{"paper_id":"2123eb2e-bb69-4a81-b3ac-2c32e7f65ff0","body_text":"Bridging the Latency Gap with a Continuous Stream Evaluation Framework in Event-Driven Perception | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Bridging the Latency Gap with a Continuous Stream Evaluation Framework in Event-Driven Perception Guang Chen, Jie Chu, Runze Zhang, Chu Yang, Zongtao Bu, Haotian Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6135923/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Mar, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Neuromorphic vision systems, inspired by biological sensory processing, offer transformative potential for real-time applications by leveraging event-driven data streams. However, current evaluation paradigms remain limited by frame-based methodologies that segment continuous event streams and neglect computational latency, which is a critical factor in dynamic, real-world environments. This discrepancy creates a substantial gap between laboratory benchmarks and practical performance, especially in scenarios requiring instantaneous responses. We introduce a novel latency-aware evaluation framework that eliminates the pre-slicing of event stream segmentation and employs continuous, adaptive sampling to preserve temporal continuity while quantifying computational delay. To validate our approach, we create a high-frequency dataset with 500 Hz temporal resolution, capturing scenarios from static to high-speed motion without aliasing. Experiments demonstrate that conventional metrics overestimate real-time performance by up to 40%, underscoring the necessity of latency-aware assessment. We further propose two bio-inspired strategies, predictive motion extrapolation and context-aware sampling, which could leverage the temporal dynamics and motion sensitivity of event streams to reduce latency-induced errors by 59% while boosting inference speed by 12%. These advances bridge offline evaluation and real-world deployment, offering tools for designing neuromorphic systems in robotics and autonomous applications where sub-millisecond responsiveness is paramount. Our work establishes a foundation for continuous, latency-aware perception, combining methodological innovation with practical impact for next-generation vision technologies. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Computational science Biological sciences/Neuroscience/Visual system/Retina Physical sciences/Engineering/Electrical and electronic engineering Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 16 Mar, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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