jepa

KITE 提出一种知识注入的时间序列编码器，将工程师的领域先验（语义先验）通过可切换的协变量与注意力偏置机制注入自监督事件预测器，在稀有事件预测上显著优于无先验基线，并支持跨域迁移。

SPERA 是一种采用 JEPA 潜空间预测架构的 EEG 基础模型，引入 Legendre 多项式空间先验与分解式时空注意力，在约 8 万小时、29,048 名受试者的 EEG 数据上预训练，并在九项下游任务上取得最高平均平衡准确率。

RSI-Anything 提出一个基于 JEPA-Anything 的领域通用递归自我改进框架，利用预测性世界模型指导行动与学习数据选择，在代码修复、GUI 任务、实验室规划和反应优化等多个领域均取得显著性能提升。

LeWAM 提出一种端到端的 JEPA 控制框架，通过联合训练下一隐状态预测与动作流匹配目标，并对隐状态施加各向异性高斯约束，使单一模型同时支持直接策略执行与高效规划，在接触丰富任务和长程任务上将成功率从 28.6% 提升至 89.7% 和从 10.9% 提升至 46.2%，规划速度提升 32.3 倍。

RoboJEPA 基于 JEPA 架构，在涵盖 12 种机器人具身的大型真实机器人数据集上训练，首次为多具身机器人世界模型建立了 scaling law：其 latent rollout 的“想象力误差”随计算量呈二阶幂律变化，并可作为真实机器人规划性能的可靠代理。该模型参数量达 8B，是迄今最大的 JEPA 预测模型，支持零样本部署为长时程规划的机器人智能体，并开源全部检查点与训练/部署代码。

DSReg提出通过依赖稀疏性正则化（Structural Diversity条件），在无需重建、解码器或标签的情况下可证明地恢复单个世界隐变量，建立了首个完全可识别的JEPA表示学习方法。

JEPA-Anything introduces a domain-agnostic world modeling framework based on orthogonal predictive factorization (OPF) that works across vision, biology, clinical trajectories, control, molecular dynamics, physical fields, and weather, outperforming matched JEPA baselines on all 10 dynamics tasks and supporting experimentally validated biological intervention nomination.

The paper develops an early-training stability theory for Joint-Embedding Predictive Architectures that unifies empirical collapse-avoidance heuristics via a drive/decay stability ratio, predicts a phase boundary confirmed across 800+ Tabular-JEPA configurations, and introduces a ResidualPred transformer predictor that improves effective rank and accuracy on tabular and I-JEPA image benchmarks.

H-JEPА introduces an action-conditioned joint-embedding predictive architecture that separates a wide perceptual code from an inherited control state governed by dissipative port-Hamiltonian dynamics, using port-inverse consistency as a parameter-free reweighting of prediction error, and matches or beats baselines like Delta-JEPA on pixel-based control benchmarks.

JEPA 有一个未被讨论的严重盲点：它严重依赖稳定、缓慢变化的特征。只要在每个 episode 中加入一个固定不变的无关 patch，规划就会崩溃；若让该 patch 不可预测，性能反而几乎恢复正常。

JEPA-TTT adapts the latent dynamics predictor of a pretrained JEPA world model during test time using self-supervised dense replay, improving planning under dynamics shifts with 83% lower latent prediction error and 153% better planning performance over the frozen model across four continuous-control environments.

PDE-JEPA introduces predictive representation pretraining with a masked-latent encoder, a geometry projector aligning latent trajectories to physical field evolution, and a physics-structured latent predictor for parametric PDEs, outperforming SOTA by 33.4% in-distribution and 51.4% in extrapolation across nine PDE benchmarks.

This paper systematically evaluates masking strategies for EEG foundation models across MAE and JEPA frameworks, identifying optimal configurations and a novel failure mode, achieving comparable performance to REVE with reduced compute.

This paper proposes a training recipe for tabular foundation models using a Joint-Embedding Predictive Architecture (JEPA) to address latent term collapse, and compares its performance to a value-only approach across 147 real datasets.

The paper presents SAAC-JEPA, a schema-adaptive action-conditioned JEPA model for transferring predictive representations across CNC machines with partial sensor overlap, demonstrating that cross-machine adaptation requires distinct evaluation beyond source-domain accuracy.

Crucible is an experimental neurosymbolic stack integrating Mamba2 with Z3 verification, JEPA for concept prediction, and HDC memory, designed for free Kaggle T4 sessions.

ARC-Bench is introduced as a benchmark to audit whether frozen JEPA-style world models correctly rank candidate actions based on latent distance, revealing severe and structural failures in official checkpoints. The paper explains that closed-loop replanning masks these defects, leading to overestimated success rates.

Flow-JEPA introduces a conditional flow matching approach to JEPA world models, improving robustness and accuracy in predicting future latent states under noisy conditions.

The paper proposes SciJEPA, a citation-free framework for scientific document representation using asymmetric within-document predictive learning, and demonstrates that regularization improves performance.

XP-JEPA introduces cross-predictive physical grounding to improve latent dynamics for better forecastable control in world models without requiring physical inputs at test time.