LongLive: Real-time Interactive Long Video Generation
Authors: Shuai Yang
Wei Huang
Ruihang Chu
Yicheng Xiao
Yuyang Zhao
Xianbang Wang
Muyang Li
Enze Xie
Ying-Cong Chen
Yao Lu
Song Han
Yukang Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Real-time
Interactive
Long Video Generation
Primary area: generative models
TL;DR: We present LongLive, that is a real-time, interactive, and AR framework for long video generation.

Abstract: We present LongLive, a frame-level autoregressive (AR) framework for real-time and interactive long video generation. Long video generation presents challenges in both efficiency and quality. Diffusion and Diffusion-Forcing models can produce high-quality videos but suffer from low efficiency due to bidirectional attention. Causal attention AR models support KV caching for faster inference but often degrade in quality on long videos due to memory challenges during long-video training. In addition, beyond static prompt-based generation, interactive capabilities, such as streaming prompt inputs, are critical for dynamic content creation, enabling users to guide narratives in real time. This interactive requirement significantly increases the complexity, especially in ensuring visual consistency and semantic coherence during prompt transitions. To address these challenges, LongLive adopts a causal, frame-level AR design that integrates a KV-recache mechanism that refreshes cached states with the new prompt for smooth, adherent switches streaming long tuning to enable long video training and to align training and inference (train-long–test-long); and short window attention paired with a frame-level attention sink, preserving long-range consistency while enabling faster generation. With these key designs, LongLive fine-tunes a 1.3B-parameter short-clip model to minute-long generation in just 32 GPU-days. At inference, LongLive sustains 20.7 FPS on a single NVIDIA H100, achieves strong performance on VBench in both short- and long-video settings. LongLive supports up to 240-second videos on a single H100 GPU. With FP8 quantization, LongLive boosts inference to 24.8 FPS with marginal quality loss.

Published as a conference paper at ICLR 2026
LONGLIVE: REAL-TIME INTERACTIVE LONG VIDEO GENERATION
Shuai Yang!.3
Wei Huang
Ruihang Chu
Yicheng Xiao
Yuyang Zhao Yao Lu¹
Xianbang Wang
Muyang Li
Enze Xie
Yingcong Chen
Song Han 1.2
Yukang Chen
INVIDIA
MIT
HKUST(GZ))
'HKU
TTH
ABSTRACT
We present LONGLIVE, frame-level autoregressive (AR) framework for real- time and interactive long video generation. Long video generation presents chal- lenges in both efficiency and quality. Diffusion and Diffusion-Forcing models can produce high-quality videos but suffer from low efficiency due to bidirectional attention. Causal attention AR models support KV caching for faster inference, but often degrade in quality on long videos due to memory challenges during long-video training. addition, beyond static prompt-based generation, interac- tive capabilities, such streaming prompt inputs, critical for dynamic content creation, enabling users to guide narratives in real time. This interactive require- ment significantly increases complexity, especially in ensuring visual consistency and semantic coherence during prompt transitions. To address these challenges, LONGLiVE adopts causal, frame-level AR design that integrates KV-recache mechanism that refreshes cached states with new prompts for smooth, adherent switches; streaming long tuning enable long video training and align train- ing and inference traat-long--est-lonn):: and short window attention paired with a frame-level attention sink, shorten as frame sink, preserving long-range con- sistency while enabling faster generation. With these key designs, LONGLIVE fine-tunes a T.BB-parameter short-clip model to minute-long generation just 32 GPU-days. At inference, LONGLIVE sustains 20.7 FPS on single NVIDIA H100, achieves strong performance on VBench in both short and long videos. LONGLIVE supports up to 240-second videos a single H100 GPU. LOnGLivE further supports INT8-quantized inference with only marginal quality loss. our code is available at: https: /ttps::///gttttt /ggthtttt...//..// com NVlabs LongLive
User enters prompts soqurntially.
Langl generates videos ral time.
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LangLäve Quality
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Visual Quality
20.0%
14.7% 11.5
51.5%
17.0%
10s
206
304
4bs
50s
60s
Study
Figure 1: The workflow of LONGLIVE. LONGLIVE accepts sequential user prompts and gener- ates corresponding videos in real time, enabling user-guided long video generation. The 60-second sequence shown is example, LONGLIVE supports up to secns videos in single H100 GPU.
INTRODUCTION
Long video generation is essential for advancing creative, educational, and cinematic applications. It enables coherent storytelling, richer scene development, and more complex temporal dynamics

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Published as a conference paper at ICLR 2026
Prompt
Key
New Prompt
Previous Video
Prompt
Prompt
Attention
Prompt
Recached tokens
KV re-eache
to maintain adherence across prempt switches
Frame
Short Window
Sink
Attention
The framework LongLive. (Left) LONGLIVE processes sequential user prompts and generates a corresponding long video using efficient short window attention and frame sink. Com- pared the normal attention window of 5s, our short window only uses half the size, with the help of frame sink, which maintains the long-range consistency. (Right) To maintain consistency when the prompt switches, LONGLIVE employs a KV-recache technique that updates cached key-value states by combining previous videos with new prompt embeddings through cross-attention layers.
Figure 2:
than short clips provide. However, static prompt-based generation limits adaptability once the process has commenced. It is difficult for users to conceive highly detailed, long-fome prompts in single step. Beyond simply producing long videos, the ability interact alongside the genera- tion process, such as streaming prompt inputs during runtime, opens new possibilities for adaptive content creation. This interactive paradigm enables users guide narratives, adjust visual styles, or introduce new elements on the fly. Therefore, interaction makes long video generation controllable.
Interactive long video generation poses difficulties in both quality and efficiency. For the quality perspective, it is difficult to maintain smooth, consistent, and coherent transitions when switching between user prompts during generation. Even subtle mismatches in visual style, motion continuity, or scene layout can disrupt the narrative flow and reduce the overall realism of the video. For the efficiency perspective, the computational and memory demands scale rapidly with video length. For example, generating 180-second video with the Wan-2.1 (Wan et al., 2025) model requires pro- cessing over one million tokens, which computationally prohibitive. addition, in an interactive setting, prolonged user waiting times severely degrade the overall user experience.
Existing video generation methods have limitations in long video generation. For diffusion-basel video generation models (Wan al., 2025; Kong et al., 2024; Yang al., 2025; Wei et al., 2025; OpenAl, 2024; Kuaishou, 2024) and cictusioonfoorcig models (Chen et al., 2024a; 2025a; Zhang & Agrawala, 2025), although they produce high-quality short clips, their reliance bidirectional attention makes inference inefficient. The bidirectional attention prevents KV (key-value) cache technique, leading redundant computation and prohibitive latency for long videos. For example, SkyReels-V2 (Chen et al., 2025a) requires approximately 50 minutes on an H100 GPU to generate 60-second video. For AR models with causal attention, they can leverage cached KV states for faster inference, but they often exhibit degraded quality when generating long videos. Due to the high cost of directly training on long videos, existing AR models (Huang al., 2025; Teng et al., 2025) typically adopt train-short-test-e video length increases. the interactive setting involving prompt switching, error accumulation, and loss of temporal coherence over time further result in visual artifacts and inconsistency.
strategy. Consequently, the quality gradually degrades as the
In this paper, we propose LONGLIVE, a real-time interactive long video generation framework, as illustrated in Figure 1. LONGLIVE is causal attention, frame-level AR video generation model, enabling it to inherit the KV cache mechanism for efficient inference. Our key design is KV-recache, prompt embeddings. This
shown in Figure 2, which updates cached states by incorporating technique ensures both smoothness and prompt adherence across prompt switches interactive settings. In addition, for efficient fine-tuning, present streaming long tuning strategy that
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Published as a conference paper at ICLR 2026
preserves consistency between training and inference soain-long-tes--lonee to address the degrada- tion commonly observed in long-video AR generation. For efficient inference, we introduce short window attention combined with a frame-level attention sink (abbreviated as frame sink), which significantly accelerates inference while preserving performance.
