arXiv AI
Jun 2
BRo-JEPA: Learning Modular Arithmetic in Latent Space
arXiv:2606. 01372v1 Announce Type: cross Abstract: Can neural networks learn abstract algebraic rules, or do they merely memorize training patterns?
BRo-JEPA: Learning Modular Transformations in Latent Space
arXiv:2606. 01372v2 Announce Type: replace-cross Abstract: Can neural networks learn algebraic rules from visual inputs, or do they merely fit observed patterns?
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arXiv AI
Jun 10
One Lens, Many Worlds : A Capability-Typed Interface for World-Model Interpretability
arXiv:2606. 09936v1 Announce Type: cross Abstract: World models are now built on substantially different computational substrates.
arXiv AI
Jun 16
Improved Baselines with Representation Autoencoders
arXiv:2605. 18324v2 Announce Type: replace-cross Abstract: Representation Autoencoders (RAE) replace traditional VAE with pretrained vision encoders.
arXiv Machine Learning
Jul 2
MetaOthello: A Controlled Study of Multiple World Models in Transformers
arXiv:2602. 23164v2 Announce Type: replace Abstract: Foundation models must handle multiple generative processes, yet mechanistic interpretability largely studies capabilities in isolation; it remains unclear how a single transformer organizes multiple, potentially conflicting "world models".
arXiv Machine Learning
Jun 24
Selective Rotary Position Embedding
arXiv:2511. 17388v3 Announce Type: replace-cross Abstract: Position information is essential for language modeling.
Hugging Face Trending Papers
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No Gaussian Required: Contrastive Inverse Dynamics for JEPA World Models
Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting future embeddings, but the objective admits a trivial solution of a constant encoder, so every practical system adds an anti-collapse mechanism (LeCun, 2022; Assran et al. , 2023; Bardes et al.
arXiv AI
Jul 8
Transformers converge to invariant algorithmic cores
arXiv:2602. 22600v2 Announce Type: replace-cross Abstract: Training selects for behavior, not circuitry: many weight configurations can implement the same function.
arXiv Machine Learning
Jul 9
When Do Geometric Algebra Layers Beat Scalarization? A Controlled Study on SO(3)-Equivariant Vector Laws
arXiv:2607. 06634v1 Announce Type: new Abstract: Compact networks built from Clifford algebra Cl(3,0) primitives are exactly SO(3)-equivariant and learn synthetic 3D vector laws from few samples.
arXiv Machine Learning
Jun 5
LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
arXiv:2603. 19312v3 Announce Type: replace Abstract: Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces, yet existing methods remain fragile, relying on complex multi-term losses, exponential moving averages, pre-trained encoders, or auxiliary supervision to avoid representation collapse.
arXiv Machine Learning
Jul 7
Qantara: Bridge-Flow Training for Multi-Paradigm JEPA Control
arXiv:2607. 04978v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) underpin a growing family of latent world models for control from raw pixels, but every existing JEPA world model commits at training time to a single inference paradigm: either trajectory optimisation in a learned dynamics model, or direct behaviour cloning.
arXiv AI
1d ago
No Gaussian Required: Contrastive Inverse Dynamics for JEPA World Models
arXiv:2608. 17542v1 Announce Type: cross Abstract: Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting future embeddings, but the objective admits a trivial solution of a constant encoder, so every practical system adds an anti-collapse mechanism (LeCun, 2022; Assran et al.
arXiv Machine Learning
Jun 25
ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learning
arXiv:2501. 14622v5 Announce Type: replace Abstract: Learning efficient representations for decision-making policies is a challenge in imitation learning (IL).