Priors in LLM Routing: From Initialization to Regularization

Abstract

This paper investigates how different forms of prior knowledge can be systematically incorporated into LLM routing mechanisms — from model weight initialization to training-time regularization. We propose and evaluate several approaches for encoding inductive biases into routing architectures.

Role: Second author — contributed to benchmark construction, dataset processing, and experimental pipeline.

Status: Preprint