In our experiments, LONGLIVE delivers both high efficiency and strong quality for interactive long- video generation. In terms of training efficiency, we fine-tune a 1.3B- -.B--parameter model to produce high-quality minute-long videos in only 32 GPU-days. Training on long videos is essential: not only improves long-horizon fidelity but also enables efficient inference strategies that markedly accelerate decoding. In terms of inference efficiency, LONGLIVE sustains 20.7 FPS on a single NVIDIA H100, supporting real-time interaction and outperforming sfate-of-the-ans art approaches in throughput. In terms of quality, our framework achieves strong VBench scores on both short- and long-video settings. LONGLIVE scales to produce videos up to 240 seconds, on single H100 GPU, while maintaining high visual fidelity and temporal coherence, effectively handling long video generation with little degradation. Moreover, we further enable INT8-quantized inference in LONGLIVE, with only marginal quality loss, as shown in Appendix H.
RELATED WORK
We present core related work here and provide extended discussion with details in the appendix D. A growing number of works (Chen et al., 2024a; Song al., 2025; Mao et al., 2025; Yuan al., 2025; Zhang & Agrawala, 2025; Gao et al., 2025; Henschel et al., 2025) integrate diffusion model- ing with AR prediction, an intermediate paradigm between purely diffusion-based approaches and purely AR approaches. SkyReels-V2 (Chen et al., 2025a) couples diffusion forcing with a film- structure planner and multimodal controls. Recent efforts (Yin et al., 2025; Huang al., 2025; Gu et al., 2025; Teng et al., 2025) have advanced causal AR-based models for long video generation. StreamDiT (Kodaira et al., 2025) trains a diffusion model with window attention, but has potential drift or detail loss over long streams. AAPT (Lin et al., 2025) transforms pre-trained latent video diffusion model into a 1 INFE, real-time streaming generator using adversarial post-training and sup- ports interactive control via camera and human pose, whereas LongLive instead uses diffusion-based instribution-matching and train-long-test-lon without GAN post-training Most recently, Self-forcing (Huang et al., 2025) addresses the gap in AR video diffusson by simulating inference conditions during training, rolling out generation with KV cache, and conditioning on model outputs. MAGI-1 (Teng al., 2025) scales AR video generation to large models and datasets through chunk-wise prediction, but its prompt switching requires manual adjustment of K VV-cache windows different steps.
distillation for text-driven, multi-minute generation
3 METHOD
3.1 KV RECACHE
Causal AR models naturally support interactive prompt switching, but this ability is limited. Dis- carding all prior KV cache at the switch improves adherence to the new prompt, yet it introduces abrupt visual changes and temporal discontinuities, as shown in Figure 3 (a). Conversely, retain- ing the entire KV cache often prevents the model from following new prompts, or adapting to new prompts after delay, because the cache is saturated with information from the previous prompt, as shown in Figure: 3 (b). Based on this observation, we first diagnose why prompt switching is hard for streaming video generators. In DiT (Peebles Xie, 2023) architectures, cross-attention and self-attention layers alternate. During generation, large amounts of information from the previous prompt are repeatedly injected through
layers and then propagated forward by self- attention, so that this prompt signal is written into the running KV cache. Consequently, when the prompt is switched, the certain instances, this results in inconsistent adherence to the new prompt.
still carries residual semantics of the old prompt in the cache. And in
To address this issue, we introduce KV recache. At a prompt switch boundary, we recompute the KV cache using the already generated frames together with the new prompt, effectively crasing residual information from the previous prompt while keeping the motion and visual cues that guarantee temporal continuity. Concretely, at the first swsthh frame, we encode the generated
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0s
Prompt1
56
Ambitious young man in shorp suit stonds orms-crossed, slight confident smile, bustling modern office behind him.
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Inconsistent visuals
(a) w/o KV cache
Prompt2
10s
(b) w/ KV cache
0s Prompt1
5s Prompt2
X New prompt ignored
He uncrosses his
orms, focus
tightening os he about to address the team: some office backdrop, medium shot, static.
10s
Prompt1
Prompt2
Consistent
nCW prompt
(c) KV re-eache
Figure 3: Prompt switching under different KV-cache strategies. (a) w/o KV cache: New prompt takes effect, but transitions are abrupt and visuals are inconsistent. (h) w/ KV cache: Smooth continuity, but the new prompt is not followed (lag or ignore). (c) KV re-cache: Smooth, visually consistent transitions with full new-prompt compliance.
video prefix as the visual context and pair it with the next prompt to rebuild the cache; subsequent steps then proceed normally using this refreshed cache. In this way, the cache retains the visual state of the ongoing video, but the prompt semantics now cleanly correspond to the active prompt, enabling improved semantic alignment without visual discontinuities.
To ensure train- inferenceee alignment, we integrate the recaching operation into our training loop (Figure 4). When training iteration contains a prompt switch, we (i) perform recache once, (ii) continue rollout with the updated cache, and (iii) in distillation, feed the teacher model with the new prompt as well, so the student is supervised under the exact post-switch condition will face at inference. This training scheme further removes the train-inference mismatch. Models trained with recache therefore exhibit both strong temporal smoothness and fast semantic convergence to the next prompt at inference, as illustrated in Figure 3 (c). In terms of efficiency, recaching is invoked only once per training sample. The added cost is thus minimal; for a 1Os video with single switch, recaching introduces only about 6% extra time cost compared to recaching usage.
Moreover, although training includes only one prompt switch per long sequence, this mechanism generalizes well during inference. The model supports interactive inference with multiple prompt switches by performing a single recaching step at each boundary. Given n + prompts and n switch points, the generator rolls out causally, applies KV recaching each switch, and continues producing frames semantically aligned with the active prompt while maintaining smooth transitions. detailed illustration of this procedure is outlined in Appendix Algorithm 2.
3.2 STREAMING LONG TUNING
LongLive builds upon causal frame-level AR video generators. These models are trained only on short clips. At inference, they produce long videos via rolling, fixed-length context window that repeatedly feeds the model its own outputs. As the rollout continues, small prediction errors accu- mulate and the context inside the window becomes progressively noisier, so model conditions on more degraded self- in training, this eeff-generated history. Since such long-range, self-generate contexts were absent shhotttest-looe regime induces content drift and breaks consistency over long horizons. To address this mismatch, we propose train-long testog-est-oongg strategy. During training, the model synthesizes long sequences by conditioning on its own imperfect predictions, with supervi- sion applied throughout the entire rollout. This exposes the model to extended, self-generated, and progressively degraded frames already in training. aligning training with inference, mitigating error accumulation improve fidelity and consistency.
