5 papers · 1 filter
R-APS: Compositional Reasoning and In-Context Meta-Learning for Constrained Design via Reflective Adversarial Pareto Search
João Pedro Gandarela, Thiago Rios, Stefan Menzel +1
Large language models (LLMs) are fluent on open-ended tasks, yet in agentic settings, where a system must plan, use tools, and act over extended horizons, fluency does not ensure r…
Symbolic Intermediaries as a Linguistic-Numerical Interface for LLM-Driven Geometric Reasoning
João Pedro Gandarela, Thiago Rios, Stefan Menzel +1
Large Language Models (LLMs) display reasoning capabilities over linguistic and symbolic objects but have limited capabilities to directly interpret the continuous numerical output…
Language Models Refine Mechanical Linkage Designs Through Symbolic Reflection and Modular Optimisation
João Pedro Gandarela, Thiago Rios, Stefan Menzel +1
Designing mechanical linkages involves combinatorial topology selection and continuous parameter fitting. We show that language models can systematically improve linkage designs th…
LLM2TEA: An Agentic AI Designer for Discovery with Generative Evolutionary Multitasking
Melvin Wong, Jiao Liu, Thiago Rios +2
This paper presents LLM2TEA, a Large Language Model (LLM) driven MultiTask Evolutionary Algorithm, representing the first agentic AI designer of its kind operating with generative…
From Idea to CAD: A Language Model-Driven Multi-Agent System for Collaborative Design
Felix Ocker, Stefan Menzel, Ahmed Sadik +1
Creating digital models using Computer Aided Design (CAD) is a process that requires in-depth expertise. In industrial product development, this process typically involves entire t…