Does Video Memory Use What It Retrieves? A Causal Audit of Memory Specificity
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Abstract (arXiv)
Video models increasingly use memory to preserve information over long sequences, with the assumption that gains come from retrieving and using the correct past content. Standard memory ablations test whether memory helps, but not whether the retrieved content is responsible. We test this directly with read-time memory substitution, which replaces the consumed memory value while leaving the rest of the computation unchanged. This separates memory benefit from memory specificity, the extent to which the gain depends on retrieved content. Across frozen video world models, identity-free controls containing no evaluation-specific content recover essentially the full benefit on Ego-Exo4D and 7-Scenes and about 70% on TUM. In the Ego-Exo4D dose response, recovery falls from 102% to 1% as these values move away from observed training-memory representations, supporting representation repair as the best-supported explanation in this setting. WorldMem shows graded dependence. A wrong memory from the same trajectory recovers 94.1% of the PSNR benefit relative to zero content, while a donor from a disjoint trajectory and biome recovers 43.7%. SAM 2 shows strong content dependence. On DAVIS, replacing the correct spatial memory with a valid wrong memory reduces mean region and boundary score from 0.926 to 0.182. At MOSEv2 reappearance, it falls from 0.459 to 0.000. These results show that memory gains can depend on generic representation support, broader context, or exact episodic content. Read-time substitution provides a direct way to distinguish them.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 37 references that are in the map. First posted on arXiv 10 Sep 2026.
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References · 22
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
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- 2024DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planningcited by 386
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- 2014Memory Networkscited by 363
- 2024MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understandingcited by 229
- 2016Key-Value Memory Networks for Directly Reading Documentscited by 223
- 2020Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Biascited by 191
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- 2025Video World Models with Long-term Spatial Memorycited by 136
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- 2025MOSEv2: A More Challenging Dataset for Video Object Segmentation in Complex Scenescited by 62
- 2026Echo-Memory: A Controlled Study of Memory in Action World Modelscited by 6
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- 2026Learning to Retrieve: Internalizing Memory Retrieval for Video World Models
- 2026Visual Agentic Memory: Enabling Online Long Video Understanding via Online Indexing, Hierarchical Memory, and Agentic Retrieval
- 2026What Should a Streaming Video Model Remember?
- 2026The Past Frames the Future: Memory for Autoregressive Video Generation
- 2026Present but Not Remembered: Auditing How Frozen VLAs Encode, Deploy, and Steer Visual History
- 2026FlashBack: Knowing When to Remember in Streaming Vision-Language Models
- 2026Beyond Retrieval: Progressive Latent Memory Evolution for Streaming Video Understanding
- 2026WorldMemBench: A Comprehensive Benchmark for Memory of Video World Models