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(a) Short Tuning
fter
Mer
Ber 3
Snident
Teacher
Studeut
Teacher
Stadent
Teacher
Oc
(b) Naive Long Tuning
Supervise
OOM Incomeet Supervision
Nee
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Student
(c) Streaming Long Tuning
Jter fl
Her n
kistory
Gav
Atstory
Student
Teacher
Stadent
Tescher
Os
5(n-1)s
5(n-13s
5n
5(t+1)
Figure 4: The streaming long tuning pipeline. (a) Short tuning: only 5s clips are supervised, like Self-Forcing (Huang et al., 2025), leading to quality loss on long videos. (b) Naive long tuning: naively scaling long sequences causes incorrect teacher supervision and OOM. (c) Streaming long tuning: our approach trains on long sequences by reusing the historical KV cache each iteration generate the next 5s clip, then supervising with the teacher.
Self- self-superviiion (Huang et al., 2025) methods are able to avoid collecting large long-video dataset. requires no real video data: a pretrained teacher provides synthetic supervision that guides the student to match the teacher's output distribution. However, two practical challenges arise in this method. First, the teacher itself typically trained for short clips and thus cannot reliably supervise an entire long sequence end-to-end. Second, naïvely unrolling and backpropagating through long sequences easily triggers out-of-memory (OOM) issues and is computationally wasteful.
To address these two challenges, we introduce streaming long tuning procedure (Figure 4) that learns on long videos while keeping memory and supervision local and reliable. the first itera- tion, the generator samples short video clip (e.g., 5s) from scratch, and we apply DMD (Yin al., 2024b;a) on this short clip. In subsequent iterations, the generator extends the short clip from the previous iteration, and produce the short clip conditioned on the previously stored KV cache, and we again apply DMD only to this newly generated clip. We repeat this rolling extension until the video reaches a preset maximum length, then fetch new batch and restart from scratch. This schedule mirrors the inference-time rollout and thus reduces train-test inconsistency. At each itera- tion, the teacher provides reliable supervision for the current short clip (where it is competent), and the collection of per-clip supervisions provides global guidance for the full sequence. In practice, we detach the already generated frames so they act as a constant causal context. The gradients are computed only for the current generated clip. Consequently, memory usage is limited by the clip duration, avoiding OOM. A detailed illustration of this process appears in Appendix Algorighm 1.
Our study reveals that tuning on long videos is not only critical for the performance of long video generation, but also a prerequisite for efficient long inference strategies. These strategies include window attention and frame sink, which significantly improve inference speed.
3.3 EFFICIENT LONG INFERENCE
Short-window Attention In long video generation, the cost of dense causal attention grows quadratically with the sequence length, making naive inference prohibitive on long videos. Mo- tivated by evidence of temporal locality in video generation: nearby frames contribute more to predicting the next one (Gu et al., 2025; Zhang & Agrawala, 2025), we adopt local window atten- tion during inference and during streaming tuning. Limiting attention to fixed temporal window reduces both computation and memory. Attention complexity becomes proportional to the window size rather than the growing sequence length, and the KV cache needed per layer scales with the window rather than the total video. However, window size introduces a We generate 20-second videos using different attention window settings, shown in the first and
trade-off.
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Os
20s
Os
20s
0s
20s
Frame (Widoww 21)
(Window 21)
(Window 21)
Frame
(Window 12)
(Windoww 12)
w/ Frame Sink (Window 9 Sink
w/ Frame Sink (Window 9 Sink 3)
w/ Frame Sink (Window 9 Sink 3)
Figure 5: Comparison in a 20-second generated video long window attention (Window 21 latent frames), Shoor-wiidoww attention (Window 12), and short-window + frame-sink (Window + Sink 3). Shorter windows boost efficiency but weaken long-range consistency; adding a frame-sink restores consistency while keeping the efficiency gains.
second rows in Figure 5. Larger windows retain more temporal context and yield stronger long- range consistency, but incur higher latency and memory. Shrinking the window improves efficiency at the cost of consisteney, since distant but critical cues disappear from the receptive field.
Frame Sink Prior work reported that attention-sink tokens alone do not prevent long-rollout col- lapse video models (Huang al., 2025). In contrast, we empirically find that attention sinks become effective once long-rollout collapse is addressed via streaming long tuning. Serving as per- sistent global anchors, attention sinks markedly improve long-range temporal consistency, thereby mitigating the quality-efficiency trade-off when using shortw attention. As shown in the third row of Figure 5, adding a frame-sink greatly boosts long-range consistency under a short window while maintaining low cost. Concretely, we fix the first frame chunk of the video global sink tokens; these tokens are permanently retained in the KV cache and concatenated to every attention block's keys and values, making them globally attendable even with local-window attention. The remainder of the KV cache uses a short rolling window and is evicted normally. In experiments, a whndoww with frame-sink preserves high long-video video quality while reducing end-to-end com- pute time by 28% and peak memory by 17% on single H100 GPU.
Consistency between Training and Inference We integrate short-window attention and the frame sink into streaming wning to align train-test behavior and improve efficiency. Let the local attention window W frames and the supervised clip length (from the teacher) be T' frames. At cach training step, keep (i) the KV cache from the last W frames of the preceding context without gradients and (ii) the full KV cache of T frames for the current supervised clip with gradients. We also maintain S sink tokens (the first two frames) that never evicted and concatenated to every layer's ' KV so they remain globally attendable. Consequently, the resident KV size per step is OW++ and does not grow with total video length, preventing OOM on very long rollouts. The sinks stabilize identity and scene semantics, allowing us to train with the same shortened window used inference. For KV re-caching, we rebuild the cache from only the most recent W generated frames, which refreshes semantics while preserving local continuity and saves the re-caching cost.
4 EXPERIMENT
Implementation We build LONGLIVE on Wan2.1-T2V-1.38 (Wan et al., 2025), which produces 5s clips at 16 FPS and 832 x 480 resolution. We first adapt the pretrained model into a few-step causal-attention model using self-forcing (Huang et al., 2025) improved DMD pipeline (DMD DMD2 ftw-step) on VidProM (Wang & Yang, 2024) data, while enabling our short-window atten- tion and the frame sink (we keep all tokens from the first frame chunk sink tokens). We then perform streaming long tuning on a 60s sequence that contains a single prompt switch. To con- struct this switch-prompt dataset, prompt Qwen2-72B, -2B--nnsruttt (Yang et al., 2024a) to generate

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Published as a conference paper at ICLR 2026
Table 1: Comparison with relevant baselines. We compare LONGLIVE with representative open- source video generation models of similar parameter sizes and resolutions. Evaluation scores are calculated on the standard prompt suite of VBench (Huang al., 2024a). FPS single H100 GPU.
Throughpul (FPS)
Evaluation scores
Model
#Params Resolution
Total
Quality
Semantic
Diffusiio models
L.T--Vido (HaCohen et al., 2025)
1.9B
768 832x480
8.98
80.00
82.30
70.79
Wan2.1 (Wan al., 2025)
1.3B
0.78
84.26
85.30
80.09
Autoregressive models
SkyReels-V2 (Chen et 2025a)
1.3B
960x540
0.49
82.67
84,70
74.53
MAGI-1 (Teng el al., 2025)
4.5B
832
0.19
79.18
82.04
67.74
CausVid (Yin et al., 2025)
1.3B
832
17.0
81.20
84.05
69.80
NOVA (Deng et al., 2025)
0,6B
x68x480
0.88
80.12
80,39
79.05
Pyramid Flow (Jin et al., 2025)
640x384
6.7
81.72
84.74
69.62
Self Forcing, chunk-wise (Huang al., 2025)
1.3B
832x480
17.0
84.31
85.07
81.28
Self Forcing, frame-wise (Huang et al,, 2025)
1,3B
832x480
8.9
84,26
85.25
80.30
LONGLIVE
1.3B
832x4800
20.7
84.34:10.52
85.72.11.07
79.62.12.73
Table 2: Interactive long video evaluation: Quality scores are reported on the whole 60s sequence.
