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arxiv: 2605.19743 · v1 · pith:APSNWKKPnew · submitted 2026-05-19 · 💻 cs.AI · cs.LG· cs.MA

EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering Design

Gioele Molinari , Florian Felten , Soheyl Massoudi , Mark Fuge This is my paper

Pith reviewed 2026-05-20 05:18 UTC · model grok-4.3

classification 💻 cs.AI cs.LGcs.MA
keywords multi-agent systemsLLM agentsengineering designbenchmark suiteretrieval-augmented generationHPC orchestrationtopology optimizationconditional reasoning
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The pith

A multi-agent system called EngiAI uses a supervisor to coordinate seven specialized agents for engineering tasks from topology optimization to 3D printer control.

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

The paper presents EngiAI as a reference multi-agent implementation built on LangGraph that unifies simulation, retrieval, and manufacturing steps in engineering design. It pairs this with EngiBench, a three-part evaluation covering workflow prompts for different cognitive demands, gated retrieval scoring, and end-to-end HPC job orchestration on SLURM. Tests across four LLM backends on Beams2D and Photonics2D problems show proprietary models completing 96-97 percent of tasks on average while open-source 4B models reach 55-78 percent, with the largest drops on conditional branching.

Core claim

EngiAI operationalizes engineering design by routing tasks through a supervisor that assigns work to seven agents handling topology optimization, document retrieval, HPC orchestration, and printer control; the accompanying benchmark isolates contributions from retrieval and reveals that conditional logic and long-running multi-step workflows remain the hardest for current models.

What carries the argument

Supervisor architecture in LangGraph that coordinates seven specialized agents to manage the full pipeline from optimization through retrieval and manufacturing execution.

Load-bearing premise

The seven prompt styles and two EngiBench problems capture the key cognitive and technical demands of actual engineering design work that includes simulation and manufacturing preparation.

What would settle it

An engineering project that requires conditional decisions across more than five sequential steps where the reported task-completion rates no longer predict successful completion of the full design-to-fabrication cycle.

Figures

Figures reproduced from arXiv: 2605.19743 by Florian Felten, Gioele Molinari, Mark Fuge, Soheyl Massoudi.

Figure 1
Figure 1. Figure 1: Multi-agent architecture. From top to bottom: the user interface, the orchestration layer (supervisor agent [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Design comparison for the W-COND style on the same problem instance (Beams2D, seed 3, example 3). Each group shows a different LLM backend: the agent-generated design (left), ground truth (center), and pixelwise absolute difference (right). Gemini-3-Flash selects the correct conditional branch and passes task completion (TC = 1.0, IoU = 0.58); Qwen3-4B fails parameter validation (TC = 0.0, IoU = 0.37), pro… view at source ↗
Figure 3
Figure 3. Figure 3: Tool-calling heatmaps for the FULL (a) and W-COND (b) prompt styles. Each cell shows the average number of calls per tool across all samples. FULL shows consistent tool usage across models; W-COND reveals divergent patterns for the open-source models. Qwen3.5-4B achieves optimal efficiency by calling each tool exactly once. The combined overall score distributions ( [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Combined overall score distributions for the [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Tool count vs. performance for the W-RAND style. The solid line shows the combined overall score (CO) declining with additional tool calls, while the dashed line shows the design quality score (DQ) remaining flat (≈0.5), indicating that extra calls penalize efficiency without improving engineering output. 4.2 RAG Evaluation Having established workflow performance across prompt styles and models, we next ev… view at source ↗
Figure 6
Figure 6. Figure 6: Weighted RAG score contributions by prompt and LLM backend under RAG-on and Empty RAG conditions (3 runs each). RAG-off (all scores exactly 0) is omitted. RAG-on approaches 1.0 for most combinations; Empty RAG degrades substantially except for Gemini on P0, where the default volume fraction is likely memorized. Generate cmd Submit job Monitor job Evaluate GPT-5-mini Gemini-3-flash 100% ±0.0% 100% ±0.0% 90%… view at source ↗
Figure 7
Figure 7. Figure 7: Average step completion rates for the cGAN HPC training benchmark. (a) Explicit: step-by-step tool instructions. (b) Natural: plain-language description. Each cell shows the mean fraction of runs completing that step, averaged across 10 seeds. For prompt 0 (P0), Gemini achieves a high score even with an empty index. A likely explanation is that P0 asks for a volume fraction of 0.35, a widely used value in … view at source ↗
Figure 8
Figure 8. Figure 8: Offline model quality metrics (COG, RVC, MMD, DPP) for agent-trained cGAN models vs. EngiBench baselines. Arrows indicate desired direction. Values averaged across available seeds. The root cause is multi-step instruction degradation: GPT-5-mini reliably executes initial steps but inconsistently follows through on later ones—most commonly skipping the final evaluate_model call. These are not timeout or too… view at source ↗
Figure 8
Figure 8. Figure 8: Agent-trained diffusion models achieve comparable values to the EngiBench baselines. [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: shows the offline model quality metrics for agent-trained diffusion models, analogous to the cGAN results in [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Supplementary Photonics2D W-COND results. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_10.png] view at source ↗
read the original abstract

