RLA-WM predicts residual latent actions via flow matching to create visual feature world models that outperform prior feature-based and diffusion approaches while enabling offline video-based robot RL.
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arXiv preprint arXiv:2603.14482 (2026)
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2026 17representative citing papers
LookWhen factorizes video recognition into learning when, where, and what to compute via uniqueness-based token selection and dual-teacher distillation, achieving better accuracy-FLOPs trade-offs than baselines on multiple datasets.
World models succeed when their latent states are built to meet task-specific sufficiency constraints rather than preserving the maximum amount of information.
Being-H0.7 adds future-aware latent reasoning to direct VLA policies via dual-branch alignment on latent queries, matching world-model benefits at VLA efficiency.
TrajPilot predicts candidate future trajectories from egocentric context and uses them to condition action prediction in an embedding space, outperforming VLM and planner baselines on Ego-Exo4D, Ego4D, and other datasets with gains increasing at longer horizons.
Latent prediction video models exhibit a distinct robustness profile across corruption, occlusion, fine-grained discrimination, and temporal sensitivity compared to other self-supervised video models when used as world models.
SARA improves text alignment and motion quality in video diffusion models by routing token-relation distillation supervision to semantically salient pairs using a Stage-1 aligner trained with SAM masks and InfoNCE.
Converting 3D MRI volumes into action-conditioned 2D slice navigation sequences offers a complementary self-supervised pretraining signal for learning anatomical and spatial representations.
CoMET achieves strong multimodal classification performance by composing frozen modality encoders, PCA compression, and tabular foundation models without any training, reaching state-of-the-art on diverse benchmarks including large-scale hierarchical tasks.
Empirical tests show that factorized world-model with hard-region-weighted latent dynamics improves ImageNet-100 by 5.92 and SSv2 by 3.21 points over baseline in mixed-dataset pretraining while staying within 0.3 points on Diving-48.
An empirical audit of 22 JEPA-style training auxiliaries on Llama-3.2-1B fine-tuning for regex generation finds no statistically significant task improvement after multiple-testing correction, even when auxiliaries visibly alter hidden-state geometry.
Semantic latent spaces from pretrained encoders outperform reconstruction-based spaces for robotic world models on planning and downstream policy performance.
Composing a policy that maps 2D waypoints to joint actions with a frozen world model yields a lifted world model that achieves 3.8 times lower mean joint error than direct low-level search while being more compute-efficient and generalizing to unseen environments.
Motif-Video 2B reaches 83.76% on VBench, outperforming a 14B-parameter model with 7x fewer parameters and far less training data through shared cross-attention and a three-part backbone.
JFAA freezes a JEPA future-prediction model, adds a lightweight probe and ensemble, and wins the 2026 EK-100 action anticipation challenge.
VISTA wins first place on the Ego4D Short-Term Object Interaction Anticipation challenge by combining spatial object proposals with temporal context via feature modulation and ROI fusion, followed by ensembling.
citing papers explorer
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Learning Visual Feature-Based World Models via Residual Latent Action
RLA-WM predicts residual latent actions via flow matching to create visual feature world models that outperform prior feature-based and diffusion approaches while enabling offline video-based robot RL.
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LookWhen? Fast Video Recognition by Learning When, Where, and What to Compute
LookWhen factorizes video recognition into learning when, where, and what to compute via uniqueness-based token selection and dual-teacher distillation, achieving better accuracy-FLOPs trade-offs than baselines on multiple datasets.
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Latent State Design for World Models under Sufficiency Constraints
World models succeed when their latent states are built to meet task-specific sufficiency constraints rather than preserving the maximum amount of information.
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Being-H0.7: A Latent World-Action Model from Egocentric Videos
Being-H0.7 adds future-aware latent reasoning to direct VLA policies via dual-branch alignment on latent queries, matching world-model benefits at VLA efficiency.
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How You Move Tells What You'll Do: Trajectory-Conditioned Egocentric Prediction
TrajPilot predicts candidate future trajectories from egocentric context and uses them to condition action prediction in an embedding space, outperforming VLM and planner baselines on Ego-Exo4D, Ego4D, and other datasets with gains increasing at longer horizons.
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Latent Video Prediction Learns Better World Models
Latent prediction video models exhibit a distinct robustness profile across corruption, occlusion, fine-grained discrimination, and temporal sensitivity compared to other self-supervised video models when used as world models.
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SARA: Semantically Adaptive Relational Alignment for Video Diffusion Models
SARA improves text alignment and motion quality in video diffusion models by routing token-relation distillation supervision to semantically salient pairs using a Stage-1 aligner trained with SAM masks and InfoNCE.
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3D MRI Image Pretraining via Controllable 2D Slice Navigation Task
Converting 3D MRI volumes into action-conditioned 2D slice navigation sequences offers a complementary self-supervised pretraining signal for learning anatomical and spatial representations.
-
Modular Multimodal Classification Without Fine-Tuning: A Simple Compositional Approach
CoMET achieves strong multimodal classification performance by composing frozen modality encoders, PCA compression, and tabular foundation models without any training, reaching state-of-the-art on diverse benchmarks including large-scale hierarchical tasks.
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Factorized Latent Dynamics for Video JEPA: An Empirical Study of Auxiliary Objectives
Empirical tests show that factorized world-model with hard-region-weighted latent dynamics improves ImageNet-100 by 5.92 and SSv2 by 3.21 points over baseline in mixed-dataset pretraining while staying within 0.3 points on Diving-48.
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Representation Without Reward: A JEPA Audit for LLM Fine-Tuning
An empirical audit of 22 JEPA-style training auxiliaries on Llama-3.2-1B fine-tuning for regex generation finds no statistically significant task improvement after multiple-testing correction, even when auxiliaries visibly alter hidden-state geometry.
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Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models
Semantic latent spaces from pretrained encoders outperform reconstruction-based spaces for robotic world models on planning and downstream policy performance.
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Lifting Embodied World Models for Planning and Control
Composing a policy that maps 2D waypoints to joint actions with a frozen world model yields a lifted world model that achieves 3.8 times lower mean joint error than direct low-level search while being more compute-efficient and generalizing to unseen environments.
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Motif-Video 2B: Technical Report
Motif-Video 2B reaches 83.76% on VBench, outperforming a 14B-parameter model with 7x fewer parameters and far less training data through shared cross-attention and a three-part backbone.
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JFAA: Technical Report for the EPIC-KITCHENS-100 Action Anticipation Challenge at EgoVis 2026
JFAA freezes a JEPA future-prediction model, adds a lightweight probe and ensemble, and wins the 2026 EK-100 action anticipation challenge.
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VISTA: Technical Report for the Ego4D Short-Term Object Interaction Anticipation at EgoVis 2026
VISTA wins first place on the Ego4D Short-Term Object Interaction Anticipation challenge by combining spatial object proposals with temporal context via feature modulation and ROI fusion, followed by ensembling.
- EgoExo-WM: Unlocking Exo Video for Ego World Models