{"ID":2898861,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.03065","arxiv_id":"2507.03065","title":"Cycle-Consistent Helmholtz Machine: Goal-Seeded Simulation via Inverted Inference","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.","short_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 tempor...","url_abs":"https://arxiv.org/abs/2507.03065","url_pdf":"https://arxiv.org/pdf/2507.03065v1","authors":"[\"Xin Li\"]","published":"2025-07-03T17:24:27Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}