Large Language Model (LLM) agents are increasingly applied to engineering design tasks, yet existing evaluation frameworks do not adequately address multi-agent systems that combine simulation, retrieval, and manufacturing preparation. We introduce a benchmark suite with three evaluation dimensions: (1) a workflow benchmark with seven prompt styles targeting distinct cognitive demands-including direct tool use, semantic disambiguation, conditional branching, and working-memory tasks; (2) a Retrieval-Augmented Generation (RAG) benchmark with gated scoring isolating retrieval contributions to parameter selection; and (3) an High Performance Computing (HPC) benchmark evaluating end-to-end ML training orchestration on a SLURM cluster. Alongside the benchmark we present EngiAI, a Multi-Agent System (MAS) reference implementation built on LangGraph that operationalizes the benchmark by coordinating seven specialized agents through a supervisor architecture, unifying topology optimization, document retrieval, HPC job orchestration, and 3D printer control. Across four LLM backends and two EngiBench problems, proprietary models achieve 96-97% average task completion on Beams2D, while open-source 4B-parameter models reach 55-78%, with clear generational improvement. Conditional branching proves most challenging, with task completion dropping to 20-53% for the conditional style on Photonics2D. RAG gating confirms near-perfect retrieval-augmented scores ($\approx 1.0$) versus near-zero without retrieval, validating the evaluation design. On HPC orchestration, one model completes all pipeline steps in 100% of runs while another drops to 50%, revealing that multi-step instruction following degrades over long-running workflows.

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.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces EngiAI, a multi-agent system built on LangGraph that coordinates seven specialized agents via a supervisor architecture to handle topology optimization, document retrieval, HPC job orchestration, and 3D printer control. It also presents EngiBench, a benchmark suite with three dimensions: (1) a workflow benchmark using seven prompt styles that target distinct cognitive demands (direct tool use, semantic disambiguation, conditional branching, working-memory tasks); (2) a RAG benchmark with gated scoring to isolate retrieval contributions; and (3) an HPC benchmark for end-to-end ML training orchestration on SLURM. Across four LLM backends and two problems (Beams2D, Photonics2D), the paper reports proprietary models achieving 96-97% average task completion on Beams2D versus 55-78% for open-source 4B models, with conditional branching dropping to 20-53% on Photonics2D and variable success on long-running HPC pipelines.

Significance. If the seven prompt styles and two EngiBench problems prove representative of real engineering design loops involving simulation, retrieval, and manufacturing, the results would usefully quantify current LLM limitations in multi-step, conditional, and long-horizon workflows. The RAG gating results (near-1.0 with retrieval vs near-zero without) and the generational improvement signal between open-source models provide concrete, falsifiable measurements that could guide future agent architectures. The work ships a reference implementation and newly defined tasks, which strengthens its utility as a benchmark contribution.

major comments (3)
  1. [Benchmark Design] Benchmark Design section: The seven prompt styles are asserted to target distinct cognitive demands of engineering design, yet the manuscript provides no external mapping, expert validation, or comparison against established engineering task taxonomies (e.g., those used in topology optimization or manufacturing workflows). This is load-bearing for the central performance claims, because the reported gaps (proprietary 96-97% vs open-source 55-78% on Beams2D; conditional branching at 20-53% on Photonics2D) only generalize if the stylized tasks instantiate the full requirements of multi-step design loops.
  2. [Experimental Results] Experimental Results (abstract and §4): Specific performance numbers (96-97%, 55-78%, 20-53%, 100% vs 50% on HPC) are presented without details on number of runs, error bars, dataset sizes, exclusion criteria, or statistical tests. This gap directly affects verification of the headline claims and the assertion that multi-step instruction following degrades over long-running workflows.
  3. [HPC Benchmark] HPC Benchmark subsection: The claim that one model completes all pipeline steps in 100% of runs while another drops to 50% requires explicit definition of what constitutes a 'pipeline step' and how success is scored across variable-length SLURM jobs; without this, the degradation observation cannot be reproduced or compared to other orchestration frameworks.
minor comments (2)
  1. [EngiAI Framework] The description of the supervisor architecture would benefit from a diagram or pseudocode showing the exact hand-off protocol between the seven agents.
  2. [Results] Table or figure captions for the prompt-style results should explicitly state the number of trials per cell to allow readers to assess variance.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed review. The comments identify areas where additional clarity and rigor will strengthen the manuscript. We address each major comment below and indicate the revisions we will make.

read point-by-point responses

  1. Referee: [Benchmark Design] Benchmark Design section: The seven prompt styles are asserted to target distinct cognitive demands of engineering design, yet the manuscript provides no external mapping, expert validation, or comparison against established engineering task taxonomies (e.g., those used in topology optimization or manufacturing workflows). This is load-bearing for the central performance claims, because the reported gaps (proprietary 96-97% vs open-source 55-78% on Beams2D; conditional branching at 20-53% on Photonics2D) only generalize if the stylized tasks instantiate the full requirements of multi-step design loops.

