AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Accurate and efficient pronunciation transcription is essential for preparing text-to-speech training data at scale. Existing approaches have different limitations: grapheme-to-pronunciation (G2P) and speech-to-pronunciation (S2P) methods each capture only partial information, using only text or only speech, while spee...
Tandem speech-to-speech architectures couple a responsive speech frontend with an asynchronous text backend. In KAME, a large language model (LLM) serves as the backend, supplying candidate responses as guidance to the speech frontend while the user is still speaking. Ordinary conversation recordings capture the eventu...
This work proposes Feedback-to-Rubrics, a problem setting for learning criteria from inline comments on artifacts, which infers rubrics from these comments and iteratively refines them by observing errors in comment prediction based on the inferred rubrics.
SAIL is a framework that reframes robot imitation as an iterative refinement problem capable of scaling with test-time compute, and utilizes Monte Carlo Tree Search, where each node is a complete trajectory and edges correspond to trajectory refinements.
Closed-loop robot policies require observation processing, state management, and situation-dependent branching, making them costly to design and tune manually. Although coding agents increasingly support control-code generation and optimization, it remains unclear whether implementations improved on source tasks also s...
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