arXiv Machine Learning
Jul 3
Certified World Models: Predictability Across Configuration, Horizon, and Resolution
arXiv:2606. 13092v3 Announce Type: replace Abstract: Scale buys interpolation; structure buys certifiable transfer.
When Do Conservation Laws Survive Learned Representations? Certified Horizons for Latent World Models
arXiv:2606. 24945v1 Announce Type: new Abstract: We ask a representation-learning question about physical world models: when does a conservation law remain certifiable after a model learns a latent representation?
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arXiv Machine Learning
Jul 7
Reduced-Order Models: The Mother of World Models
arXiv:2607. 03198v1 Announce Type: new Abstract: World models -- compressed latent representations of an environment that support action-conditioned prediction and planning -- are typically presented as a product of modern self-supervised learning.
arXiv AI
Jun 2
TERRA: Task-Embedded Reasoning and Representation Architecture for Cross-Domain Applications
arXiv:2606. 01520v1 Announce Type: new Abstract: A single action-conditioned latent predictive architecture can in principle be trained on the structured state of a driving scene, a robot workspace, or a financial order book.
arXiv Machine Learning
Aug 12
Intrinsic Structure: Spectral Identifiability for Mechanistic Interpretability
arXiv:2608. 10172v1 Announce Type: new Abstract: Mechanistic interpretability explains models by identifying circuits inside them, but has no way to tell whether a circuit is a property of the model or an artifact of the method that found it.
arXiv Machine Learning
Jun 25
Conformal Orbit-Valid Trust Horizons for Equivariant World Models
arXiv:2606. 24946v1 Announce Type: new Abstract: Learned world models are useful only over horizons on which their rollout error remains controlled.
arXiv Machine Learning
Jul 30
What Can Latent World Models Know? Physical Parameter Identifiability in Multimodal Predictive Representations
arXiv:2607. 27017v1 Announce Type: new Abstract: A central premise of latent world models is that predicting the future forces a representation to internalize the physics of its environment.
arXiv Machine Learning
Jul 14
A Control Theory of Predictability in Latent World Models
arXiv:2607. 10362v1 Announce Type: new Abstract: Latent world models are trained to predict future states in a learned representation and are then deployed inside a planner that selects actions by simulating them forward.
arXiv Machine Learning
Jun 17
Variational autoencoders with latent high-dimensional steady geometric flows for dynamics
arXiv:2410. 10137v5 Announce Type: replace Abstract: We develop Riemannian approaches to variational autoencoders (VAEs) for PDE-type ambient data with regularizing geometric latent dynamics, which we refer to as VAE-DLM, or VAEs with dynamical latent manifolds.
arXiv AI
Jun 24
A Simplex Witness Certificate and Escape Force for Constant Collapse in Variational Autoencoders
arXiv:2605. 18224v4 Announce Type: replace-cross Abstract: We study exact constant collapse in variational autoencoders: the deterministic encoder mean becomes independent of the input.
arXiv Machine Learning
Aug 3
Symplectic Representation of Legendre Dynamics
arXiv:2512. 19409v2 Announce Type: replace Abstract: Modern learning systems act on internal representations of data, yet how these representations encode underlying physical or statistical structure is often left implicit.
arXiv Machine Learning
Jun 3
High-Dimensional Latents Should Be Diagnosed Through Phase Structure
arXiv:2606. 02600v1 Announce Type: cross Abstract: We study autoencoder and variational-autoencoder latent spaces through the lens of spin-glass theory.
arXiv Machine Learning
Jul 15
Constructed Reality, Contested Priors: Decoupling and the Architecture of Cognitive Relapse Under the Free Energy Principle
arXiv:2607. 11958v1 Announce Type: new Abstract: Under the free energy principle, a predictive system does not observe reality directly; it maintains a generative model of the world and experiences that model's best current hypothesis.