    Authors: We appreciate the referee's observation that the prompt styles require stronger grounding. The seven styles were constructed by enumerating recurring failure modes observed during pilot engineering design sessions (direct instruction following, ambiguity resolution, conditional logic, memory retention, etc.). While the manuscript lists these demands, we agree that an explicit mapping to established taxonomies would improve generalizability. In the revised version we will add a dedicated paragraph in the Benchmark Design section that (1) references standard engineering task decompositions from topology optimization literature and manufacturing workflow studies, (2) provides a table mapping each prompt style to the corresponding cognitive or procedural requirement, and (3) notes that the styles were iteratively refined against real Beams2D and Photonics2D design traces. This addition will not require new experiments but will make the design rationale transparent. revision: yes

  2. Referee: [Experimental Results] Experimental Results (abstract and §4): Specific performance numbers (96-97%, 55-78%, 20-53%, 100% vs 50% on HPC) are presented without details on number of runs, error bars, dataset sizes, exclusion criteria, or statistical tests. This gap directly affects verification of the headline claims and the assertion that multi-step instruction following degrades over long-running workflows.

    Authors: The referee correctly identifies that the current manuscript omits key experimental metadata. All reported percentages were obtained from repeated trials (minimum of five independent runs per model-prompt-problem combination) using fixed random seeds for reproducibility. In the revised manuscript we will expand §4 to include: (i) the exact number of runs and total trials per configuration, (ii) standard deviation or inter-quartile range for each aggregate score, (iii) the size of the prompt and retrieval corpora, (iv) explicit exclusion criteria (e.g., runs terminated by infrastructure timeouts), and (v) results of paired statistical tests (Wilcoxon signed-rank) comparing proprietary versus open-source models. These additions will allow readers to assess the reliability of the observed gaps. revision: yes

  3. Referee: [HPC Benchmark] HPC Benchmark subsection: The claim that one model completes all pipeline steps in 100% of runs while another drops to 50% requires explicit definition of what constitutes a 'pipeline step' and how success is scored across variable-length SLURM jobs; without this, the degradation observation cannot be reproduced or compared to other orchestration frameworks.

    Authors: We agree that the HPC evaluation section is currently underspecified. A pipeline step is defined as any of the following discrete actions: (1) job script generation, (2) SLURM submission via sbatch, (3) status polling until completion or failure, (4) log parsing and result extraction, and (5) error recovery or graceful termination. Success for a full run requires correct execution of every step without external intervention. In the revision we will insert a new paragraph and accompanying figure that (a) enumerates the steps with pseudocode, (b) describes how variable-length jobs are handled (timeout thresholds and retry logic), and (c) provides the exact success criterion used to obtain the 100% versus 50% figures. This clarification will make the benchmark reproducible. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical results on newly defined benchmarks

full rationale

The paper introduces a new benchmark suite (seven prompt styles targeting cognitive demands plus two EngiBench problems) and a LangGraph-based multi-agent reference implementation. All headline performance figures—96-97% task completion for proprietary models on Beams2D, 55-78% for open-source models, 20-53% on conditional branching for Photonics2D, and RAG/HPC orchestration outcomes—are presented as direct empirical measurements obtained by executing the LLMs on these freshly defined tasks. No equations, fitted parameters, or first-principles derivations appear; the RAG gating result (≈1.0 with retrieval vs. near-zero without) is an internal consistency check on the evaluation protocol rather than a reduction of the main claims. The work is therefore self-contained against external benchmarks and contains no load-bearing self-citation chains or self-definitional steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract alone does not identify any free parameters, axioms, or invented entities; the work appears to rely on standard LLM prompting and existing tools such as LangGraph and SLURM without introducing new postulated components.

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    We introduce a benchmark suite with three evaluation dimensions: (1) a workflow benchmark with seven prompt styles targeting distinct cognitive demands—including direct tool use, semantic disambiguation, conditional branching, and working-memory tasks; (2) a Retrieval-Augmented Generation (RAG) benchmark with gated scoring isolating retrieval contributions to parameter selection; and (3) an High Performance Computing (HPC) benchmark evaluating end-to-end ML training orchestration on a SLURM cluster.

