mlsanityData to 7 Oct 2026

Diagnosing Under-Development of Irreversible Processes in Video Generation

arXiv 2608.00617 ↗First posted 1 Aug 2026cs.CVcited by 0

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Abstract (arXiv)

Many physical attributes are irreversible: ice melts but does not re-freeze, paper chars but does not un-burn. Do video generators respect this? We show the question is hard to measure, and that what can be measured reliably is development rather than reversal. Metrics of local reversal are null-degenerate: a per-clip violation rate scores 0.50 on pure noise, and a variance-normalized reversal residual sits at its noise ceiling. What survives null-testing is a two-part protocol: progress (a directional attribute correlation) and a stasis rate. Under this protocol, generated video separates cleanly from real footage, and the gap is human-validated. Across seven text-to-video models, real reference footage advances (ρ=+0.40, 35% static) while every generator shows near-zero progress and 92--100% stasis; nine annotators rate real footage far above generated (2.75 vs. 0.99 on a 0--4 scale). The reliable finding is under-development: generators barely advance irreversible attributes rather than reversing them. As a complementary mechanism, we show that post-hoc readout guidance is gameable, whereas enforcing monotonicity by construction in a disentangled attribute latent removes the gameable readout, validated in controlled and semi-synthetic settings.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 34 references that are in the map. First posted on arXiv 1 Aug 2026.

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31 of 34 references.

References · 31

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
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  12. 2024Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generationcited by 167
19 more
  1. 2025Do generative video models understand physical principles?cited by 151
  2. 2025WorldModelBench: Judging Video Generation Models As World Modelscited by 116
  3. 2025WISA: World Simulator Assistant for Physics-Aware Text-to-Video Generationcited by 87
  4. 2024Constrained Synthesis with Projected Diffusion Modelscited by 83
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  6. 2025PISA Experiments: Exploring Physics Post-Training for Video Diffusion Models by Watching Stuff Dropcited by 49
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  8. 2022Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videoscited by 39
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  13. 2023VideoDreamer: Customized Multi-Subject Text-to-Video Generation with Disen-Mix Finetuning on Language-Video Foundation Modelscited by 16
  14. 2026Physical Simulator In-the-Loop Video Generationcited by 12
  15. 2026Phantom: Physics-Infused Video Generation via Joint Modeling of Visual and Latent Physical Dynamicscited by 10
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  18. 2026OSCBench: Benchmarking Object State Change in Text-to-Video Generationcited by 3
  19. 2025FluencyVE: Marrying Temporal-Aware Mamba with Bypass Attention for Video Editingcited by 2

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