Summary of Tulip Agent — Enabling Llm-based Agents to Solve Tasks Using Large Tool Libraries, by Felix Ocker et al.
Tulip Agent – Enabling LLM-Based Agents to Solve Tasks Using Large Tool Libraries
by Felix Ocker, Daniel Tanneberg, Julian Eggert, Michael Gienger
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed tulip agent architecture for autonomous large language model (LLM)-based agents enables efficient and scalable access to a tool library containing a potentially large number of tools. Unlike existing implementations, the tulip agent does not require encoding all available tool descriptions within its system prompt or embedding the entire prompt for retrieving suitable tools. Instead, it can recursively search the extensible tool library, implemented as a vector store, which significantly reduces inference costs and enables using even large tool libraries. This architecture also allows the agent to adapt and extend its set of tools. The tulip agent is evaluated with several ablation studies in a mathematics context, demonstrating generalizability with an application to robotics. The proposed architecture has potential applications in various domains, including human-robot collaboration, where it can enable more efficient and adaptable tool usage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Tulip agent is a new way for computers to work together with tools without needing to know all the details about those tools. It’s like having a super-smart librarian that helps you find what you need quickly and efficiently. This system uses something called large language models, which are very good at understanding and generating human-like text. The tulip agent is better than other systems because it doesn’t need to store all the information about each tool in its memory. Instead, it can look up the tools it needs as it goes along, which makes it much faster and more useful. This could be really helpful for things like robots that need to use different tools for different tasks. |
Keywords
» Artificial intelligence » Embedding » Inference » Large language model » Prompt
