Cycle-Consistent Helmholtz Machine: Goal-Seeded Simulation via Inverted Inference

TL;DR

The Cycle-Consistent Helmholtz Machine introduces goal-seeded, goal-directed inference for simulation and planning, enabling structured latent trajectory generation conditioned on abstract goals within a cycle-consistent variational framework.

Contribution

It presents a novel cycle-consistent extension to the Helmholtz Machine that supports goal-conditioned simulation and structured latent inference, bridging memory, planning, and unsupervised learning.

Findings

Supports goal-conditioned latent trajectory simulation.

Enables spatial compositional imagination.

Improves representational efficiency.

Abstract

The Helmholtz Machine (HM) is a foundational architecture for unsupervised learning, coupling a bottom-up recognition model with a top-down generative model through alternating inference. However, its reliance on symmetric, data-driven updates constrains its ability to perform goal-directed reasoning or simulate temporally extended processes. In this work, we introduce the \emph{Cycle-Consistent Helmholtz Machine} (C$^2$HM), a novel extension that reframes inference as a \emph{goal-seeded}, \emph{asymmetric} process grounded in structured internal priors. Rather than inferring latent causes solely from sensory data, C$^2$HM simulates plausible latent trajectories conditioned on abstract goals, aligning them with observed outcomes through a recursive cycle of forward generation and inverse refinement. This cycle-consistent formulation integrates top-down structure with bottom-up evidence via a variational loop, enforcing mutual alignment between goal-conditioned latent predictions and recognition-based reconstructions. We formalize this mechanism within the framework of the \emph{Context-Content Uncertainty Principle} (CCUP), which posits that inference proceeds by aligning structured, low-entropy content with high-entropy, ambiguous context. C$^2$HM improves representational efficiency, supports memory chaining via path-dependent inference, and enables spatial compositional imagination. By offering a biologically inspired alternative to classical amortized inference, $C^2$HM reconceives generative modeling as intentional simulation, bridging memory-based planning and unsupervised learning in a unified probabilistic framework.