AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The Noisy Test-time Reinforcement Learning framework (NTRL-Code) is proposed, which enables robust self-evolution of code LLMs using only unlabeled noisy data during the testing stage, and employs an abstract-syntax-tree (AST)-based structural aggregation mechanism to estimate a proxy target from multiple candidate pro...
FISA is proposed, a framework for MLLM self-improvement that constructs augmented images from the model's own failure cases that generates visually challenging yet answer-preserving image complications, verifies their utility through self-examination, and applies dual fidelity filtering to avoid semantic distortion.
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