CLIP scores are reported on 10s video segments with the same semantics (t higher is better).
Quality Score
CLIP Score
Method
0-10s
10-20
20-30s
30-40s
40-50s
50-60s
SkyReels-V2 V2 (Chen et ol., 2025a)
79.85:20.22
21.34:11.8
23.15:::.85
23.54:2.21
17.95:1.46
20.12:20.97
19.25:0097
Self-Forcing (Huang al., 2025)
82. 15:555
27.92+0.86
25.34=1.75
23.12=1.46
22.45=1.58
23.67=0082
23.55=1877
LONGLIVE
85.02:11112
29.45:1.72
26.15:11003
25.10:21.34
24.85
24.90-1113
24.6511.38
follow-up prompts conditioned on each original VidProM prompt. During training, each iteration continues the model's own rollout by generating the next 5s video clip until a maximum length of 60s is reached; each batch includes exactly one prompt switch with the switch time sampled uni- formly from 5s to 55s. When switch occurs, we apply KV-recache. During streaming long tuning, we also keep the same short-window attention and frame-sink settings. This training procedure takes about 12 hours on 64 H100 GPUs. Notably, LONGLIVE supports any model capable of au- toregressive rollout with a KV cache. We implement LONGLIVE on a linear- ineer--ttention AR model, SANA- Video (Chen al., 2025b), achieving further acceleration long- video generation.
4.1 SHORT VIDEO GENERATION
We first evaluate LONGLIVE's short-video generation on VBench using their official prompts, and compared it with relevant open-source video generation models of similar scale, including LTX-- Video (HaCohen et al., 2025), Wan2.1 (Wan et al., 2025), SkyReels- (Chen et al., 2025a), MAGI- 1 (Teng et al., 2025), Caus Vid (Yin et al., 2025), NOVA (Deng al., 2025), Pyramid Flow (Jin et al., 2025), and Self-forcing (Huang et al., 2025). All scores are normalized using the same numerical system with VBench. On 5-second clips, LONGLIVE matches the strongest baselines in total score, demonstrating excellent quality and stability. as shown in Table 1. Benefiting from the short window attention design, LONGLIVE is also the fastest among all the methods, reaching 20.7 FPS for real- time inference. It shows that LONGLIVE does not degrade the short-clip generation capability.
4.2 LONG VIDEO GENERATION
We evaluate LONGLIVE'S single- prompt long-video generation on VBeech-Long (Huang et al., 2024b) using its official prompt set. For each prompt, we generate 30-second video and split into clips according to the VBench-Long official scripts. We compare against three representative open-source models: SkyReels-V2 V2 (Chen et al., 2025a), FramePack (Zhang & Agrawala, 2025), and Self-Forcing (Huang et al., 2025). Because FramePack is an 12V model, we first synthesize an initial frame from the same text prompt and feed it to FramePack: other T2V models generate directly from the prompt. We report the standard VBench-Long metrics for long- horgzhorizon quality and consistency in Table 3. LONGLIVE achieve the state-of-the-art performance, while being the fastest.

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Published as a conference paper at ICLR 2026
0-10s
10-20s
20-30s
30-40s
40-50s 50-60s
LongLive
SkyReels-V2
Forcing
0-10s: Medium close-up serene
white gown amid drifting sakura petals and soft pink smoke. 10-20s: She slowly lifts her hand, fingertips grazing petal/soft pink smoke
20 20-30s: A gentle breeze rises. The petals float softly in the air, creating dreamy and ethereal atmasphere.
30-40s: She softly closes her while holding the same poised gesture.
40-50s: Her fingers barely touching the delicate pink smoke, while small bird flits in.
50-60s: the bird now perches on her outstretched finger: pink and yan-blue smoke thickens and envelops the scene.
Figure 6: Qualitative comparison for interactive long video generation. LONGLIVE exhibits strong prompt compliance. smooth transitions, and high long-range consistency while sustaining high throughput. Compared to ours, SkyReels-V2 shows weaker long-range consistency, and Self- Forcing faces quality degradation on longer videos.
Table 3: Single-prompt 30s long video evaluation on VBench-Long.
Table Ablation study on KV recache. KV
recache achieves the best consistency and CLIP score.
Total Score
Quality Score
Semantic Score
Throughput (FPS)
Model
Background Consistency
Subject Consistency
CLIP Score
FPS
Method
SkyRoels-V2 V2
75.29
80.77
53.37
0.49
FramePuck
81.95
83.61
75.32
0.92
cache
92.75
89.59
28.95
22.8
Self-Forcing
81.59
83.82
72.70
17.0
cache
94.77
93.69
25.92
21.9
LONGLIVE
83,52
85.44
75.82
20.7
94.04
20.7
4.3 INTERACTIVE LONG VIDEO GENERATION
For interactive long-form videos with multiple prompt switches, few existing methods support true streaming generation. We implemented this setting for two representative baselines: SkyReels- V2 and Self-Forcing. We then compare our approach against them. Because the standard VBench proto- col is not directly applicable, we curated a custom set of 160 interactive 60-second videos, each com- prising six successive 10-second prompts as the validation set. For long-horizon quality, we evalu- ate our 60s interactive videos on VBench-Long dimensions that support customized prompt videos, including subject consistency, background consistency, motion smoothness, aesthetic quality, and imaging quality. For semantic adherence, we segment each video at prompt boundaries and com- pute clip-wise semantic score using CLIP (Radford et al., 2021) scores. Qualitative and quantitative results are shown in Figure 6 and Table 2, respectively. LONGLIVE exhibits strong prompt com- pliance, smooth transitions, and high long-range consistency while sustaining high throughput. In contrast, Self-Forcing degrades longer horizons and, SkyReels-v2 shows weaker consistency. In terms of speed, LONGLIVE is more than x faster than SkyReels-v2 and slightly faster than Self- Forcing, even with KV re-cache, thanks our short-window attention design. Please our project page for more qualitative comparisons for interactive long video generation. Finally, user study in which participants rated Overall Quality, Motion Quality, Instruction Following, and Visual Quality, i.e., Figure (right) further supports the effectiveness of our approach.
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Consistency Score
94.1
94.2
Time (ms)
92.5
90.6
93.3
92.0
93.7
94.2
Time (ms)
800
Memory
20
Memory (GB)
90.0
89.7
Baseline (Window Arts)
600
87.5
665.88
Ours (Window 9 Sink 3)
85.0
12
18
24
9
12
15
18
21
24
27
Attention Window Size
Attention Window Size
Figure 7: Ablation study on short window size and frame sink. Smaller windows reduce consistency, while enabling frame sink mitigates the drop.