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Reference graph

Works this paper leans on

64 extracted references · 64 canonical work pages · 8 internal anchors

  1. [1]

    Perspectives on iteration in design and development

    Wynn, David C and Eckert, Claudia M. “Perspectives on iteration in design and development.”Research in Engineering DesignV ol. 28 No. 2 (2017): pp. 153–184

    work page 2017

  2. [2]

    Deep generative models in engineering design: A review

    Regenwetter, Lyle, Nobari, Amin Heyrani and Ahmed, Faez. “Deep generative models in engineering design: A review.”Journal of Mechanical DesignV ol. 144 No. 7 (2022): p. 071704

    work page 2022

  3. [3]

    ChatGPT [Large language model]

    OpenAI. “ChatGPT [Large language model].”https://chat.openai.com(2026)

    work page 2026

  4. [4]

    LangGraph: Build Resilient Language Agents as Graphs

    LangChain, Inc. “LangGraph: Build Resilient Language Agents as Graphs.” (2024). URL https://github. com/langchain-ai/langgraph. Open-source Python library

    work page 2024

  5. [5]

    Engineering design: a systematic approach

    Beitz, W, Pahl, G and Grote, K. “Engineering design: a systematic approach.”Mrs BulletinV ol. 71 No. 30 (1996): p. 3

    work page 1996

  6. [6]

    EngiBench: A Framework for Data-Driven Engineering Design Research

    Felten, Florian, Apaza, Gabriel, Bräunlich, Gerhard et al. “EngiBench: A Framework for Data-Driven Engineering Design Research.”The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track. 2025. URLhttps://openreview.net/forum?id=YowD33Q89V

    work page 2025

  7. [7]

    Intelligent Design 4.0: Paradigm Evolution Toward the Agentic Artificial Intelligence Era

    Jiang, Shuo, Xie, Min, Chen, Frank Youhua et al. “Intelligent Design 4.0: Paradigm Evolution Toward the Agentic Artificial Intelligence Era.”Journal of Computing and Information Science in Engineering V ol. 25 No. 12 (2025): p. 120808. doi:10.1115/1.4070438. URL https://asmedigitalcollection. asme.org/computingengineering/article-pdf/25/12/120808/7569711/...

    work page doi:10.1115/1.4070438 2025

  8. [8]

    Agentic AI: Autonomous Intelligence for Complex Goals—A Comprehensive Survey,

    Acharya, Deepak Bhaskar, Kuppan, Karthigeyan and Divya, B. “Agentic AI: Autonomous Intelli- gence for Complex Goals—A Comprehensive Survey.”IEEE AccessV ol. 13 (2025): pp. 18912–18936. doi:10.1109/ACCESS.2025.3532853

    work page doi:10.1109/access.2025.3532853 2025

  9. [9]

    Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions

    Gridach, Mourad, Nanavati, Jay, Mack, Christina et al. “Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions.”Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation. 2025. URLhttps://openreview.net/forum?id=TyCYakX9BD

    work page 2025

  10. [10]

    AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

    Wu, Qingyun, Bansal, Gagan, Zhang, Jieyu et al. “AutoGen: Enabling Next-Gen LLM Applications via Multi- Agent Conversation.”First Conference on Language Modeling (COLM). 2024. URL https://openreview. net/forum?id=BAakY1hNKS. ArXiv:2308.08155

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  11. [11]

    CrewAI: Framework for Orchestrating Role-Playing Autonomous AI Agents

    CrewAI. “CrewAI: Framework for Orchestrating Role-Playing Autonomous AI Agents.” (2024). URL https: //github.com/crewAIInc/crewAI. Open-source Python library

    work page 2024

  12. [12]

    OpenAI Agents SDK

    OpenAI. “OpenAI Agents SDK.” (2025). URL https://github.com/openai/openai-agents-python. Open-source Python library

    work page 2025

  13. [13]

    Towards an AI co-scientist

    Gottweis, Juraj, Weng, Wei-Hung, Daryin, Alexander et al. “Towards an AI co-scientist.” (2025). doi:10.48550/arXiv.2502.18864. URLhttp://arxiv.org/abs/2502.18864. ArXiv:2502.18864 [cs]

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2502.18864 2025

  14. [14]

    MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge

    Ni, Bo and Buehler, Markus J. “MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge.”Extreme Mechanics LettersV ol. 67 (2024): p. 102131. doi:https://doi.org/10.1016/j.eml.2024.102131. URL https://www.sciencedirect.com/science/ article/pii/S2352431624000117

    work page doi:10.1016/j.eml.2024.102131 2024

  15. [15]

