In the high-cost simulation-driven design domain, translating ambiguous design requirements into a mathematical optimization formulation is a bottleneck for optimizing product performance. This process is time-consuming and heavily reliant on expert knowledge. While large language models (LLMs) offer potential for automating this task, existing approaches either suffer from poor formalization that fails to accurately align with the design intent or rely on solver feedback for data filtering, which is unavailable due to the high simulation costs. To address this challenge, we propose automated problem formulation (APF), a solver-independent framework that utilizes LLMs to convert engineers’ natural language requirements into executable optimization models. The core of this framework is an innovative pipeline for automatically generating high-quality data, which overcomes the difficulty of constructing suitable fine-tuning datasets in the absence of high-cost solver feedback with the help of data generation and test instance annotation. The generated high-quality dataset is used to perform supervised fine-tuning on LLMs, significantly enhancing their ability to generate accurate and executable optimization problem formulations. Experimental results on antenna design demonstrate that APF significantly outperforms the existing methods in both the accuracy of requirement formalization and the quality of resulting radiation efficiency curves in meeting the design goals.

Scientific innovation is pivotal for humanity, and harnessing large language models (LLMs) to generate research ideas could transform discovery. However, existing LLMs often produce simplistic and repetitive suggestions due to their limited ability in acquiring external knowledge for innovation. To address this problem, we introduce an enhanced planning and search methodology designed to boost the creative potential of LLM-based systems. Our approach involves an iterative process to purposely plan the retrieval of external knowledge, progressively enriching the idea generation with broader and deeper insights. Validation through automated and human assessments demonstrates that our framework substantially elevates the quality of generated ideas, particularly in novelty and diversity. The number of unique novel ideas produced by our framework is 3.4 times higher than without it. Moreover, our method outperforms the current state-of-the-art, generating at least 2.5 times more top-rated ideas based on 170 seed papers in a Swiss Tournament evaluation. Our code is available at https://github.com/hflyzju/Nova

Existing studies on relation extraction focus at the document level in a centralized training environment, requiring the collection of documents from various sources. However, this raises concerns about privacy protection, especially in sensitive domains such as finance and healthcare. For the first time, this work extends document-level relation extraction to a federated environment. The proposed federated framework, called FedLCC, is tailored for biomedical relation extraction that enables collaborative training without sharing raw medical texts. To fully exploit the models of all participating clients and improve the local training on individual clients, we propose a novel concept of localized context contrast on the basis of contrastive learning. By comparing and rectifying the similarity of localized context in documents between clients and the central server, the global model can better represent the documents on individual clients. Due to the lack of a widely accepted measure of non-IID text data, we introduce a novel non-IID scenario based on graph structural entropy. Experimental results on three document-level biomedical relation extraction datasets demonstrate the effectiveness of our method. Our code is available at https://github.com/xxxxyan/FedLCC.