AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Robots must often continue a task after a target moves, the viewpoint shifts, or an obstacle appears, even though their earlier observations and committed actions reflect the previous scene. Many simulation robustness benchmarks fix external conditions at reset, leaving this temporal challenge underexamined. We introdu...
Quantitative spatial reasoning in visual-language models (VLMs) aims to infer spatial distances and directional relationships among objects in 3D space from a 2D image and a natural language query. Despite recent progress, VLM spatial reasoning remains brittle under distribution shifts, largely due to the high cost of...
PuRF is introduced, a novel PuRiFication-driven cache-based method for multi-label test-time adaptation of vision-language models that consistently outperforms state-of-the-art methods on ViT-B/32 across five datasets.
This comprehensive survey formally defines the CTTA problem, analyzes the diverse continual domain shift patterns that characterize different evaluation protocols, and proposes a hierarchical taxonomy that categorizes existing methods into three families: optimization-based strategies (entropy minimization, pseudo-labe...
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