4.4 KV RECACHE
In Table 4, we ablate KV caching strategies at prompt switches in a 10-second video setting with single switch the 5-second. We compare No KV cache: clear the entire cache at the switch; (ii) KV-cache: retain the full cache unchanged; and (iii) KV-recache (ours): refresh the cache by recomputing key-value states conditioned on the preceding frames and the new prompt. We assess visual consistency with VBench Background Consistency and Subject Consistency, and measure semantic score with the CLIP model. Clearing the cache breaks long-range consistency, causing abrupt visual changes. Retaining the cache preserves continuity but induces prompt inertia: the model sticks to the previous prompt, yielding a lower semantic score on the switched prompt. Our KV recache maintains continuity while restoring compliance to the switched prompt. Please Figure 3, Appendix Figure E, and the demo page for more qualitative comparisons on KV recache.
4.5 SHORT-WINDOW ATTENTION AND FRAME SINK
In Figure 7, we ablate short- Mhottw attention and the frame-sink under a 10-second generation set- ting. We vary the local-attention window from 3 to 27 latent frames, and additionally evaluate con- figuration with 9 local latent frames plus 3 sink latent frames (effective window size 12). Long-range consistency measured using VBeech-Long (Huang et al., 2024b) (Background Consistency and Subject Consistency). Consistency improves the attention window grows and saturates around f4-frame window, revealing a clear quality-efficienyy trade-off: larger windows retain more tempo- ral context but increase latency and memory, while smaller windows are cheaper but less consistent. Our frame-sink mechanism mitigates this trade-off by recovering long-range context without attend- ing to the full history: the 9-local + 3-sink setting achieves consistency close to a 21 1-frrmme window while preserving the speed and memory footprint of a short window.
CONCLUSION
In this work, we introduce LONGLIVE, frame-level AR framework for real-time and interac- tive long video generation. To maintain visual smoothness and semantic adherence during prompt switches in interactive settings, we propose KV-recache technique. We present streaming long tuning strategy that enables direct training on long videos, ensuring high-quality outputs. We further introduce short window attention and frame sink to accelerate long video generation while preserv- ing visual consistency. Experimental results demonstrate that LONGLIVE can efficiently fine-tune a model for long-video AR generation in only 32 GPU-days. Moreover, tuning on long videos is essential not only for long video generation but also prerequisite for efficient inference (e.g., window attention with frame attention sink), substantially improving inference speed. During infer- ence, it achieves 20.7 7 FPS inference video generation while maintaining high fidelity and temporal coherence. Using INT8 quantization, LONGLIVE compresses from 2.7 GB to 1.4 GB, with minimal performance degradation. LONGLIVE also supports INT8-quantized inference, incurring only marginal quality loss. We provide further results, analyses, implementation details, and qualitative showcases in the Appendix.
single NVIDIA H100 GPU, and supports up to 240-second

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APPENDIX
A ETHICS STATEMENT
This study self-supervised, efficient fine-tuning procedure and does not introduce any addi- tional external video datasets for training. All text prompts leveraged in self-supervised training, generated from Qwen2-72B-Instruee (Yang et al., 2024a), are clean, safe, and for academic research purposes only.
B REPRODUCIBILITY STATEMENT
To facilitate reproducibility, we will sourceource this project, including both training and inference code well model weights. In addition, we provide the full training procedure and implementa- tion details in Section 4 and Section F.
C USE OF LARGE LANGUAGE MODELS
During manuscript preparation, we used large language models-GPT: (OpenAI, for language polishing of paragraphs and sentences (grammar, flow, and tonc). These tools were not used to generate ideas, design experiments, or determine conclusions. All technical content, methodology, and interpretations were written, verified, and approved by the authors. To reduce risks of factual drift or citation errors, we required human review of every model-edited sentence and cross-checked all references against primary sources. The authors take full responsibility for the accuracy and integrity of the manuscript.
D GENERAL RELATED WORK
D.1 DIFFUSION-BASER LONG VIDEO GENERATION
Recent advances in diffusion models (Villegas al., 2023; He et al., 2022; Chen et al., 2024b; Wang et al., 2025; Guo et al., 2025; Dalal et al., 2025) have explored long video generation. Phenaki (Vil- legas et al., 2023) compresses video into discrete tokens, enabling variable-length generation from open-domain text. NUWA-XL (Yin et al., 2023) extends diffusion to extremely long sequences coarse-to-fine "diffusion over diffusion" framework, generating global keyframes and fill- ing intermediate frames in parallel. LVDM (He et al., 2022) leverages compact 3D latent space with hierarchical generation. LaVie (Wang al., 2025) proposes a cascaded pipeline, with joint fine-tuning, rotary position encoding, and temporal attention. SEINE (Chen et al., 2024b) em- ploys smooth shot transitions using a stochastic masking- based diffusion model. LCT (Guo et al., 2025) expanded pre-trained short-video models scene-level contexts for multi-shot coherence, via large-scale fine-tuning. Other approaches (Dalal et al., 2025) use a test-time training technique generate minute-long videos. TECO (Yan et al., 2023), MALT Diffusion (Yu al., 2025), and Rolling Diffusion Ruhe et al. (2024) all study long-horizon sequence generation or prediction by designing architectures and training schemes that better preserve temporal coherence over many frames (e.g., transformer-basee predictive models in TECO on navigation-style video benchmarks, memory-angmented latent transformers in MALT on UCF-101 / Kinetics-600, and sliding-window diffusion with per-frame time reparameterization in Rolling Diffusion for Kinetics-600 video pre- diction and other temporal data). However, they are primarily evaluated on relatively small-scale video prediction or dlass-conditionell generation datasets such as UCF or Kinetics, are not designed as large-scale text-to-video systems, and do not target real-time streaming generation, which makes them conceptually related but orthogonal to the real-time, text-driven long-video setting addressed by LongLive. Although these models can generate long-duration videos, they often incur heavy computational costs, motivating more efficient and real-time solutions.
Several recent works extend the generation length of diffusion models in a training- free manner. RI- FLEx (Zhao et al., 2025) conducts video length extrapolation by adjusting the intrinsic frequency of position embeddings, mitigating temporal repetition and motion slowdown. FreeNoise (Qiu et al., 2024) uses noise rescheduling strategy and window-hased temporal attention. FreeLong (Lu al.,
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2024) blends temporal frequency components at inference. FreeLong++ (Lu & Yang, 2025) intro- duces multi-band spectral fusion to capture and fuse multi multi-frequency temporal information. In these training-free settings. models achieve at most 4-8x extension in length (up to 40 seconds), which remains inadequate for long-form scenarios.
