Large language models (LLMs) have emerged as powerful tools in chemistry, significantly
impacting molecule design, property prediction, and synthesis optimization. This review …

Performing language-conditioned robotic manipulation tasks in unstructured environments
is highly demanded for general intelligent robots. Conventional robotic manipulation …

Combining Large Language Models (LLMs) with search engine services marks a significant
shift in the field of services computing, opening up new possibilities to enhance how we …

Contemporary evaluation techniques are inadequate for agentic systems. These
approaches either focus exclusively on final outcomes--ignoring the step-by-step nature of …

Data-driven scientific discovery requires the iterative integration of scientific domain
knowledge, statistical expertise, and an understanding of data semantics to make nuanced …

The rapid advancement of Large Language Models (LLMs) has significantly influenced
various domains, leveraging their exceptional few-shot and zero-shot learning capabilities …

Automated machine learning (AutoML) accelerates AI development by automating tasks in
the development pipeline, such as optimal model search and hyperparameter tuning …

Given that Large Language Models (LLMs) have made significant progress in writing code,
can they now be used to autonomously reproduce results from research repositories? Such …

The application of ChatGPTin the medical field has sparked debate regarding its accuracy.
To address this issue, we present a Multi-Role ChatGPT Framework (MRCF), designed to …

Recent advances in large language models (LLMs) have opened new avenues for applying
multi-agent systems in very large-scale simulations. However, there remain several …