To solve complex tasks, large language models (LLMs) often require multiple rounds of
interactions with the user, sometimes assisted by external tools. However, current evaluation …

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

Clinicians often rely on data engineers to retrieve complex patient information from
electronic health record (EHR) systems, a process that is both inefficient and time …

We introduce Eurus, a suite of large language models (LLMs) optimized for reasoning.
Finetuned from Mistral-7B and CodeLlama-70B, Eurus models achieve state-of-the-art …

The development of software agents that can autonomously take actions to achieve goals
has been a long-standing foundational objective in the field of AI. Recent advances in …

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) excel in stand-alone code tasks like HumanEval and
MBPP, but struggle with handling entire code repositories. This challenge has prompted …

We present SOLO, a single transformer for Scalable visiOn-Language mOdeling. Current
large vision-language models (LVLMs) such as LLaVA mostly employ heterogeneous …

Large language models (LLMs) support data analysis through conversational user
interfaces, as exemplified in OpenAI's ChatGPT (formally known as Advanced Data Analysis …

Recent large language models (LLMs) advancements sparked a growing research interest
in tool assisted LLMs solving real-world challenges, which calls for comprehensive …