Summary of Asynchronous Tool Usage For Real-time Agents, by Antonio A. Ginart et al.
Asynchronous Tool Usage for Real-Time Agents
by Antonio A. Ginart, Naveen Kodali, Jason Lee, Caiming Xiong, Silvio Savarese, John Emmons
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel approach is proposed to overcome the limitations of current large language models (LLMs) by introducing asynchronous AI agents that can parallel-process and use tools in real-time. The synchronous design of existing LLMs restricts user queries and tool-use to sequential turns, hindering interactivity. To address this, an event-driven finite-state machine architecture is developed for agent execution and prompting, integrated with automatic speech recognition and text-to-speech. This work presents both a conceptual framework and practical tools for creating AI agents capable of fluid, multitasking interactions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers have created new language models that can do things in parallel, like having conversations while doing tasks. Right now, these models are stuck in a turn-based mode where you ask a question and then wait for an answer before moving on to the next thing. This makes it hard for people to interact with them naturally. To solve this problem, scientists have designed new AI agents that can do things at the same time and use tools instantly. They created a special system that lets these agents process events and give responses quickly, like recognizing speech and speaking back. |
Keywords
» Artificial intelligence » Prompting
