Lightweight Adaptation for LLM-based Technical Service Agent: Latent Logic Augmentation and Robust Noise Reduction

ArXi:2603.18074v1 Announce Type: cross Adapting Large Language Models in complex technical service domains is constrained by the absence of explicit cognitive chains in human nstrations and the inherent ambiguity arising from the diversity of valid responses. These limitations severely hinder agents from internalizing latent decision dynamics and generalizing effectively. Moreover, practical adaptation is often impeded by the prohibitive resource and time costs associated with standard