{"slug": "temporal-distance-jepa-plan-aware-representation-learning-for-latent-world-model", "title": "Temporal-Distance JEPA: Plan-Aware Representation Learning for Latent World Model Predictive Control", "summary": "Researchers propose temporal-distance JEPA (TD-JEPA), a plan-aware representation learning method that mines directed temporal cost from reward-free trajectories to improve latent world model predictive control. Under locked evaluation, TD-JEPA raises Two-Room success to 100.0% versus LeWM's 97.4% and improves OGB-Cube by 14.2 points over LeWM. The method narrows the train-plan gap for JEPA world-model planners by discovering temporal progress structure in offline logs.", "body_md": "arXiv:2607.25337v1 Announce Type: new\nAbstract: Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting in representation space rather than reconstructing pixels, making them a natural backbone for latent model predictive control from offline demonstration logs. JEPA-style training optimizes short-horizon latent prediction, whereas planning requires a multi-step ranking of imagined futures by goal progress. Prior JEPA planners often inherit that ranking from embedding geometry, typically latent Euclidean distance, which arises as a byproduct of representation learning rather than as a progress cost mined from the logs. We propose temporal-distance JEPA (TD-JEPA), which retains the LeWM encoder--predictor backbone and mines a directed temporal cost from reward-free trajectories: same-trajectory step order supplies positive targets, cross-trajectory pairs act as heuristic negatives, and a rollout-consistency term matches the planner horizon. The mined supervision serves two roles: as the deployed planning cost when progress is topological, and as a representation signal that improves Euclidean planning when contact geometry dominates. Under locked evaluation, deploying the mined cost raises Two-Room success to 100.0% versus LeWM's 97.4%, while shared Euclidean planning on the same temporally trained checkpoint raises OGB-Cube by 14.2 points over LeWM and improves Push-T. Against LeWM and the concurrent RC-aux baseline under locked evaluation, TD-JEPA matches or exceeds both methods on every environment. Ablations show that the directed head, cross-trajectory negatives, and rollout consistency each contribute. TD-JEPA narrows the train--plan gap for JEPA world-model planners by discovering temporal progress structure in offline logs and co-designing cost form with plan-time deployment. Code is available at https://github.com/HKBU-KnowComp/TD-JEPA.", "url": "https://wpnews.pro/news/temporal-distance-jepa-plan-aware-representation-learning-for-latent-world-model", "canonical_source": "https://arxiv.org/abs/2607.25337", "published_at": "2026-07-29 04:00:00+00:00", "updated_at": "2026-07-29 04:27:37.251693+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "neural-networks", "ai-research"], "entities": ["TD-JEPA", "LeWM", "Joint-Embedding Predictive Architectures (JEPAs)", "HKBU-KnowComp"], "alternates": {"html": "https://wpnews.pro/news/temporal-distance-jepa-plan-aware-representation-learning-for-latent-world-model", "markdown": "https://wpnews.pro/news/temporal-distance-jepa-plan-aware-representation-learning-for-latent-world-model.md", "text": "https://wpnews.pro/news/temporal-distance-jepa-plan-aware-representation-learning-for-latent-world-model.txt", "jsonld": "https://wpnews.pro/news/temporal-distance-jepa-plan-aware-representation-learning-for-latent-world-model.jsonld"}}