Flow Caching for Autoregressive Video Generation
Authors: Yuexiao Ma
Xuzhe Zheng
Jing Xu
Xiwei Xu
Feng Ling
Xiawu Zheng
Huafeng Kuang
Huixia Li
XING WANG
Xuefeng Xiao
Fei Chao
Rongrong Ji
Venue: ICLR 2026 Poster (2026)
Keywords: Autoregressive video generation
chunkwise caching
KV cache compression
ultra-long video synthesis
video acceleration
Primary area: generative models

Abstract: Autoregressive models, often built on Transformer architectures, represent a powerful paradigm for generating ultra-long videos by synthesizing content in sequential chunks. However, this sequential generation process is notoriously slow. While caching strategies have proven effective for accelerating traditional video diffusion models, existing methods assume uniform denoising across all frames—an assumption that breaks down in autoregressive models where different video chunks exhibit varying similarity patterns at identical timesteps.
In this paper, we present \textbf{FlowCache}, the first caching framework specifically designed for autoregressive video generation. Our key insight is that each video chunk should maintain independent caching policies, allowing fine-grained control over which chunks require recomputation at each timestep. We introduce a chunkwise caching strategy that dynamically adapts to the unique denoising characteristics of each chunk, complemented by a joint importance–redundancy optimized KV cache compression mechanism that maintains fixed memory bounds while preserving generation quality.
Our method achieves remarkable speedups of $\textbf{2.38}\times$ on MAGI-1 and $\textbf{6.7}\times$ on SkyReels-V2, with negligible quality degradation (VBench: $0.87\uparrow$ and $0.79\downarrow$ respectively). These results demonstrate that FlowCache, successfully unlocks the potential of autoregressive models for real-time, ultra-long video generation—establishing a new benchmark for efficient video synthesis at scale. The code is available at https://github.com/mikeallen39/FlowCache.

Published as a conference paper at ICLR 2026
FLOW CACHING FOR AUTOREGRESSIVE VIDEO GEN- ERATION
Yuexiao Ma,,, Xuzhe Zhengl,, Jing Xu Xiwei Xu' Feng Ling', Huafeng Kuang,, Huixia Li,, Xing Wang". Xuefeng Xiao². Fei Chao', Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China.
Xiawu Zheng', Rongrong Ji
BByteDance
ABSTRACT
Autoregressive models, often built Transformer architectures, represent powerful paradigm for generating ultra-long videos by synthesizing content in sequential chunks. However, this sequential generation process notori- ously slow. While caching strategies have proven effective for accelerating traditional video diffusion models, existing methods assume uniform denois- ing across all frames-an assumption that breaks down in autoregressive mod- els where different video chunks exhibit varying similarity patterns at identi- cal timesteps. In this paper, we present FlowCache, the first caching frame- work specifically designed for autoregressive video generation. Our key in- sight is that each video chunk should maintain independent caching policies, allowing fine-ga ena-gga control over which chunks require recomputation each timestep. We introduce chunkwise caching strategy that dynamically adapts to the unique denoising characteristics of each chunk, complemented by a joint redundancy
imporaance-rduuddancyy optimized KV cache compression mechanism that main- tains fixed memory bounds while preserving generation quality. Our method achieves remarkable speedups of 2.38x MAGI-1 and 6.7x with negligible quality degradation (VBench: 0.87 1 and 0.79 respectively). These results demonstrate that FlowCache, successfully unlocks the potential of a
SkyReels-V2,
autoregressive models for real-time, ultra-long video new benchmark for efficient video synthesis scale. The code available
Prompt: an airplane landing smoothly on runway
Prompt: A car drives along coastal road suaset
Yanilla
Speedup:
Speedup:
TeaCuche
Speedup:
VBench:
Speedup:
VBench:
FlowCache
Speedup: 2.38%
Vieenc:: 77.93
Speedup: 6.7x
Veench: 83.05
MAGI-1
SkaReeb--2
Figure 1: Visual comparison of video generation quality among the vanilla model, TeaCache, and our FlowCache.
Corresponding author: rrji @ xmx.eedu.cn, f: Equal contribution, $: This work was done when Yuexiao Ma intern ByteDance.

