What Do Latent Predictive Vehicle Representations Retain? Measuring State, Geometry, and Local Response

arXiv:2609.32512 · cs.LG, cs.SY, eess.SY · Submitted 2026-09-26 · Read on arXiv

cs.LG, cs.SY, eess.SY

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Steering — 15× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Regret — 9× in this paper · explained in 3 episodes
• Embeddings — 8× in this paper · explained in 2 episodes
• World Model — 7× in this paper · explained in 7 episodes
• Latent Dynamics — 5× in this paper · explained in 2 episodes
• Self-Supervised Learning — 4× in this paper · explained in 8 episodes
• Latent World Models — 3× in this paper · explained in 2 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Decode — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Closed-Loop System — 1× in this paper · explained in 4 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Parameter Identifiability — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• System Identification — 1× in this paper · explained in 3 episodes

Sources

• TD-JEPA: Latent-predictive Representations for Zero-Shot Reinforcement Learning

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks