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

• URL: http://arxiv.org/abs/2507.03065v1
• Date: Thu, 03 Jul 2025 17:24:27 GMT
• Title: Cycle-Consistent Helmholtz Machine: Goal-Seeded Simulation via Inverted Inference
• Authors: Xin Li,
• Abstract summary: We introduce the emphCycle-Consistent Helmholtz Machine (C$2$HM)<n>C$2$HM reframes inference as a emphgoal-seeded, emphasymmetric process grounded in structured internal priors.<n>By offering a biologically inspired alternative to classical amortized inference, $C2$HM reconceives generative modeling as intentional simulation.
• Score: 5.234742752529437
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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