While generative AI has significantly advanced video editing, existing methods primarily focus on single-shot or short video clips. Editing long videos with multiple instructions remains a formidable challenge. Naive chunking strategies, e.g., fixed-duration segmentation, often lead to entity fragmentation, severe editing hallucinations, and disrupted temporal continuity. To bridge this gap, we introduce the Multi-Instruction Multi-Shot Long-Video Editing (MMLVE) task, which is structured around three core objectives: Cross-Shot Editing Consistency (CSEC), Multi-Instruction Decoupling (MID), and Zero-Destruction on Spatiotemporal Structure (ZDSS). To tackle these three unique challenges, we introduce an agentic editing framework that leverages the synergy of Large Language Models (LLMs) and Vision-Language Models (VLMs) to achieve shot-level video decoupling and precise instruction parsing. Furthermore, to comprehensively evaluate this task, we construct MMLVE-Bench, which is an MMLVE-focused dataset characterized by complex real-world spatiotemporal dynamics, high-density heterogeneous instructions, and sparse, random entity distributions. Three MMLVE-focused evaluation metrics are further exploited to assess the quality of the editing results. Extensive experiments demonstrate that our MMLVE-Agent outperforms existing closed-source SOTA approaches (e.g., Seedance 2.0), successfully eliminating editing hallucinations, preserving cross-shot editing consistency, and attaining seamless spatiotemporal transitions.
Figures & tables
Figure 1: Task Definition of MMLVE. The core task entails three key constraints: (1) CSEC : Cross-Shot Editing Consistency, preserving entity attributes across transitions; (2) MID : Multi-Instruction Decoupling, ensuring independent execution of diverse commands; and (3) ZDSS : Zero-Destruction on Spatiotemporal Structure, maintaining non-edited backgrounds and temporal continuity.
Figure 2: An overview of our MMLVE-Agent framework. The pipeline comprises three core modules: (1) Instruction & Video Analysis, where the input long-video is segmented into physical shots via PyDetect. Then, an LLM Agent parses and decouples complex user instructions, while a VLM Agent extracts keyframes and conducts shot-by-shot plot analysis to capture scene dynamics; (2) Global Memory Card Making, where target entities are retrieved via VLM voting, and the Global Memory Card, a global visual editing anchor, is generated. This card is iteratively refined through an image-level Pos-Neg Editing Feedback (P-NEF) to ensure Cross-Shot Editing Consistency (CSEC); and (3) Multi-Shot Video Editing, which executes retrieval-based on-demand editing. A video-level P-NEF loop self-corrects the generated shots to precisely ensure Multi-Instruction Decoupling (MID) and Zero-Destruction on Spatiotemporal Structure (ZDSS) in each segmented shot before the final video combination.
Figure 3: Pos-Neg Editing Feedback (P-NEF) mechanism. When faced with complex instructions (grid-shape reference image or multiple instructions), the initial generation often suffers from attribute interference (e.g., incorrectly extracting the left man’s facial ID). To address this, the VLM evaluator generates dual feedback: a Negative Prompt ( Pneg ) to correct errors and a Positive Prompt ( Ppos ) to act as a balancer for the model’s attention. Crucially, as shown in the bottom row, solely relying on the Negative Prompt (NEF w./o. positive) causes the model to focus more on “avoid items” in the prompt, resulting in changes to parts that do not require editing (e.g., the T-shirt’s feature of the man).
Figure 4: An example in MMLVE-Bench. The figure illustrates a typical complex user prompt associated with a multi-shot long-video in MMLVE-Bench. To highlight the heterogeneous nature of the editing commands, different instruction types are color-coded: text in grey denotes a MODIFY instruction, text in blue indicates ADD, and text in green represents DELETE.
Figure 5: Compared with the baseline methods, only MMLVE-Agent (ours) achieves Cross-Shot Editing Consistency (CSEC), Multi-Instruction Decoupling (MID), and Zero-Destruction on Spatiotemporal Structure (ZDSS).
