arXiv:2606. 30068v1 Announce Type: new Abstract: Joint-embedding predictive (JEPA-style) objectives learn representations by predicting future latents.
JEPA-x is a cross‑predictive physics grounding method that aligns visual latent dynamics with privileged physical trajectories. By treating visual observations and physical states as two views of the same action‑conditioned trajectory and sharing a predictor, it forces the model to learn a common transition rule for both modalities. The physical branch is only used during training, so deployment incurs no extra cost, and the approach significantly reduces rollout drift and boosts control success across a multi‑task suite.
arXiv:2607. 29031v1 Announce Type: cross Abstract: Existing autonomous-driving world models typically perform dense prediction of future videos, occupancy states, BEV representations, or agent motion.
The paper investigates how encouraging action predictability in latent world models can preserve intrinsic dynamics, which are crucial for planning. By deriving a theoretical lower bound and conducting empirical tests, the authors show that action predictability prevents the encoder from merging distinct action outcomes, even when visual appearance varies. Using a method called Concat IDM, they achieve a substantial increase in planning success on the Push‑T benchmark, from 9.1% to 77.2%, demonstrating the practical benefit of this inductive bias.
The paper investigates how to combine multiple calibrated predictors into a single refined predictor that preserves all available information and cannot be further refined. It models calibrated predictors as signaling schemes and defines refinement via feature‑independent garblings, showing that a new predictor is constructible exactly when its signal vector lies in the linear span of the input signal vectors. The authors provide algorithmic results: deterministic refinement is polynomial‑time solvable for two predictors but NP‑hard for many, while randomized refinement admits a polynomial‑time algorithm for any number of inputs by decomposing signal vectors into extreme rays of a polyhedral cone.
arXiv:2608. 01336v1 Announce Type: cross Abstract: Modern autonomous-driving fleets record far more video than human reviewers can inspect.
arXiv:2609.24126v1 Announce Type: cross Abstract: Black-box machine learning models increasingly deliver strong predictions, but extracting useful information from them, such as a set of important fe...
arXiv:2606. 03885v1 Announce Type: new Abstract: Feature attribution methods explain predictions by assigning importance scores to input features.
arXiv:2608. 12939v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPAs) learn world models that predict in a compact latent space rather than in pixels, reducing the pressure to model nuisance appearance.
arXiv:2604. 15107v2 Announce Type: replace-cross Abstract: Shapley values provide a flexible framework for attributing feature contributions to model predictions, but they are not naturally suited for feature selection: a feature may receive a positive attribution even when it is redundant given the remaining variables.
arXiv:2608. 16689v1 Announce Type: cross Abstract: Instance-wise feature selection is a valuable tool for interpreting labeled data and the predictions of black-box models.
arXiv:2603. 06054v2 Announce Type: replace-cross Abstract: The use of Vision-Language Models (VLMs) in automated driving applications is becoming increasingly common, with the aim of leveraging their reasoning and generalisation capabilities to handle long-tail scenarios.