Real-Time Inference for Distributed Multimodal Systems under Communication Delay Uncertainty

• URL: http://arxiv.org/abs/2511.16225v1
• Date: Thu, 20 Nov 2025 10:48:54 GMT
• Title: Real-Time Inference for Distributed Multimodal Systems under Communication Delay Uncertainty
• Authors: Victor Croisfelt, João Henrique Inacio de Souza, Shashi Raj Pandey, Beatriz Soret, Petar Popovski,
• Abstract summary: Connected cyber-physical systems perform inference based on real-time inputs from multiple data streams.<n>We propose a novel neuro-inspired non-blocking inference paradigm that employs adaptive temporal windows of integration.<n>Our framework achieves robust real-time inference with finer-grained control over the accuracy-latency tradeoff.
• Score: 37.15356899831919
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Connected cyber-physical systems perform inference based on real-time inputs from multiple data streams. Uncertain communication delays across data streams challenge the temporal flow of the inference process. State-of-the-art (SotA) non-blocking inference methods rely on a reference-modality paradigm, requiring one modality input to be fully received before processing, while depending on costly offline profiling. We propose a novel, neuro-inspired non-blocking inference paradigm that primarily employs adaptive temporal windows of integration (TWIs) to dynamically adjust to stochastic delay patterns across heterogeneous streams while relaxing the reference-modality requirement. Our communication-delay-aware framework achieves robust real-time inference with finer-grained control over the accuracy-latency tradeoff. Experiments on the audio-visual event localization (AVEL) task demonstrate superior adaptability to network dynamics compared to SotA approaches.

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