D.2 AUTOREGRESSIVE LONG VIDEO GENERATION
A growing number of works (Chen et al., 2024a; Song al., 2025; Mao et al., 2025; Yuan al,, 2025; Zhang & Agrawala, 2025; Gao et al., 2025; Henschel et al., 2025; Gao et al., 2025) integrate diffusion modeling with AR prediction, an intermediate paradigm between purely diffusion-based approaches and purely AR approaches. Diffusion-forciin paradigm by injecting noise into future tokens and training the model to denoise them, combining diffusion quality with AR efficiency. Streaming T2V (Henschel et al., 2025) extends this idea with short and long long-term memory modules for coherent text-to-video generation. Pyramidal-ftow (Jin et al., 2025) proposes multi-scale flow matching design to reduce computation. History-guidee video diffusion (Song et al., 2025) further incorporates flexible-length historical context to improve temporal consistency over extended rollouts. SkyReels-V2 (Chen et al., 2025a) couples diffusion forcing with film-structure planner and multimodal controls. FramePack (Zhang & Agrawala, 2025) compresses input frames into fixed-size context to address memory and efficiency bot- tlenecks. Lumos-1 1 (Yuan et al., 2025) employs large language models (LLMs) style architec- tures, integrating spatiotemporal modeling under the diffusion-forcing framework. Most recently, LongVie (Gao et al., 2025) introduces multimodal-guide degradation-awant training. Recent efforts (Yin et al., 2025; Huang et al., 2025; Gu et al., 2025; Teng et al., 2025; Zhou et al., 2025; Deng et al., 2025) have advanced causal AR-based models for long video generation. Vid (Yin et al., 2025) reformulates bidirectional video diffusion into a causal AR process, using distribution matching distillation compress multi-step demoising into few steps. FAR (Gu et al., 2025) further enhances AR generation by combining a high-resolution short-term context with a compressed long-term context via flexible positional encoding. MAGI- (Teng et al., 2025) scales AR video generation to large models and datasets through chunk-wise prediction. Most recently, Self-forcing (Huang et al., 2025) addresses the train-test gap in AR video diffusion by simulating inference conditions during training, rolling out generation with KV cache, and conditioning on model outputs. Despite the promise of purely AR for long text-to-video gener- ation, achieving real-time efficiency and maintaining high quality simultancously remains an open challenge.
(Chen et al., 2024a) formalizes this hybrid
control, unified noise initialization, and
Recent works have begun exploring interactive video generation, where users can directly influence generation in real time through text or keyboard prompts. The Matrix (Feng et al., 2024) demon- strates infinite-horizon world generation with first- and third-person control, using shifted window denoising process. Yume (Mao et al., 2025) builds an interactive world generation pipeline capable of constructing explorable environments from single image, video, or text, allowing responsive user navigation. Matrix -ame (Zhang et al., 2025) employs large-scale pretraining and action- labeled finetuning produce controllable, high-fidelity video conditioned on reference frames, mo- tion context, and user actions. While effective, these methods are specifically tailored for interactive video generation in video game environments, such Minecraft and GTA. MAGI-1 (Teng al., 2025) supports general interaction, but its prompt switching requires manual adjustment of KV- cache windows at different steps, which complicates practical use.
E TRAINING PROMPT GENERATION
LONGLIVE does not require video data since we adopt a self-training method, It relies only on a set of prompts to teach the model with interaction ability ( (Yang et al., 2024b; 2021; 2023). To efficiently produce appropriate, reasonable, and safe interactive prompts, we employ the Qwen2- InB-Instruct (Yang et al., 2024a) LLM. Given a source prompt from didP (Wang & Yang, 2024), we instruct Qwen2-72B-Inseee to synthesize the next scene under several constraints. The instruction template is shown below.
You are a video-prompt generatien specialist. Your task
Receive an ORIGINAIL_PRMME for the first part of a conti shot. write one stand-alone Kng1 ish paragraph (80 words) that shows the
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Algorithm 1 Streaming Long Tuning
Algorithm 2 Interactive Inference
Require: Causal video generator Ga, Prompt set P
Require: Causal vidco generator Go
Require: Video length Lvido Per clip length lelip
Require: Prompt sequence P = [po)
1: while not converged do
switch-index sequence S = [$1, -.......
2: Initialize KV cache C t-
Require: Number of video frames N, diffusion
3: Initialize current video length 1 <- 0
steps per frame
1: Initialize model output x +
4: Sample (p, Pnext) ~ P
2: Initialize KV cache C + 0
5: Sample switch index
3: Pastive + P.ppop .V do
E (1,2..... Lviieo//ctpp 1} <-= lelip
4: for ==1,...,,N
6:
5: i then
7:
if 2 Ividen then
6:
7: Puetive + p.ppp
8:
C+ 1
C+-rcccce(GG, x, C, phenns)
9:
Resample (p. Pnexi) and
8:
end if
end if
9:
Initialize rir ~ 1),
Pnetive + P:
i
10: for j = T T..... , do
11:
11:
Set 46 + Go (aii, C, Pacive)
Pnext:
otherwise
12:
if 1
12:
if = then
13:
x. x.ppped (
C recache(Ga, V, C2 Pactive)
14:
C (u)) 0, C, Paciie)
14:
end if
15:
else
generate.next.cIipCC C, Pactive)
16:
Sample e ~ N(0,
16:
C + DMD_Loss(CC) x, Pactive)
17:
Set wi, c
C.bacwward)
18:
end if
18:
update generator parameter 0
19:
end for
1 leisp
20:
end for
20:
end while
return
next moment of the same shot.
* exactly one new action/ebject for the existing main subject.
Keep setting, subject, mood, style, camera scale, and canera movement
or angle exactly as in the ORIGINAL_PROMPT.
Elements may vanish only if naturally obscured by the new action.
Do *ot use phrases like *still, as before, continues> that reveal you read the prior text.
Use clear mid-level English; avoid
literary words.
End the paragraph with **the same camera keywords that appear at the separated by single spaces, no brackets. +uttput format MUST be exactly ene line, wrapped between <CUTPUT> and </OUTPUT>.**
end of the ORIGINAL PROMPT**, PRICINAL_PROMPT...
**NO+* add explanations, greetings, headings, numbering, markdowm,
or extra lines.
Anything written outside the twe tags will be ignored.
F TRAINING DETAILS
IMPLEMENTATION
We first adapt the pretrained Wan2. 1-122-12.33 into a chunk- autoregressive (AR) causal- attention model. First, we conduct an ODE initialization as the same as the model with DMD, but switch to short-windon attention with frame-sink tokens: the chunk size is 3 latent frames, the local attention window is 9 frames, and the first chunk (3 latent frames) serves as the sink. After this initialization, we perform streaming long-tuning strictly following Algorithm I: at each iteration, we roll out a 5 clip and supervise the student using Wan2. 1--2V-14BB as the teacher. Optimization uses AdamW for both actor and critic with learning rates Ir = 1.0 x 10-5 0.0,
Then we train
(actor) and Iraitiic 2.0 x 10 32 = 0.999 for the critic. Training is conducted on 64 GPUs with one sample per GPU (global batch size 64). We apply EMA to the actor with decay 0.99, starting step 200. The maximum
set
0.0. B₂ 0.999 for the actor and 31 31,eritic
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Table A: LoRA budget vs, performance on VBench-Long. moderate budget approaches full- model quality with far fewer trainable parameters.
LoRA rank
32
64
128
256
512
Full Model
Trainable Parameters
44 M
87 M
175 M
350 M
700 M
1.3 B
Total Score
81.08
82.68
82.98
83.12
83.04
83.52
Table B: INT8 NWT8-Quantized results on VBench. FPS is measured on single NVIDIA 5090 GPU.
Precision
Model Size
Throughput (FPS)
Total
Quality
Semantic
INT8
1.4 GB
16.4
84.31 84.87
86.20 86.97
76.74
BF16
2.7 GB
12.6
76.47
sequence length is set the target inference horizon; both 60 and 240 work well in practice. For the 60s setting, we train for 3,000 iterations.
F.2 LORA TUNING
Motivated by LongLora (Chen et al., 2024c), we assume that improving the quality of long context does not require full model fine-tuning. We therefore adopt LoRA tuning throughout the streaming long tuning procedure. Interestingly, we find that effective long-range generation demands relatively high adapter ranks; in our setup. the resulting adapters require 256 ranks, making roughly 27% of the model's parameters trainable. Even so, LoRA substantially reduces the training footprint, cutting the parameter/optimizzer state to about 27% of that required by full fine-tuning (i.e., 73% savings).
