3 accepted papers
Large language models (LLMs) exhibit strong generative capabilities but remain vulnerable to confabulations, fluent yet unreliable outputs that vary arbitrarily even under identical prompts. Leveraging a quantum tensor network–based pipeline, we propose a quantum physics-inspired uncertainty quantif…
The problem of inferring the hidden state of individual nodes in social/sensor networks in which node activities affect their neighbors is growing in importance. We present an undirected generative model, a type of probabilistic model that has so far not been used for modeling latent variables influ…
We examine the design of self-organizing mobile adaptive networks with multiple targets in which the network nodes form distinct clusters to learn about and purse multiple targets, all while moving in a cohesive collision-free manner. We build upon previous distributed diffusion-based adaptive learn…