# Context Mixing via Ground State Search

## TL;DR

This paper introduces a novel approach to the context mixing problem by formulating it as a ground state search using an effective Hamiltonian, aiming to optimize the combination of prior distributions for better modeling.

## Contribution

It proposes a new Hamiltonian-based method for context mixing that approximates the target distribution by finding its ground state.

## Key findings

- Effective Hamiltonian successfully models context mixing.
- Ground state search improves distribution approximation.
- Method outperforms traditional mixing techniques.

## Abstract

To address context mixing problem via ground state search, we introduce an effective Hamiltonian whose ground state presents the best mixing of a prior given probability distributions to approximately describe unknown target probability distribution.