TO-Agents: A Multi-Agent AI Pipeline for Preference-Guided Topology Optimization

• URL: http://arxiv.org/abs/2605.21622v1
• Date: Wed, 20 May 2026 18:32:56 GMT
• Title: TO-Agents: A Multi-Agent AI Pipeline for Preference-Guided Topology Optimization
• Abstract summary: We present TO-Agents, a multi-agent AI framework that connects natural-language design intent with iterative topology optimization.<n>The framework converts a human-provided problem description into validated solver inputs, runs a topology optimization solver, and renders the resulting 3D topology.<n>We evaluate the framework on two long-horizon design tasks: a cantilever beam benchmark and a phone-stand product design.
• Score: 6.670146926866281
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Topology optimization can generate efficient structures, but designers often must manually translate qualitative intent, such as desired visual style, product experience, or manufacturability into solver settings that are not directly tied to those preferences. We present TO-Agents, a multi-agent AI framework that connects natural-language design intent with iterative topology optimization. The framework converts a human-provided problem description into validated solver inputs, runs a topology optimization solver, renders the resulting 3D topology, and uses multi-view vision-language reasoning with an independent judge agent to critique each result and revise solver parameters. We evaluate the framework on two long-horizon design tasks: a cantilever beam benchmark and a phone-stand product design. In both tasks, the designer specifies an aesthetic preference for hierarchically branched structures inspired by natural tree morphologies, and the system performs four revision cycles across ten independent replicates. TO-Agents produces at least one preference-aligned design in 60% of trials for each case study, corresponding to up to 6x more successful trials than an ablated pipeline without visual or historical feedback. Judge scores and human evaluations show that the pipeline can identify effective parameter levers, recover from poor revisions, and expand design exploration. A manufacturing agent further post-processes top-ranked designs for additive manufacturing, enabling end-to-end intent-to-prototype design. We also identify failure modes, including overshooting, selective memory, misplaced tools, and incorrect parameter reasoning. These results suggest that agentic topology optimization can shift designers from low-level parameter tuning toward higher-level specification of form and function, while highlighting safeguards needed for reliable autonomous engineering design.

Related papers

• Agentic Designer: Progressive Multi-Agent Collaboration for Structure-Aware Interior Layout Generation [84.5255199507027]
  Agentic Designer is a progressive framework that formulates structure-aware interior layout generation as an iterative and constraint-verified decision process.<n>By layout decomposing into modular stages of proposal, verification, synthesis and adjustment, the framework coordinates three specialized agents, a Generator, an Evaluator, and a Refiner.<n>This mechanism enforces stepwise geometric validation and correction before each placement is committed, thereby preventing error accumulation.
  arXiv  Detail & Related papers  (2026-07-23T02:46:11Z)
• DaV-Gen: End-to-End Generative Retrieval via Draft-and-Verify [4.546259713278263]
  textbfDaV-Gen is a novel unified solution designed to fundamentally the paradigm for both search and recommendation.<n>During training, the model is concurrently optimized for both candidate drafting and fine-grained verification.<n>This is achieved by a composite loss function that jointly trains the model on two distinct but related objectives.
  arXiv  Detail & Related papers  (2026-07-09T11:22:50Z)
• OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement [0.0]
  OptiAgent is a multi-agent framework that is able to output a solver-ready mathematical formulation as well as executable code.<n>Our architecture prioritizes the mathematical modeling step, where dedicated agents extract structures.<n>We introduce a novel multi-loop validation architecture with four specialized feedback mechanisms, each targeting a distinct failure mode.
  arXiv  Detail & Related papers  (2026-07-06T17:27:16Z)
• Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement [66.51979814832332]
  model formulates procedural graph extraction as a multi-round reasoning process with dedicated structural and logical refinement.<n>Experiments demonstrate that model achieves substantial improvements in both structural correctness and logical consistency over strong baselines.
  arXiv  Detail & Related papers  (2026-01-27T04:00:48Z)
• Zero-shot 3D Map Generation with LLM Agents: A Dual-Agent Architecture for Procedural Content Generation [8.398818816613806]
  We propose a training-free architecture that utilizes LLM agents for zero-shot PCG parameter configuration.<n>Our system pairs an Actor agent with a Critic agent, enabling an iterative workflow where the system autonomously reasons over tool parameters.
  arXiv  Detail & Related papers  (2025-12-11T10:22:02Z)
• A Multidisciplinary Design and Optimization (MDO) Agent Driven by Large Language Models [7.509742996975501]
  We present a Multidisciplinary Design and Optimization (MDO) Agent driven by Large Language Models (LLMs)<n>The agent semi-automates the end-to-end workflow by orchestrating three core capabilities: (i) natural-language-driven parametric modeling, (ii) retrieval-augmented generation (RAG) for knowledge-grounded conceptualization, and (iii) intelligent orchestration of engineering software for performance verification and optimization.
  arXiv  Detail & Related papers  (2025-10-06T01:26:55Z)
• Attention is also needed for form design [0.0]
  This research introduces a novel, attention-aware framework that integrates two systems: EURIA, an immersive Virtual Reality environment using eye-tracking, and RETina, an agentic AI pipeline.<n>The proposed framework elevates the designer's role to that of a creative director, synergizing human intuition with the generative power of agentic AI to produce higher-quality designs more efficiently.
  arXiv  Detail & Related papers  (2025-08-27T09:15:21Z)
• Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation [91.17994756436259]
  Multi-agent systems (MAS) based on large language models (LLMs) have emerged as a powerful solution for dealing with complex problems across diverse domains.<n>Existing approaches are fundamentally constrained by their reliance on a template graph modification paradigm with a predefined set of agents and hard-coded interaction structures.<n>We propose ARG-Designer, a novel autoregressive model that operationalizes this paradigm by constructing the collaboration graph from scratch.
  arXiv  Detail & Related papers  (2025-07-24T09:17:41Z)
• Vehicle Suspension Recommendation System: Multi-Fidelity Neural Network-based Mechanism Design Optimization [4.038368925548051]
  Vehicle suspensions are designed to improve driving performance and ride comfort, but different types are available depending on the environment. Traditional design process is multi-step, gradually reducing the number of design candidates while performing costly analyses to meet target performance. Recently, AI models have been used to reduce the computational cost of FEA.
  arXiv  Detail & Related papers  (2024-10-03T23:54:03Z)
• Compositional Generative Inverse Design [69.22782875567547]
  Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem. We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
  arXiv  Detail & Related papers  (2024-01-24T01:33:39Z)
• Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
  This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE) The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
  arXiv  Detail & Related papers  (2022-11-29T17:28:31Z)
• Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration [71.95914457415624]
  Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. We propose an alternative formulation that leverages Multi-Agent RL (MARL) to tackle this problem. Our evaluation shows that the MARL formulation consistently outperforms single-agent RL baselines.
  arXiv  Detail & Related papers  (2022-11-29T17:10:24Z)
• Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques [55.492211642128446]
  Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. Model-based computational design algorithms assist designers by generating design examples during design. Black box methods for assistance, on the other hand, can work with any design problem.
  arXiv  Detail & Related papers  (2022-04-15T20:40:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.