We ablate LoRA tuning in Table A. We measure the 30s long middo quality by VBench-long. Scaling LoRA budget improves quality until saturation point, with the rank 256 configuration achieving the best while still training far fewer parameters than full fine-tuning.
G ABLATION STUDY ON THE NUMBER OF SINK FRAMES
We have compared no sink versus different numbers of initial sink frames. As shown in the Table and Figure A below, using small number of sink frames (S3W9) slightly improves VBench scores, while increasing the number of sink frames (S6W6, S9W3) makes the sink frame visually dominant and causes mashback-like, less coherent long-range dynamics. We will report the VBench numbers as table and provide qualitative comparisons in the PDF appendix.
We have expanded the ablation the frame-sink configuration, where we use the notation SxWy denote using z sink frames followed by a sliding window of y frames (e.g., S3W9 means sink frames + 9-frame window). As shown in Table C, introducing small number of sink frames (S3W9) slightly improves VBench scores over the no-sink baseline (SOW12). However, when we further increase the number of sink frames (S6W6, S9W3), the sink frames become visually dom- inant and the VBench scores degrade, with the S9W3 setting exhibiting a clear collapse in both overall and consistency scores. The qualitative comparisons in Figure corroborate this trend: for S0W12, temporal consistency is poor and the red boxes highlight noticeable late-stage drifting; S3W9 yields more stable and coherent dynamics; while S6W6 and S9W3 introduce sudden content changes and flickering frames, with the red boxes marking these abrupt transitions and artifacts.
H QUANTIZATION
We quantize LONGLIVE INT8 via post-training quantization (Li* et al., 2025). As shown in Table B, this reduces LONGLIVE's model size by 1.9x and improves throughput by 1.3 1.3x, with minimal degradation VBench (Table B).
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Table C: Ablation on the frame-sink configuration in terms of VBench. We use the notation S to denote sink frames followed by y-frame window. A small number of sink frames (S3W9) slightly improves the VBench score over the no-sink baseline (SOW12), while increasing the number of sink frames (S6W6, S9W3) degrades performance, with S9W3 exhibiting a clear collapse in the consistency score.
Metric
S0W12
S3W9
S6W6
S9W3
VBench Score f
83.78
83,98
79.23
74.43
Consistency Score
93.30
94.10
94.22
54.65
S0W12
S3W9
S6W6
S9W3
Figure A: Qualitative comparison of different frame-sink configurations using the SD notation (S: number sink frames; W: number window frames). For S0W12, temporal consistency is poor and the red boxes highlight noticeable late-stage drifting; S3W9 yields more stable, coherent dynamics; while S6W6 and S9W3 produce visually dominant sink frames with sudden content changes and flickering artifacts, indicated by the red boxes.
INTERACTIVE LONG VIDEO SHOWCASES
We present interactive 60s videos generated with six sequential prompts in Figure and Figure C. See our demo page in the supplementary materials for more examples.
LONG VIDEO SHOWCASES
We present pongle-promph 60 videos in Figure D. See our demo page the supplementary materials for more examples.
K KV RE-CACHING COMPARISON
We present qualitative results from the ablation study of KV re-caching in Figure E. See demo page in the supplementary materials for more examples. No KV cache: New-prompt adherence but abrupt transitions and visual discontinuity. KV cache: Smooth visuals but new-prompt non- adherence (delayed or ignored). KV recache: Visual consistency and new-prompt adherence.
L ULTRA-LONG VIDEO ABLITIES
LONgLivE train and test ultra-long sequences. We conduct experiment 240-second sequence, and it generates this ultra-long video smoothly and consistently. See our demo page in the supplementary materials for ultra-long examples.
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0s-10s: Medium shot, static: Apostle Paul walks dusty Roman-ers path, tunic cloak, staff hand; determined, wise. Rolling hills and farmland in the background.
10s-2Os: Same setup: he passes into patch of low shrubs along the path, pace steady.
20s-30s: threadbare gray-clad boy peeks from bush: Paul glances over and continues.
30s-40s: Paul stops the boy steps out, approaching hesitantly. Paul offers warm smile and nod.
40s-50s: The boy touches the hem Paul's robe; Paul rests gentle hand on his shoulder.
50s-60s: They walk side by side: a few crows sweep across the sky above the quiet countryside."
Figure B: Interactive 60s videos with sequential prompts. See our demo page the supplementary materials for more examples.
M USER STUDY DETAILS
We conducted a user study to evaluate video quality across 48 questions spanning four dimensions: Overall (overall preference considering all factors), Motion Quality (smoothness/naturannes of motion; absence of jitter or discontinuity). Instruction Following (faithfulness to the given instruc- tion/prompt), and Visual Quality (clarity, level of detail, and overall aesthetic quality). For each question, participants were shown pair of videos together with the corresponding prompt and asked choose Model A, Model B, or Same (no perceptible difference). The survey was distributed to 30 participants; we received 26 valid responses, yielding 1,248 total judgments (26 x 48). Participants were instructed to watch both vidcos carefully and replay if needed before making a choice.
N LIMITATION ANALYSIS
LONGLIvE is efficient fine-tuning scheme built top of a pretrained base model, so its ultimate performance is bounded by the capacity and quality of that base model. particular, we adopt a self-supervised fine-tuning strategy without additional curated real-video data. While this improves
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efficiency and scalability, it also limits the method's ability to correct systematic errors or biases inherited from the base model. Consequently, the quality of any short segment (e.g.. per 10-s clip) is unlikely to consistently exceed that of the base model, even if long-hoon long-horizon consistency instruction adherence improves. Therefore, our gains are primarily in adaptation and stabilization rather than absolute ceiling quality. Future work could incorporate supervised data to avoid the quality bound.
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Os-10s Casino Texas Hold' late-30s with short dark hair and light stubble in navy blazer/charcoal grips his hole cards, jaw tight. Chips crowd the felt, dealer deals, slot machines glow. Wide - medium close-up on his strained focus.
10s- Same setup: he flicks his cards to the felt and leans back, arms spread in triumph. Camera locks the celebration.
20s-30s: 30s: He flips the winning hand; nearby patron claps applause rises. Camera centers his reaction.
30s-40s: He sits upright and methodically stacks his chips, neat, deliberate movements.
40s-50s: surveys the stacks and breaks proud, self assured smile.
50s-60s: He high-fives patron: laughter and cheers ripple around the table. Same framing.
Figure C: Interactive 60s videos with sequential prompts. See our demo page in the supplementary materials for more examples.
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Published as a conference paper at ICLR 2026
forest during heavy rainstone, capturing girl fronticaalyy running through foliage. Her wild hair and desperation. Behind verious simmlls-rbbbits, girl's clothes sooked, clinging bacly, and is screaming shouting tries to escape. The background a blur of greenery and rain-drenched trees, with accosional glimpses of the darkening sky, A wide angle shot from e low angle, emphosizing the urgency and choos of the moment.
and chaotic
behind sprints, orms flailing
birds running. creating
stmosphere.
playful, cnimated panda dancing joyfully under blanket of snow, The pands moves gracefully, parforming lively dance stops that include twirling and hopping. The scere set a serone winter landscape with gently falling snowflakes and tall pine trees the background. The panda has soft, fluffy fur and expressive, joyful eyes. The comero captures the pande from medium clese-up angle, focusing on the pande's energetic mevements and the peaceful snowy environment surrounding it
Greate Batman and Joker fight using the grophics Grand Theft Auto In the video. Batman dressed his iconic black gritty urban. sports signature green purple They are engaged in fierce mations es they dodge, punch, and in background. Both chanacters displey expressions
utility belt, environment, buildings each other. The scene captured from dynomic third- person perspective, with occasiondl close-ups emphasizing their intense expressions and body longunge
Figure D: Single-prompt 60ss videos. See our demo page in the supplementary materials for more examples.
