跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文,不再错过。

覆盖计算机全学科,以 AI 为快捷入口。关注方向、作者和关键词,把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+788新增论文
+627其中 AI 相关
+81已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描:2026-11-09 11:20 · 全站最近入库:2026-11-09 11:19各信源进度 →

统计口径与数据新鲜度

统计 2026-11-08 12:51 — 2026-11-09 12:51(北京时间) · 点击数字查看对应简报;AI 相关包含在新增论文内。

最近完成检查:2026-11-09 11:20最近发现新增版本:2026-11-09 11:19库内论文最新更新时间:2026-11-06 21:58

每分钟刷新已保存的数据。空检查不代表采集异常;新收录可能包含历史论文。AI 导读按新增版本计数。

17,888 已收录论文15,288 AI 相关4,419 有修订112 HF 社区发现

浏览论文库

时间范围跟随排序:版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常,查看状态 →

找到 1 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-09 11:19(北京时间)
版本更新 · v3arXiv:2610.37690

Cassette-Editor: An Agent Harness for Editing Video the Way You Do

Zhixin Zeng, Jiahao Zhong

原文摘要

Coding agents work in an environment built for them: a compiler or a test checks each change and points to the fault, and version control keeps every change in a project people can continue. Video editing agents lack one: an edit's correctness shows only in the costly rendered video, which does not point to the decision at fault, and existing systems offer either editing operations that cover only anticipated edits or a script whose output is a video the user cannot keep editing. We present Cassette-Editor, a complete agent harness for video editing that lets the agent edit the way a person does: knowing what the project allows, seeing what each change affects, and revising from there. The agent writes patches of typed changes to the editor's timeline document. A schema check and a rulebook shared with manual editing check each patch before it commits; a rejected patch leaves the project unchanged and returns each problem located with repair options, and an accepted one becomes a step of the history the user also edits. Reads map source analysis to the frames of the current edit. Because manual gestures are writes in the same language, folded by the same executor and rulebook, the agent can express every change a manual edit makes to the project. On 31 editing requests with the model held fixed, the complete harness passed 89% of trials against 47% for the same model with ten basic editing operations and without the rulebook, at less than half the agent time and cost per successful request; the loss fell on requests at rule boundaries, composite edits, and undo, not on finding content.

cs.MM · cs.AI