AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work formalizes LRM safety objectives as requiring non-malicious reasoning and answers, safety consistency, and explicit intent identification, and introduces a reasoning-preserving gradient projection mechanism that analytically resolves conflicts between safety and reasoning gradients, effectively eliminating th...
Asynchronous Federated Learning (AFL) enhances the efficiency of edge collaborative learning systems by asynchronously aggregating client updates to prevent slowdowns from slow clients. However, in non-IID scenarios, such operations magnify uneven learning among various local data, degrading global model generalization...
Results show that privacy-sensitive visual and textual information can be recovered even from deep-layer LVLM hidden states, exposing the privacy risks of collaborative inference.
An attention-free and lightweight token reduction framework as a plug-and-play module for VLMs, which preserves both important and diverse tokens to produce a compact visual representation, and achieves a favorable accuracy-efficiency trade-off.
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