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No KV cache
K cache
recache
0s-5s: a steaming burger-seared patty (crisp edges, pink center), melted cheddar, lettuce, tomato, pickles, special sauce-on lightly charred sesame bun.
5s-10s: fresh pepper sprinkles onto a hot patty under melted cheddar with lettuce, tomato, pickles, special sauce charred sesome bun.
No KV cache
KV cache
KV recache
0s-5s: Young and beautiful girls singing...
5s-10s: One girl reaches up adjust hair...
We present qualitative results from ablation study of KV re-caching. See our demo page in the supplementary materials for more examples. No KV cache: New-prompt adherence but abrupt transitions and visual discontinuity. KV cache: Smooth visuals but new-promp: non- adherence (delayed ignored). KV recache: Visual consistency and new-prompt adherence.
Figure E:
23


Reviews:
[{"review_id": "OCYrrMe5N6", "reviewer": "Reviewer_tdeq", "summary": "This paper presents a frame-level AR framework for real-time interactive long video generation. The paper mainly focuses on efficiency design. The authors introduce a KV-recache mechanism that refreshes cached states to ensure smooth, adherent transitions when prompts change. They also propose a tuning strategy to address quality degradation in long videos, aligning training with inference. For efficiency, the model combines short window attention with a frame sink to maintain long-range consistency while accelerating generation. The resulting 1.3B parameter model achieves 20.7 FPS on an H100 GPU, supports videos up to 240 seconds, and demonstrates strong performance on VBench for both short and long video benchmarks.", "questions": "Please see weaknesses.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. A new KV-recache strategy is proposed for frame-level AR with multiple prompts.\n2. Fairly comprehensive evluation, demonstrating the performance on different lengths of video.\n3. Some efficient strategies for training and inference are proposed for long videos.", "weaknesses": "1. Generalization ability. Though the authors show some long video cases, the prompt still remains in the same scene. How does it handle multiple, rapid-fire prompts? More importantly, how does it respond to prompts that introduce abrupt semantic shifts (e.g., changing from \"Iron Man fighting\" to \"a peaceful beach\") versus the logical continuations shown in the examples?\n2. The \"train-long-test-long\" strategy is a key strength, but its supervision (DMD loss) relies on a teacher model that itself was only trained on short clips. While the student model is exposed to long rollouts, the supervision for each new clip is still provided by this short-clip teacher. This may create a ceiling for true long-range coherence, as the teacher lacks the long-horizon context the student is trying to learn.\n3. The ablation on time consuming about the KV cache, no KV Cache, and KV recache for different lengths of video is suggested to add to prove the effiency.\n4. The ablation about frame sink strategy (first frame or others) is encouraged to add.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1cFaoFzreB", "reviewer": "Reviewer_AEHT", "summary": "This paper presents LongLive, a method for fine-tuning pretrained video diffusion models for long video generation. First, it introduces KV-Recache, which regenerates the KV cache using the previously generated frames together with the new prompt when a prompt change occurs. This allows the model to generate videos that remain visually coherent with past frames while adapting better to the new prompt, rather than being stuck with the previous inputs. Moreover, the authors progressively extend the generation length by fine-tuning DMDs with an increasing number of video clips, where the gradient is computed only over the last video segment. The paper also proposes several additional techniques, such as applying an attention sink by maintaining the first frame as a global anchor. Leveraging all these techniques, LongLive demonstrates real-time long video generation (up to 240 seconds) on a single H100 GPU with minimal quality degradation.", "questions": "- What is the empirical maximum video length that the proposed model can generate without suffering from severe quality degradation? For instance, can a model generate videos even longer than 240s?\n-Why does the author choose DMD instead of other techniques, e.g., DMD2?  Is there a specific reason?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is generally well-written and easy to follow. \n- Real-time long video generation using pretrained diffusion models is a very challenging but important problem, and I think this paper provides quite effective techniques to solve this.\n- For me, several techniques are simple (which is good) yet novel and effective, such as KV recache.\n- I really like the supplementary material that the authors attached.", "weaknesses": "- There are several missing works in the literature on long video generation, such as AAPT [1], TECO [2], MALT [3], and Rolling Diffusion [4]. I recommend that the authors include a discussion of these and other relevant works that have not been covered in the paper.\n- I also believe that having a long context is another important aspect in developing long video generation models. While being efficient, the proposed model inherently has a relatively short context length (mainly due to the attention sink that focuses on the first frame), which limits its effective context window. The method is indeed effective at generating visually plausible long videos in real time; however, it might struggle in scenarios that require longer temporal dependencies — for example, when a person disappears and reappears in the same scene, or when generating long videos with multiple scene transitions.\n\n[1] Lin et al., Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generation, 2025.  \n[2] Yan et al., Temporally Consistent Transformers for Video Generation, ICML 2023.  \n[3] Yu et al., MALT Diffusion: Memory-Augmented Latent Transformers for Any-Length Video Generation, 2025.  \n[4] Ruhe et al., Rolling Diffusion Models, ICML 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Sphrt0HFgY", "reviewer": "Reviewer_1kFs", "summary": "This paper investigates real-time, interactive minute-level video generation and frames it as a causal, frame-level autoregressive task. It introduces three synergistic ideas—KV-recache for smooth prompt switching, streaming long tuning to close the train-short/test-long gap, and short-window attention plus frame-sink tokens for accelerating inference. Extensive experiments on VBench and a new 160 interactive suite show that a 1.3 B model fine-tuned in only 32 GPU-days reaches 20.7 FPS on one H100 and outperforms diffusion and AR baselines in both short and  long clip settings. The manuscript is clearly structured and well illustrated.", "questions": "See weakness", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "- This paper tackles an important practical problem—interactive long video generation—and provides the first system that delivers >20 FPS at high resolution while keeping visual consistency. \n\n- The authors propose KV-recache, a simple yet effective cache refresh that unsticks old-prompt semantics without breaking temporal continuity, nicely verified by ablation.\n\n- Experiments are thorough: short vs. long, single vs. multi-prompt, throughput vs. quality, and ablations.\n\n- The writing is accessible, with intuitive figures that contrast the proposed strategies and a reproducibility statement promising full code and weights.", "weaknesses": "Overall, the paper offers solid technical novelty and the core ideas are interesting; nevertheless, I have a few minor concerns and questions listed below.\n\n- Why does SkyReels-V2 show a sudden performance jump in the 20–30 s interval? This spike suggests that the evaluation may be unstable. If so, I recommend re-running the evaluation with several random seeds (e.g., three) and reporting the mean and standard deviation to ensure reliability.\n\n- The authors fine-tune on top of Wan2.1, yet the resulting model’s semantic-alignment score drops markedly, as shown in Table 1; no explanation for this degradation is provided. I encourage the authors to investigate and clarify the cause—either through analysis or additional ablation experiments—so that the community can better understand the trade-offs and further improve the model.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)