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TRACE: Retrospective Streaming Generation of Physical Fields under Sparse Structured Sensing

arXiv · AI, language, vision and robotics · article · Aug 26, 2026 · UTC

Reconstructing continuous physical fields from sparse measurements is central to scientific monitoring, inverse modeling, and digital-twin construction. Generative reconstruction has recently emerged as a promising paradigm for this task by learning data-driven physical priors that complete plausible full fields from limited observations. However, existing methods largely assume fixed, batch conditioning, whereas real sensing systems often produce structured streams: probes scan local regions, instruments observe moving fields of view, and communication constraints may leave entire frames miss

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