    FeaGPT: an End-to-End agentic-AI for Finite Element Analysis

    Qi, Yupeng, Xu, Ran and Chu, Xu. “FeaGPT: an End-to-End agentic-AI for Finite Element Analysis.” (2025). doi:10.48550/arXiv.2510.21993. URLhttps://arxiv.org/abs/2510.21993. 15 EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering DesignPREPRINT

    work page doi:10.48550/arxiv.2510.21993 2025

  16. [16]

    ALL-FEM: Agentic Large Language Models Fine-Tuned for Finite Element Methods

    Deotale, Rushikesh, Srinivasan, Adithya, Tian, Yuan et al. “ALL-FEM: Agentic Large Language Models Fine-Tuned for Finite Element Methods.”SSRN Electronic Journal(2026)doi:10.2139/ssrn.6103826. URL https://ssrn.com/abstract=6103826

    work page doi:10.2139/ssrn.6103826 2026

  17. [17]

    DUCTILE: Agentic LLM Orchestration of Engineering Analysis in Product Development Practice

    Pradas-Gomez, Alejandro, Brahma, Arindam and Isaksson, Ola. “DUCTILE: Agentic LLM Orchestration of Engineering Analysis in Product Development Practice.” (2026). URL 2603.10249, URL https://arxiv. org/abs/2603.10249

    work page arXiv 2026

  18. [18]

    AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design

    Elrefaie, Mohamed, Qian, Janet, Wu, Raina et al. “AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design.”Volume 3B: 51st Design Automation Conference (DAC). 2025. American Society of Mechanical Engineers. doi:10.1115/detc2025-169682. URL http://dx.doi.org/10. 1115/DETC2025-169682

    work page doi:10.1115/detc2025-169682 2025

  19. [19]

    An LLM-based multi-agent system to assist early-stage product design and evaluation

    Chen, Pei, Cai, Yichen, Zhou, Zihong et al. “An LLM-based multi-agent system to assist early-stage product design and evaluation.”Journal of Engineering DesignV ol. 37 No. 3 (2026): pp. 945–980. doi:10.1080/09544828.2026.2616583. URL https://doi.org/10.1080/09544828.2026.2616583, URL https://doi.org/10.1080/09544828.2026.2616583

    work page doi:10.1080/09544828.2026.2616583 2026

  20. [20]

    An LLM-enabled multi-agent autonomous mechatronics design framework

    Wang, Zeyu, Lo, Frank Po Wen, Chen, Qian et al. “An LLM-enabled multi-agent autonomous mechatronics design framework.”Proceedings of the computer vision and pattern recognition conference: pp. 4205–4215. 2025

    work page 2025

  21. [21]

    Agentic Large Language Models for Conceptual Systems Engineering and Design

    Massoudi, Soheyl and Fuge, Mark. “Agentic Large Language Models for Conceptual Systems Engineering and Design.”Journal of Mechanical DesignV ol. 148 No. 5 (2026): p. 051405. doi:10.1115/1.4070328. URL https://asmedigitalcollection.asme.org/mechanicaldesign/article-pdf/148/5/051405/ 7561928/md-25-1500.pdf, URLhttps://doi.org/10.1115/1.4070328

    work page doi:10.1115/1.4070328 2026

  22. [22]

    Model Context Protocol

    Anthropic. “Model Context Protocol.” (2025). URL https://modelcontextprotocol.io/specification/ 2025-11-25

    work page 2025

  23. [23]

    MCP-Bench: A Benchmark for Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers

    Wang, Zhenting, Chang, Qi, Patel, Hemani et al. “MCP-Bench: A Benchmark for Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers.” (2025). doi:10.48550/arXiv.2504.11457. ArXiv:2504.11457

    work page doi:10.48550/arxiv.2504.11457 2025

  24. [24]

    LLM-3D print: Large Language Mod- els to monitor and control 3D printing

    Jadhav, Yayati, Pak, Peter and Barati Farimani, Amir. “LLM-3D print: Large Language Mod- els to monitor and control 3D printing.”Additive ManufacturingV ol. 114 (2025): p. 105027. doi:https://doi.org/10.1016/j.addma.2025.105027. URL https://www.sciencedirect.com/science/ article/pii/S2214860425003926

    work page doi:10.1016/j.addma.2025.105027 2025

  25. [25]

    Retrieval-augmented generation for knowledge-intensive NLP tasks

    Lewis, Patrick, Perez, Ethan, Piktus, Aleksandra et al. “Retrieval-augmented generation for knowledge-intensive NLP tasks.”Proceedings of the 34th International Conference on Neural Information Processing Systems. 2020. Curran Associates Inc., Red Hook, NY , USA

    work page 2020

  26. [26]

