear the load of the new empirical findings, which remain independently replicable through repeated prompting trials. No equations, uniqueness theorems, or ansatzes are invoked that would create a closed loop.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the approach appears to rest on standard LLM prompting practices and graph-partitioning concepts drawn from prior literature without new postulates.

pith-pipeline@v0.9.0 · 5453 in / 1388 out tokens · 100293 ms · 2026-05-07T05:21:32.710649+00:00 · methodology

discussion (0)

Reference graph

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