ST-Prune: Training-Free Spatio-Temporal Token Pruning for Vision-Language Models in Autonomous Driving explores A training-free, plug-and-play framework that significantly reduces computational overhead for vision-language models in autonomous driving by intelligently pruning spatio-temporal tokens, achieving near-lossless performance at 90% reduction.. Commercial viability score: 8/10 in Vision-Language Models for Autonomous Driving.
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Canonical ID st-prune-training-free-spatio-temporal-token-pruning-for-vision-language-models-in-autonomous-driving | Route /paper/st-prune-training-free-spatio-temporal-token-pruning-for-vision-language-models-in-autonomous-driving
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/st-prune-training-free-spatio-temporal-token-pruning-for-vision-language-models-in-autonomous-drivingMCP example
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}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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