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SMART achieves a remarkable 0.42 dB improvement in PSNR over state-of-the-art methods, tackling the critical challenge of geometric consistency in light field super-resolution.
Adversaries can exploit overthinking in LVLMs to induce inference delays of up to 6.96x, jeopardizing robotic safety and performance.
Evolving prompts and verifying answers can boost visual reasoning model accuracy by over 19%鈥攁 game changer for scaling reliable data in AI.
Federated RAG systems can now be practical: this work achieves a 62x speedup over prior secure methods, while maintaining model utility, by decoupling attention from data localization.