Exploiting answer-invariant redundancies in satellite imagery for efficient VLM inference on edge

Abstract:Onboard vision-language models could enable satellites to answer queries directly, but exhaustive tiled inference over high-resolution imagery is slow and energy-intensive. We identify answer-invariant token redundancy (AITR): image tiles and vision tokens that can be removed without changing the final answer. We present Rift, a two-stage system that performs query-conditioned tile pruning followed by elastic prefill to reduce token budget. We evaluate it on LLaVA-1.5 7B running on Jetson AGX Orin. Compared with exhaustive tiled inference, Rift reduces energy by 78% and latency by 69%, while increasing accuracy from 45% to 73%.

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2609.29029 [cs.CV]

(or arXiv:2609.29029v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.29029 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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