Cooperative Design Optimization through Natural Language Interaction
- URL: http://arxiv.org/abs/2508.16077v1
- Date: Fri, 22 Aug 2025 04:12:03 GMT
- Title: Cooperative Design Optimization through Natural Language Interaction
- Authors: Ryogo Niwa, Shigeo Yoshida, Yuki Koyama, Yoshitaka Ushiku,
- Abstract summary: We propose a design optimization framework that enables natural language interactions between designers and the optimization system.<n>We show that our method provides higher user agency than a system-led method and shows promising optimization performance compared to manual design.
- Score: 14.689289362271246
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Designing successful interactions requires identifying optimal design parameters. To do so, designers often conduct iterative user testing and exploratory trial-and-error. This involves balancing multiple objectives in a high-dimensional space, making the process time-consuming and cognitively demanding. System-led optimization methods, such as those based on Bayesian optimization, can determine for designers which parameters to test next. However, they offer limited opportunities for designers to intervene in the optimization process, negatively impacting the designer's experience. We propose a design optimization framework that enables natural language interactions between designers and the optimization system, facilitating cooperative design optimization. This is achieved by integrating system-led optimization methods with Large Language Models (LLMs), allowing designers to intervene in the optimization process and better understand the system's reasoning. Experimental results show that our method provides higher user agency than a system-led method and shows promising optimization performance compared to manual design. It also matches the performance of an existing cooperative method with lower cognitive load.
Related papers
- Accelerated Gradient-based Design Optimization Via Differentiable Physics-Informed Neural Operator: A Composites Autoclave Processing Case Study [0.0]
We introduce a novel Physics-Informed DeepONet (PIDON) architecture to effectively model the nonlinear behavior of complex engineering systems.<n>We demonstrate the effectiveness of this framework in the optimization of aerospace-grade composites curing processes achieving a 3x speedup.<n>The proposed model has the potential to be used as a scalable and efficient optimization tool for broader applications in advanced engineering and digital twin systems.
arXiv Detail & Related papers (2025-02-17T07:11:46Z) - A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
arXiv Detail & Related papers (2024-12-18T14:11:15Z) - Learning Joint Models of Prediction and Optimization [56.04498536842065]
Predict-Then-Then framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving.
This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by joint predictive models.
arXiv Detail & Related papers (2024-09-07T19:52:14Z) - ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models [5.642568057913696]
This paper presents ADO-LLM, the first work integrating large language models (LLMs) with Bayesian Optimization for analog design optimization.<n>ADO-LLM leverages the LLM's ability to infuse domain knowledge to rapidly generate viable design points to remedy BO's inefficiency in finding high value design areas.<n>We evaluate the proposed framework on two different types of analog circuits and demonstrate notable improvements in design efficiency and effectiveness.
arXiv Detail & Related papers (2024-06-26T21:42:50Z) - Enhanced Bayesian Optimization via Preferential Modeling of Abstract
Properties [49.351577714596544]
We propose a human-AI collaborative Bayesian framework to incorporate expert preferences about unmeasured abstract properties into surrogate modeling.
We provide an efficient strategy that can also handle any incorrect/misleading expert bias in preferential judgments.
arXiv Detail & Related papers (2024-02-27T09:23:13Z) - 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) - ECoDe: A Sample-Efficient Method for Co-Design of Robotic Agents [11.449817465618658]
Co-designing autonomous robotic agents involves simultaneously optimizing the controller and physical design of the agent.
This can be challenging when the design space is large.
We propose a multi-fidelity-based exploration strategy to improve the sample efficiency of co-design.
arXiv Detail & Related papers (2023-09-08T02:54:31Z) - Fusion of ML with numerical simulation for optimized propeller design [0.6767885381740952]
We propose an alternative way to use ML model to surrogate the design process.
By using this trained surrogate model with the traditional optimization method, we can get the best of both worlds.
Empirical evaluations of propeller design problems show that a better efficient design can be found in fewer evaluations using SAO.
arXiv Detail & Related papers (2023-02-28T16:42:07Z) - 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) - Bayesian Optimization for Selecting Efficient Machine Learning Models [53.202224677485525]
We present a unified Bayesian Optimization framework for jointly optimizing models for both prediction effectiveness and training efficiency.
Experiments on model selection for recommendation tasks indicate models selected this way significantly improves model training efficiency.
arXiv Detail & Related papers (2020-08-02T02:56:30Z)
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.