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#video-models

3 curated events
papersTODAY 04:00 UTC

LPA-CWM: Learned Adjudicator Improves Motion Reasoning in Counterfactual World Models

This arXiv paper introduces LPA-CWM, a learned adjudicator that weighs the outputs of counterfactual world models when extracting motion from pretrained video predictors. The authors note that predictions produced under different target-frame masks differ in reliability, making uniform weighting suboptimal. Their approach learns to score and combine those predictions rather than treating them equally.

research/video-models/world-models/arxiv
papersTODAY 04:00 UTC

Probing Video Foundation Models for Intuitive Physics Representations

A study examines whether pretrained video foundation models hold intuitive-physics information in their frozen representations. Using frozen-feature probing on the IntPhys2 benchmark, the authors compare how this information differs across model families, layers, and probe types. The work is a cross-listed arXiv replacement submission in cs.AI and cs.LG.

video-models/probing/physics/research
papersTODAY 04:00 UTC

Study finds video models store correct physics but fail to apply it

A new arXiv paper asks whether video generators that produce physically implausible motion never learned the correct dynamics or merely fail to use what they learned. The authors introduce a notion of causal writability to probe this, and report that the correct motion is still represented inside the model and can be made to steer generation. The finding points to a gap between internal knowledge and how it is used, rather than a simple absence of physical understanding.

video-models/physics/interpretability/generative-models