Abstract: Embedded intelligence is a challenging field in engineering given its resource-constrained environment which regular machine learning algorithms demand. Most embedded intelligence models are ...

We introduce iterative retrieval, a novel framework that empowers retrievers to make iterative decisions through policy optimization. Finding an optimal portfolio of retrieved items is a combinatorial ...

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...

Abstract: Embedded machine learning applications face challenges related to massive data movement and high computational intensity, exacerbated by the limited performance of mobile devices.