Static HTML snapshot of the model record for crawlers and no-JS readers.
| Attribute | Value |
|---|---|
| Model | LeWorldModel |
| Lab / Organization | Mila / NYU / Samsung SAIL |
| Category | Self-Supervised World Model |
| Subtype | Compact JEPA Model |
| World Model Type | Compact joint-embedding predictive world model for physical understanding |
| Primary Domain | Physical Reasoning |
| Architecture | Compact Vision Transformer with JEPA and SigReg regularization |
| Modality | Visual → Latent Predictions |
| Training Method | Self-supervised JEPA training with SigReg collapse prevention, single-GPU trainable |
| Status | active |
| Year | 2026 |
| Performance Index | 78/100 (medium confidence, v1.1) |
Main editorial body preserved directly in static HTML.
LeWorldModel (LeWM) is a compact JEPA-based world model with only 15 million parameters that achieves physical understanding and planning up to 48× faster than foundation-model-based alternatives. Developed by researchers from Mila, NYU, and Samsung SAIL, it introduces SigReg, a novel regularization technique that solves the representation collapse problem that has plagued JEPA architectures. LeWM demonstrates that large-scale foundation models are not strictly necessary for physical reasoning: efficient, well-regularized architectures can learn meaningful world representations from pixels on commodity hardware.
LeWorldModel is a compact joint-embedding predictive world model for physical understanding developed by Mila / NYU / Samsung SAIL in 2026 for physical reasoning.
Short extractable facts for answer engines and no-JS readers.
| Signal | Value |
|---|---|
| Definition | LeWorldModel is a compact joint-embedding predictive world model for physical understanding developed by Mila / NYU / Samsung SAIL in 2026 for physical reasoning. |
| Short Description | A compact 15M-parameter JEPA world model that learns real-world physics on a single GPU, solving the notorious representation collapse problem. |
| Benchmark Rows | 2 |
| FAQ Entries | 1 |
| Related Models | 4 |
| Related Guides | 0 |
| Related Research Topics | 0 |
| Last Updated | 2026-04-10 |
Key capabilities associated with this model.
Representative applications attached to this model record.
Balanced assessment surfaced in static HTML.
Primary references preserved in static HTML for citation extraction.
| Reference | Link |
|---|---|
| LeCun et al., 2026. LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels. arXiv:2603.19312. | Open source |
Nearby models linked from the current editorial record.
| Model | Category | World Model Type | Index v1.1 |
|---|---|---|---|
| V-JEPA | Self-Supervised World Model | Self-supervised visual world model | 70/100 |
| V-JEPA 2 | Self-Supervised World Model | Joint-embedding predictive world model for video understanding and robot planning | 87/100 |
| I-JEPA | Self-Supervised World Model | Self-supervised visual world model | 61/100 |
| AMI World Model | Foundation World Model | Multimodal generative world model | 38/100 |
Side-by-side comparisons already connected to this model.
| Comparison | Matchup | Summary |
|---|---|---|
| V-JEPA 2 vs V-JEPA | V-JEPA 2 vs V-JEPA | V-JEPA 2 dramatically scales up Meta FAIR's self-supervised video world model, achieving state-of-the-art visual understanding and zero-shot robot control, capabilities V-JEPA didn't demonstrate. |
| V-JEPA 2 vs I-JEPA | V-JEPA 2 vs I-JEPA | Two milestones of the JEPA roadmap: I-JEPA established self-supervised image representation by predicting in latent space; V-JEPA 2 extends the paradigm to video at foundation scale and demonstrates zero-shot robot control. |
| LeWorldModel vs DreamerV3 | LeWorldModel vs DreamerV3 | LeWorldModel revisits LeCun's energy-based JEPA philosophy for control, predicting in latent space without pixel reconstruction. DreamerV3 remains the canonical RSSM-based agent that learns by imagining pixel-grounded rollouts. |
| Genie 3 vs V-JEPA 2 | Genie 3 vs V-JEPA 2 | Two green-index leaders that represent different frontier philosophies. Genie 3 is an interactive generative world model that turns text into playable environments, while V-JEPA 2 is a self-supervised latent predictor optimized for physical reasoning and zero-shot robot planning. |
FAQ answers rendered directly into static HTML for extractable responses.
SigReg is a regularization technique introduced in LeWorldModel that prevents the representation collapse problem in JEPA architectures by constraining the singular values of the latent representations, ensuring they remain informative throughout training.
Short extractable summary preserved directly in static HTML.
Editorial provenance and refresh policy preserved directly in static HTML.
Published by world-models.io editorial board.
Lead editor Tyler D. - Technical editor, methodology and benchmark analysis.
This model page synthesizes primary papers, official model pages, benchmark evidence, and related world-models.io context into a reference resource.
Each editorial page is assembled from primary sources, normalized into extractable summaries, checked for factual drift, and reviewed before publication or major refreshes. Last reviewed: 2026-04-10.
Pages are refreshed when a new paper, benchmark, release, architecture update, or stronger primary source materially changes the answer a reader or AI system should retrieve.
Each page links back to relevant primary sources and keeps a stable canonical URL so readers can verify claims, trace context, and reference the most up-to-date version. See the editorial policy.
Primary model and lab sources embedded in static HTML.