Multi-scale Temporal Prediction via Incremental Generation and Multi-agent Collaboration

2025-09-22

AI Summary

This paper addresses the challenge of jointly modeling cross-horizon temporal dependencies (short- vs. long-term) and multi-granular state representations (coarse- vs. fine-grained) in multi-scale time-series prediction. To this end, we propose an incremental generative multi-agent collaborative framework: (1) an incremental generation module enables dynamic synchronization between visual preview and decision-making; (2) a decision-driven multi-agent coordination mechanism balances global coherence and local fidelity; and (3) a unified architecture integrates vision-language modeling with hierarchical state prediction to jointly model human behavior and procedural states across general and surgical scenarios. We further introduce the first benchmark dataset specifically designed for Multi-Scale Time-Series Prediction (MSTP). Extensive experiments demonstrate that our method significantly mitigates performance degradation in long-horizon forecasting and consistently outperforms state-of-the-art approaches across all multi-scale prediction tasks.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Coordination and Collaboration
• Computer Vision: Multi-modal Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Accurate temporal prediction is the bridge between comprehensive scene understanding and embodied artificial intelligence. However, predicting multiple fine-grained states of a scene at multiple temporal scales is difficult for vision-language models. We formalize the Multi-Scale Temporal Prediction (MSTP) task in general and surgical scenes by decomposing multi-scale into two orthogonal dimensions: the temporal scale, forecasting states of humans and surgery at varying look-ahead intervals, and the state scale, modeling a hierarchy of states in general and surgical scenes. For example, in general scenes, states of contact relationships are finer-grained than states of spatial relationships. In surgical scenes, medium-level steps are finer-grained than high-level phases yet remain constrained by their encompassing phase. To support this unified task, we introduce the first MSTP Benchmark, featuring synchronized annotations across multiple state scales and temporal scales. We further propose a method, Incremental Generation and Multi-agent Collaboration (IG-MC), which integrates two key innovations. First, we present a plug-and-play incremental generation module that continuously synthesizes up-to-date visual previews at expanding temporal scales to inform multiple decision-making agents, keeping decisions and generated visuals synchronized and preventing performance degradation as look-ahead intervals lengthen. Second, we present a decision-driven multi-agent collaboration framework for multi-state prediction, comprising generation, initiation, and multi-state assessment agents that dynamically trigger and evaluate prediction cycles to balance global coherence and local fidelity.

Problem

• Predicting multiple fine-grained scene states across varying temporal scales
• Modeling hierarchical state relationships in general and surgical scenarios
• Preventing performance degradation as prediction intervals lengthen over time

Innovation

• Incremental generation module synthesizes visual previews
• Multi-agent collaboration framework balances coherence and fidelity
• Plug-and-play architecture prevents performance degradation over time