LookALike: Human Mimicry based collaborative decision making
Rabimba Karanjai Weidong Shi
Artificial General Intelligence falls short when communicating role specific
nuances to other systems. This is more pronounced when building autonomous LLM
agents capable and designed to communicate with each other for real world
problem solving. Humans can communicate context and domain specific nuances
along with knowledge, and that has led to refinement of skills. In this work we
propose and evaluate a novel method that leads to knowledge distillation among
LLM agents leading to realtime human role play preserving unique contexts
without relying on any stored data or pretraining. We also evaluate how our
system performs better in simulated real world tasks compared to state of the
art.