---
title: "Editing the Moving World: Model Editing for Video LLMs (2026)"
description: "The authors build VMEB, which they present as the first benchmark for editing Video LLMs (Vid-LLMs). 50 references."
authors: ["Qian Zhang", "Xinye Li", "Xiaokai Wu", "Junhao Xu", "Zhanyue Qin", "Qingbin Liu", "Junxian Cai", "Xi Chen", "Bolin Zhang", "Zhiying Tu", "Dianhui Chu", "Xiaoyan Yu", "Dianbo Sui"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.274/"
doi: "10.18653/v1/2026.acl-long.274"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2026-editing-moving-world-model-editing/"
---
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# Editing the Moving World: Model Editing for Video LLMs

**Authors:** [Qian Zhang](https://md.research.elamur.ai/free/en/authors/qian-zhang/), Xinye Li, Xiaokai Wu, Junhao Xu, Zhanyue Qin, Qingbin Liu, Junxian Cai, [Xi Chen](https://md.research.elamur.ai/free/en/authors/xi-chen/), Bolin Zhang, Zhiying Tu, Dianhui Chu, Xiaoyan Yu and Dianbo Sui · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.274/) · [PDF](https://aclanthology.org/2026.acl-long.274.pdf) · [DOI 10.18653/v1/2026.acl-long.274](https://doi.org/10.18653/v1/2026.acl-long.274) · [OpenAlex](https://openalex.org/W7166809372)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Model (knowledge) editing has mostly been studied for text-only LLMs and image-based multimodal models. It remains unclear whether edits work for models that reason over video. The authors build VMEB, which they present as the first benchmark for editing Video LLMs (Vid-LLMs). Each instance pairs a video and a question with the model's original answer and a desired new answer. VMEB measures the familiar Reliability, Generality and Locality, with Locality split into text and video parts. It also adds a Robustness metric, which checks whether an edit survives signal-level changes to the video such as mirroring, noise, black frames, frame skipping or altered playback speed. Six editing methods are adapted to three Vid-LLM families, and none performs well on every axis. FT damages the model, and SERAC edits weakly. MEMIT and AlphaEdit struggle because they target single text tokens, while visual meaning is spread across many embeddings. Several methods still succeed when the video is blanked out, which suggests they shift answer preferences rather than visual understanding. The authors argue that video editing needs dedicated methods rather than adapted text techniques.

## Key points
- Models tested: LLaVA-NeXT-Video (7B), Qwen2.5-VL (3B and 7B) and Qwen3-VL (8B).
- Editing methods: FT, IKE, MEND, SERAC, and the locate-then-edit methods MEMIT and AlphaEdit. The locate-then-edit methods were run with the EasyEdit framework, which edits the final text token.
- Metrics:
  - Reliability: token-level accuracy on the edit target.
  - Generality: performance on rephrased questions.
  - Text and video Locality: how much unrelated behaviour is preserved.
  - Robustness: stability of the edit under perturbed videos (a new metric).
- "Visual blindness" finding: FT and IKE scores barely change when the input video is replaced with black frames. MEND drops on video locality and robustness, likely because black videos are out-of-distribution inputs for its hypernetwork. SERAC loses reliability, possibly because its scope classifier misfires.
- The authors note limitations: only synthetic perturbations were used, the range of architectures is limited, and localization strategies are fixed. The benchmark is released at github.com/Sakabamrisa/VMEB.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [Tencent](https://md.research.elamur.ai/free/en/companies/tencent/) — Company, Shenzhen, China · 163 works · 21,032 citations
- [Nanyang Technological University](https://md.research.elamur.ai/free/en/universities/nanyang-technological-university/) — University, Singapore, Singapore · 184 works · 27,484 citations

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## Cite

Cite as: Qian Zhang, Xinye Li, Xiaokai Wu, Junhao Xu, Zhanyue Qin, Qingbin Liu, Junxian Cai, Xi Chen, Bolin Zhang, Zhiying Tu, Dianhui Chu, Xiaoyan Yu and Dianbo Sui. 2026. Editing the Moving World: Model Editing for Video LLMs. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhang2026editing,
  title = {{Editing the Moving World: Model Editing for Video LLMs}},
  author = {Qian Zhang, Xinye Li, Xiaokai Wu, Junhao Xu, Zhanyue Qin, Qingbin Liu, Junxian Cai, Xi Chen, Bolin Zhang, Zhiying Tu, Dianhui Chu, Xiaoyan Yu and Dianbo Sui},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.274},
  url = {https://doi.org/10.18653/v1/2026.acl-long.274}
}
```

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*Updated 2 November 2026*