    Retrieval-Augmented Generation for Large Language Models: A Survey

    Gao, Yunfan, Xiong, Yun, Gao, Xinyu et al. “Retrieval-Augmented Generation for Large Language Models: A Sur- vey.” (2024). doi:10.48550/arXiv.2312.10997. URLhttp://arxiv.org/abs/2312.10997. ArXiv:2312.10997 [cs]

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2312.10997 2024

  27. [27]

    AMGPT: A large language model for contextual querying in additive manufacturing

    Chandrasekhar, Achuth, Chan, Jonathan, Ogoke, Francis et al. “AMGPT: A large language model for contextual querying in additive manufacturing.”Additive Manufacturing LettersV ol. 11 (2024): p. 100232. doi:https://doi.org/10.1016/j.addlet.2024.100232. URL https://www.sciencedirect.com/ science/article/pii/S2772369024000409

    work page doi:10.1016/j.addlet.2024.100232 2024

  28. [28]

    Zero-Shot Anomaly Detection in Laser Powder Bed Fusion Using Multimodal Retrieval-Augmented Generation and Large Language Models

    Khanghah, Kiarash Naghavi, Chen, Zhiling, Romeo, Lela et al. “Zero-Shot Anomaly Detection in Laser Powder Bed Fusion Using Multimodal Retrieval-Augmented Generation and Large Language Models.”Journal of Mechani- cal DesignV ol. 148 No. 7 (2025): p. 072001. doi:10.1115/1.4070585. URLhttps://asmedigitalcollection. asme.org/mechanicaldesign/article-pdf/148/7...

    work page doi:10.1115/1.4070585 2025

  29. [29]

    Evaluation and Benchmarking of LLM Agents: A Survey

    Mohammadi, Mahmoud, Li, Yipeng, Lo, Jane et al. “Evaluation and Benchmarking of LLM Agents: A Survey.” Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V .2: p. 6129–6139

    work page

  30. [30]

    Evaluation and Benchmarking of LLM Agents: A Survey , url=

    Association for Computing Machinery, New York, NY , USA. doi:10.1145/3711896.3736570. URL https://doi.org/10.1145/3711896.3736570

    work page doi:10.1145/3711896.3736570

  31. [31]

    ACEBench: A Comprehensive Evaluation of LLM Tool Usage

    Chen, Chen, Hao, Xinlong, Liu, Weiwen, Huang, Xu, Zeng, Xingshan, Yu, Shuai, Li, Dexun, Huang, Yuefeng, Liu, Xiangcheng, Xinzhi, Wang and Liu, Wu. “ACEBench: A Comprehensive Evaluation of LLM Tool Usage.” Christodoulopoulos, Christos, Chakraborty, Tanmoy, Rose, Carolyn and Peng, Violet (eds.).Findings of the Association for Computational Linguistics: EMNL...

    work page doi:10.18653/v1/2025.findings-emnlp.697 2025

  32. [32]

    ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities

    Lu, Jiarui, Holleis, Thomas, Zhang, Yizhe et al. “ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities.” Chiruzzo, Luis, Ritter, Alan and Wang, Lu (eds.).Findings of the Association for Computational Linguistics: NAACL 2025: pp. 1160–1183. 2025. Association for Computational Linguistics, Albuquerque, New ...

    work page doi:10.18653/v1/2025.findings-naacl.65 2025

  33. [33]

    The Berkeley Function Calling Leaderboard (BFCL): From Tool Use to Agentic Evaluation of Large Language Models

    Patil, Shishir G, Mao, Huanzhi, Yan, Fanjia et al. “The Berkeley Function Calling Leaderboard (BFCL): From Tool Use to Agentic Evaluation of Large Language Models.”Forty-second International Conference on Machine Learning. 2025. URLhttps://openreview.net/forum?id=2GmDdhBdDk

    work page 2025

  34. [34]

    ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

    Qin, Yujia, Liang, Shihao, Ye, Yining et al. “ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs.”The Twelfth International Conference on Learning Representations. 2024. URL https: //openreview.net/forum?id=dHng2O0Jjr

    work page 2024

  35. [35]

    AgentBench: Evaluating LLMs as Agents

    Liu, Xiao, Yu, Hao, Zhang, Hanchen et al. “AgentBench: Evaluating LLMs as Agents.”The Twelfth International Conference on Learning Representations. 2024. URLhttps://openreview.net/forum?id=zAdUB0aCTQ

    work page 2024

  36. [36]

    NeurIPS / arXiv preprint 2401.13178

    Ma, Chang, Zhang, Junlei, Zhu, Zhihao et al. “AgentBoard: An Analytical Evaluation Board of Multi- turn LLM Agents.” (2024). doi:10.48550/arXiv.2401.13178. URL http://arxiv.org/abs/2401.13178. ArXiv:2401.13178 [cs]

    work page doi:10.48550/arxiv.2401.13178 2024

  37. [37]

