LLMs are General Asynchronous Agents
Modern LLMs are increasingly capable as autonomous agents, but they follow sequential interaction cycles: read, think, reply or call tools, repeat. Many real-world use cases are not sequential: voice assistants, embodied agents, and monitoring systems receive new inputs while they think or perform another task. Modern LLMs address this with specialized architectures for voice interaction and video streams, VLAs for robot control, asynchronous tool calling for API usage, and others. In this work, we generalize from different asynchronous tasks to general asynchronous agents that can adapt to different types of concurrency. To achieve this, we develop an asynchronous LLM framework that lets users (or the agents themselves) define inference coroutines with overlapping memory states. We showcase that Qwen 3.x models are capable of asynchronous operation for streaming video understanding, videogames, and monitoring, without task-specific training.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games
LLMs are used predominantly in synchronous communication, where a human user and a model communicate in alternating turns. In contrast, many real-world settings are inherently asynchronous. For example, in group chats, o…
Action GenerationAsynchronous Group CommunicationGame of MafiaScheduling+1Asynchronous Tool Usage for Real-Time Agents
While frontier large language models (LLMs) are capable tool-using agents, current AI systems still operate in a strict turn-based fashion, oblivious to passage of time. This synchronous design forces user queries and to…
Automatic Speech Recognitionspeech-recognitionSpeech Recognitiontext-to-speech+1Target Driven Visual Navigation with Hybrid Asynchronous Universal Successor Representations
Being able to navigate to a target with minimal supervision and prior knowledge is critical to creating human-like assistive agents. Prior work on map-based and map-less approaches have limited generalizability. In this …
NavigateVisual NavigationAsynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Synchronizing decisions across multiple agents in realistic settings is problematic since it requires agents to wait for other agents to terminate and communicate about termination reliably. Ideally, agents should learn …
Decision MakingMulti-agent Reinforcement LearningPolicy Gradient Methodsreinforcement-learning+2Asynchronous Adaptation and Learning over Networks --- Part I: Modeling and Stability Analysis
In this work and the supporting Parts II [2] and III [3], we provide a rather detailed analysis of the stability and performance of asynchronous strategies for solving distributed optimization and adaptation problems ove…
Distributed Optimization