How to align asynchronous multi-agent features?
TraF-Align: Trajectory-aware Feature Alignment for Asynchronous Multi-agent Perception
This paper introduces TraF-Align, a new method for improving multi-agent perception in scenarios where time delays between agents' observations cause misaligned data. It addresses the challenge of fusing real-time data with delayed information from other agents, especially in autonomous vehicle scenarios. TraF-Align predicts the likely path (trajectory) of objects based on past observations, which helps align features from different times and viewpoints, improving accuracy and robustness to latency.
Key points relevant to LLM-based multi-agent systems: TraF-Align's trajectory prediction and attention mechanism could be adapted to manage information flow and consistency in multi-agent LLM systems. The focus on handling asynchronous communication is also relevant, as LLM agents may operate at different speeds or with varying latency. The paper's insights on spatial and semantic misalignment are directly applicable to the challenges of aligning and interpreting information generated by multiple LLMs.