Method
CSEC ↑
MID ↑
ZDSS ↑
Avg. ↑
Seedance 2.0 †
77.58
78.58
82.25
79.47
Kling o3 †
70.00
72.57
69.13
70.57
HappyHorse 1.0
74.68
67.28
66.88
69.61
MMLVE-Agent ( ours )
84.80
79.04
81.68
81.84
Table 1: Quantitative evaluation results on MMLVE-Bench. BOLD : best performance. † means there are 1-2 scenarios in which this method rejects processing due to its safety mechanism (are ignored in the mean calculation of the metrics).
Figure 6: Visual Comparison between P-NEF and NEF.
Figure 7: HTML Interface for User Study.
Appendix figures & tables11 assets
Supplementary material from the paper’s appendix.
Appendix
Method
Rank Distribution (%)
Top-1
Pairwise
Avg. Rank
1st
2nd
3rd
4th
(%) ↑
Win (%) ↑
↓
Seedance 2.0 ∗
4.7
48.8
20.9
25.6
4.7
42.4
2.67
Kling o3 ∗
17.1
22.0
41.5
19.5
17.1
44.6
2.63
HappyHorse 1.0 ∗
4.4
15.6
40.0
40.0
4.4
24.8
3.16
Ours
75.6
17.8
2.2
4.4
75.6
87.6
1.36
Appendix
Table 2: User study results. “Pairwise Win” is the head-to-head win rate against all other methods within the same ranking. ∗ denotes p<0.001 (two-sided Wilcoxon signed-rank test against ours on paired normalized preference scores).
Figure 8: Video Comparison in Media Supplement part.
Figure 9: The cases (sub_001 - sub_005) in MMLVE-Bench.
Figure 10: The cases (sub_006 - sub_010) in MMLVE-Bench.
Figure 11: The cases (sub_011 - sub_015) in MMLVE-Bench.
Figure 12: The cases (sub_016 - sub_020) in MMLVE-Bench.
Figure 13: The cases (sub_021 - sub_025) in MMLVE-Bench.
Figure 14: Global Memory Card Show.
Figure 15: Compared with the baseline methods.
Method
scene
1.1
1.2
1.3
1.4
1.5
CSEC (1.)
2.1
2.2
2.3
2.4
2.5
MID (2.)
3.1
3.2
3.3
3.4
3.5
ZDSS (3.)
MMLVE-Agent
sub_001
18
19
19
19
19
94
15
19
20
19
20
93
18
18
20
19
18
93
sub_002
19
20
19
19
19
96
20
20
20
20
20
100
19
20
20
20
19
98
sub_003
20
20
20
20
20
100
20
20
20
20
20
100
20
20
20
20
20
100
sub_004
12
14
15
8
10
59
11
18
6
8
18
61
12
16
20
10
10
68
sub_005
20
11
19
19
19
88
14
20
20
19
20
93
20
20
20
20
19
99
sub_006
20
20
20
19
19
97
19
20
20
20
20
99
14
19
20
20
18
91
Appendix
Table 3: Detail Evaluation Results for MMLVE-Agent and Happyhorse 1.0.
Method
scene
1.1
1.2
1.3
1.4
1.5
CSEC (1.)
2.1
2.2
2.3
2.4
2.5
MID (2.)
3.1
3.2
3.3
3.4
3.5
ZDSS (3.)
Kling o3
sub_001
12
18
18
16
18
82
17
20
20
20
20
97
5
5
2
8
10
30
sub_002
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
sub_003
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
sub_004
5
5
8
6
8
32
5
6
5
4
10
30
10
12
20
8
10
60
sub_005
14
8
12
14
12
60
10
15
15
15
15
70
10
10
5
12
12
49
sub_006
11
9
12
10
8
50
8
6
5
10
7
36
5
4
2
8
8
27
Appendix
Table 4: Detail Evaluation Results for Kling o3 and Seedance 2.0. “ / ” means the method rejects processing due to its safety mechanism for the scene.