Today's LLMs face limitations in handling multiple tasks simultaneously due to their synchronous operations. A new framework developed by Salesforce AI Research addresses this by introducing an event-driven finite-state machine for real-time, asynchronous AI agent interaction. The architecture integrates speech recognition, text-to-speech capabilities, and priority scheduling, enabling agents to manage conversational states and multitask effectively. This enhances interactivity and responsiveness in AI applications, with fine-tuned models like Llama 3.1 and GPT-4o showing improved performance. Future improvements may include integration with multi-modal models to further reduce latency.
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