Language model post-training is applied to refine behaviors and unlock new skills across a
wide range of recent language models, but open recipes for applying these techniques lag …

Tool learning empowers large language models (LLMs) as agents to use external tools and
extend their utility. Existing methods employ one single LLM-based agent to iteratively select …

Large Language Models (LLMs) represent a class of deep learning models adept at
understanding natural language and generating coherent responses to various prompts or …

Foundation model-enabled generative artificial intelligence facilitates the development and
implementation of agents, which can leverage distinguished reasoning and language …

Tool learning aims to enhance and expand large language models'(LLMs) capabilities with
external tools, which has gained significant attention recently. Current methods have shown …

Recently, integrating external tools with Large Language Models (LLMs) has gained
significant attention as an effective strategy to mitigate the limitations inherent in their pre …

Function calling significantly extends the application boundary of large language models,
where high-quality and diverse training data is critical for unlocking this capability. However …

The rapid adoption of large language models (LLMs) in multi-agent systems has highlighted
their impressive capabilities in various applications, such as collaborative problem-solving …

We introduce Agent K v1. 0, an end-to-end autonomous data science agent designed to
automate, optimise, and generalise across diverse data science tasks. Fully automated …

Solving complex real-world tasks requires cycles of actions and observations. This is
particularly true in science, where tasks require many cycles of analysis, tool use, and …