Multi-Energy Quasi-Symplectic Langevin Inference for Latent Disentangled Learning
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Bibliographic record
Abstract
The variational autoencoder-based method has been widely used for modeling massive datasets. However, for 3D images, simultaneously achieving disentangled representations, low-variance Evidence Lower Bounds (ELBO), and a lightweight model remains a challenging task. In this work, we propose a Langevin dynamics-based inference framework that integrates target data information for efficient likelihood inference and disentangles appearance and morphology features via multi-scale energy-level encoding that enables unsupervised disentanglement. We adopt a quasi-symplectic integrator to handle the Hessian-related computational bottleneck that often arises in Langevin-based flow inference. We demonstrate both theoretical and empirical effectiveness of our approach compared to other methods. Experiments on public benchmarks and clinical 3D imaging datasets show that our Langevin-VAE achieves high-quality generation and learns disentangled shape and appearance representations with a model size of only 1.7M parameters. The code will be available at: https://github.com/LaplaceCenter/LangevinVAE.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it