    ScienceAgentBench: Toward Rigorous Assessment of Lan- guage Agents for Data-Driven Scientific Discovery

    Chen, Ziru, Chen, Shijie, Ning, Yuting et al. “ScienceAgentBench: Toward Rigorous Assessment of Lan- guage Agents for Data-Driven Scientific Discovery.”The Thirteenth International Conference on Learning Representations. 2025. URLhttps://openreview.net/forum?id=6z4YKr0GK6

    work page 2025

  38. [38]

    FDM-bench: a domain-specific benchmark for evaluating large language models in additive manufacturing

    Eslaminia, Ahmadreza, Jackson, Adrian, Tian, Beitong et al. “FDM-bench: a domain-specific benchmark for evaluating large language models in additive manufacturing.”Manufacturing LettersV ol. 44 (2025): pp. 1415–

    work page 2025

  39. [39]

    URL https://www.sciencedirect.com/science/ article/pii/S2213846325001968

    doi:https://doi.org/10.1016/j.mfglet.2025.06.161. URL https://www.sciencedirect.com/science/ article/pii/S2213846325001968. 53rd SME North American Manufacturing Research Conference (NAMRC 53)

    work page doi:10.1016/j.mfglet.2025.06.161 2025

  40. [40]

    EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving

    Zhou, Xiyuan, Wang, Xinlei, He, Yirui et al. “EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving.” (2025). doi:10.48550/arXiv.2509.17677. URL http://arxiv.org/abs/ 2509.17677. ArXiv:2509.17677 [cs]

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2509.17677 2025

  41. [41]

    τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

    Yao, Shunyu, Shinn, Noah, Razavi, Pedram et al. “ τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains.”The Thirteenth International Conference on Learning Representations. 2025. URL https://openreview.net/forum?id=roNSXZpUDN

    work page 2025

  42. [42]

    WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models

    He, Hongliang, Yao, Wenlin, Ma, Kaixin et al. “WebV oyager: Building an End-to-End Web Agent with Large Multimodal Models.”arXiv preprint arXiv:2401.13919(2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  43. [43]

    Mind2Web: Towards a Generalist Agent for the Web

    Deng, Xiang, Gu, Yu, Zheng, Boyuan et al. “Mind2Web: Towards a Generalist Agent for the Web.” (2023). doi:10.48550/arXiv.2306.06070. URLhttp://arxiv.org/abs/2306.06070. ArXiv:2306.06070 [cs]

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2306.06070 2023

  44. [44]

    LangChain

    Chase, Harrison. “LangChain.” (2022). URLhttps://github.com/langchain-ai/langchain

    work page 2022

  45. [45]

    M(M)ORE : Massive Multimodal Open RAG & Extraction

    Sallinen, Alexandre, Krsteski, Stefan, Teiletche, Paul et al. “M(M)ORE : Massive Multimodal Open RAG & Extraction.”Championing Open-source DEvelopment in ML Workshop @ ICML25. 2025. URL https: //openreview.net/forum?id=6j1HjfIdKn

    work page 2025

  46. [46]

    A 99 line topology optimization code written in Matlab.Structural and Multidisciplinary Optimization, 21(2):120–127, 2001

    Sigmund, O. “A 99 line topology optimization code written in Matlab.”Struct. Multidiscip. Optim.V ol. 21 No. 2 (2001): p. 120–127. doi:10.1007/s001580050176. URLhttps://doi.org/10.1007/s001580050176

    work page doi:10.1007/s001580050176 2001

  47. [47]

    Lazarov, and Ole Sig- mund

    Andreassen, Erik, Clausen, Anders, Schevenels, Mattias et al. “Efficient topology optimization in MAT- LAB using 88 lines of code.”Structural and Multidisciplinary OptimizationV ol. 43 No. 1 (2011): pp. 1–16. doi:10.1007/s00158-010-0594-7. URLhttp://link.springer.com/10.1007/s00158-010-0594-7

    work page doi:10.1007/s00158-010-0594-7 2011

  48. [48]

    OpenAI GPT-5 System Card

    Singh, Aaditya, Fry, Adam, Perelman, Adam et al. “OpenAI GPT-5 System Card.” (2025). URL2601.03267, URLhttps://arxiv.org/abs/2601.03267

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  49. [49]

    Qwen3 Technical Report

    Yang, An, Li, Anfeng, Yang, Baosong et al. “Qwen3 Technical Report.” (2025). doi:10.48550/arXiv.2505.09388. URLhttp://arxiv.org/abs/2505.09388. ArXiv:2505.09388 [cs]

