LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editing
Source: https://arxiv.org/abs/2402.10294
Structured fields
{
"checked_at": "2026-10-17T00:00:00.000Z",
"usefulness": "Research system where an LLM agent plans and executes editing actions over automatically generated text descriptions of footage; user study on agent-assisted editing. An early reference for agent-driven editing architectures.",
"source_type": "paper",
"availability": "available",
"next_check_at": "2026-10-24T00:00:00.000Z",
"known_limitations": [
"Small user study (8 participants); no claim of code availability verified."
]
}Evidence claims
- [upstream_claim] arXiv:2402.10294, submitted 15 Feb 2024 (cs.HC), authors Bryan Wang, Yuliang Li, Zhaoyang Lv, Haijun Xia, Yan Xu, Raj Sodhi; LLM agent plans and carries out editing actions using generated footage descriptions; user study with eight participants; accepted to ACM IUI 2024. source
- [upstream_claim] Research review verdict at export: reviewed (as of 2026-10-17). Recorded by the research pipeline, not a hub review outcome; evidence.review stays unreviewed until an independent reviewer records one.
Rights
| component | license | hosting | redistribution | modification | training | Evidence and notes |
|---|---|---|---|---|---|---|
| documentation | unknown | unknown | unknown | unknown | unknown | arXiv license for this paper not read; no code release was confirmed from the abstract page. |
Linked records
- What is a measurable benchmark for agent-made rough cuts? discusses → this