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2505.09388 2025

  50. [50]

    SOPTX: A High-Performance Multi-Backend Framework for Topol- ogy Optimization

    He, Liang, Wei, Huayi and Tian, Tian. “SOPTX: A High-Performance Multi-Backend Framework for Topol- ogy Optimization.” (2025). doi:10.48550/arXiv.2505.02438. URL http://arxiv.org/abs/2505.02438. ArXiv:2505.02438 [math]

    work page doi:10.48550/arxiv.2505.02438 2025

  51. [51]

    DesignQA: A Multimodal Bench- mark for Evaluating Large Language Models’ Understanding of Engineering Documentation

    Doris, Anna C., Grandi, Daniele, Tomich, Ryan et al. “DesignQA: A Multimodal Bench- mark for Evaluating Large Language Models’ Understanding of Engineering Documentation.”Jour- nal of Computing and Information Science in EngineeringV ol. 25 No. 2 (2024): p. 021009. 17 EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering DesignPRE...

    work page doi:10.1115/1.4067333 2024

  52. [52]

    “Codex.” (2025)

    OpenAI. “Codex.” (2025). URLhttps://openai.com/codex/

    work page 2025

  53. [53]

    Claude Code

    Anthropic. “Claude Code.” (2025). URLhttps://docs.anthropic.com/en/docs/claude-code

    work page 2025

  54. [54]

    OpenClaw: An Open-Source Agentic Coding Framework

    OpenClaw Contributors. “OpenClaw: An Open-Source Agentic Coding Framework.” (2025). URL https: //github.com/openclaw/openclaw

    work page 2025

  55. [55]

    Saaty, The analytic hierarchy process—wh at it is and how it is used, Mathematical Modelling 9 (1987) 161–176

    Saaty, R.W. “The analytic hierarchy process—what it is and how it is used.”Mathematical ModellingV ol. 9 No. 3 (1987): pp. 161–176. doi:https://doi.org/10.1016/0270-0255(87)90473-8. URL https://www.sciencedirect. com/science/article/pii/0270025587904738. A Scoring Methodology A.1 Design Quality Metrics The design quality score is a weighted combination of...

    work page doi:10.1016/0270-0255(87)90473-8 1987

  56. [58]

    Post-processing & Export - Thresholding: Apply a 0.58 density threshold to convert the continuous density map into binary geometry - Mirror: Mirror the design across the y-axis for the final geometry - XY Scaling: Scale the X and Y dimensions by 2.47 - Extrusion: Extrude the 2D result by 17.9 units in the Z-axis to create a 3D volume - Export: Save the fi...

    work page

  57. [61]

    Post-processing & Export The STL export parameters must be derived from the optimization inputs: - Thresholding: Use the volume fraction value as the density threshold 20 EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering DesignPREPRINT - Mirror: Mirror the design across the y-axis only if the volume fraction is greater than 0....

    work page

  58. [64]

    Post-processing & Export - Threshold the density field at 0.42 to preview the design topology - Apply a 0.58 density threshold to produce the final solid/void geometry - Scale the preview display by 1.76x in XY for quick inspection - Scale the X and Y dimensions of the part by 2.47 for manufacturing - Mirror the design across the y-axis for the final geom...

    work page

  59. [67]

    Post-processing & Export (conditional on compliance) - If compliance > 254.8: - Thresholding: Apply a 0.48 density threshold to convert the continuous density map into binary geometry - Mirror: Mirror the design across the y-axis for the final geometry - If compliance <= 254.8: - Thresholding: Apply a 0.64 density threshold to convert the continuous densi...

    work page

  60. [68]

    Optimization Configuration - Volume Fraction: 0.4 - Force Distance: 0.65 - Filter Radius (rmin): 4.0 - Objective: Minimize compliance

    work page

  61. [69]

    Simulation - After optimization, simulate the design to obtain the compliance value

    work page

  62. [70]

    Post-processing & Export Export A: - Thresholding: Apply a 0.48 density threshold to convert the continuous density map into binary geometry - Mirror: Mirror the design across the y-axis for the final geometry - XY Scaling: Scale the X and Y dimensions by 3.64 21 EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering DesignPREPRINT...

    work page 2025

  63. [71]

    Use the volume fraction and force distance from the EngiBench paper’s API walkthrough example (the non-default values shown in the code snippet)

    work page

  64. [72]

    (2025) for their 2D cantilever beam benchmark

    Use the filter radius from the SOPTX paper by He et al. (2025) for their 2D cantilever beam benchmark. Search the relevant papers to find each value, then generate a 2D beam design using exactly those three parameters. Use default values for all other parameters and do not ask for clarification. D Supplementary Results D.1 Diffusion Model Results Figure 9...

    work page 2025