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Published as a conference paper at ICLR 2026
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Figure 2: Adjacent-timestep relative LI distance across denoising trajectories for autoregressive video diffusion models. Denoising progress (%) and relative LI distance are shown on horizontal and vertical axes, respectively, with colors denoting distinct video chunks, Results for (a) MAGI-1 and (b) SkyReels-V2 V2 reveal three patterns: (i) monotonic increase relative L.1 distance during denoising, confirming Theorem [ (ii) substantial metrics divergence among chunks at identical timesteps reflects heterogeneous denoising stages, yielding variable reuse probabilities; (iii) persis- tently high similarity between model inputs and sampler outputs across all chunks.
INTRODUCTION
While large language models have demonstrated remarkable reasoning and task execution capabil-
ities (Zhang et al., 2024a 2025b, Zhou et al., 2025 Wu et al. 2024; Ji ct al.
2024; Wang et al.
2025
Zhang et al. 2025a Yan et al.) 2025a b Ma et al. 2025
Li et al. 2025e
Lin et al. 2026
Video generation models (Lin et al. 2024a Zheng et al. 2024 Kong et al. 2024 Peng et al. 2025
Wan et al. 2025 Brooks al. [024 Gao et al. 2025) have demonstrated the ability to capture the
objective physical laws governing the real world and are increasingly applied across a wide range of domains, including autonomous driving (Fu al. 2024 Ma et al. 2024a). social media
tent creation (Brooks et al. 2024), special effects generation (Gao et al. 2025). and personalized product demonstrations (Wan
2025). supported by theoretical advancements in generation dy- namics (Sun et al., 2025 Li et al. 2025al Zhong et al. (025b) Wu et al. (2025). Recent works have also explored digital human synthesis
et al. (025b) and efficient scaling strategies for high-
quality video generation (Cheng et al.] 2025: Cui et al. 2025 2026 Yuan et al., 2025 Lu et al.)
2025). Among these models, the diffusion transformer architecture (DiT))Peebles Xie (2023) has
emerged as mainstream approach due to its superior generative quality compared to traditional UNet-based structures (Ronneberger et al. 2013), largely attributed to its strong scalability. How- the generation of long videos imposes prohibitively high computational costs, which signifi- cantly restricts the practical applicability of these models and limits the potential for personalized customization. In particular, the computational complexity of DiT inference scales quadratically with the length of the video token sequence, as a result of the transformer's requirement to compute attention scores between all pairs of tokens. Moreover, this challenge further exacerbated by the multi-timestep denoising process applied across video frames.
To decouple inference computational costs from sequence length, autoregressive video generation models (Teng et al.) 2025 Chen et al. 2025a) based on Causal Diffusion-Forcing (CDF) (Chen et al., 2024a Yin et al. 2025| Song et al. 2025) inference paradigms are garnering increasing attention. These models achieve this by partitioning the entire video sequence into fixed-frame video chunks and performing causal autoregressive denoising on them. With a fixed denoising window size, the computational complexity of long-video inference drops from quadratic to linear scaling, thereby establishing theoretical feasibility for real-time long-video generation. Nevertheless, at high reso- lutions, the computational demands of autoregressive video models for producing extended videos remain considerabl,, creating a substantial gap between current capabilities and the goal of real- time generation.For example, using A800 GPU with batch size 1, generating 10-second video at 720 x 720 resolution with full KV cache reference requires approximately 32GB of memory and 50 minutes of inference time for the MAGI- MAG1-1-4.5B- 4.5B-distil model.
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To address these challenges, class of training-free -free cache-based methods Yuan al. 2024 Liu
iet al. 2025b at Kahatapitiya et al. 2024 Bu et al. 2025 Chen et al., 2024c Zhao et al.
Ma et al.
2024b Selvaraju et (0244) has been developed accelerate the denoising process in Di
models.
These approaches are orthogonal to various compression techniques, such as quantization
Qin et al.
2020
Huang et al. [024a] Qin et al., 2024
2022] Ashkboos et al.] 2024 Frantar et al.
2022
ct al.
2024a0
et al. 2023 2025a
[2244] Lin et al. 2024b] 2025 Shao et al. 2023] Chen ct al.
2025b [2024b] 2 c He al.
2023b al Yang
2025a) and sparsification (Xu al. 2025
et al.
2025| Yang et al. 2025b JAISWAL et al.) Liu et al. [202 2022 Yin et al.] 2023] Huang methods (Wang et a Yin et al. 2024, Meng et al. 2023] Sauer et al. 2024), they facilitate faster DiT inference
ct al.
2025b aj Zhang et al. [023a|] Lu et al., 2024). Unlike distillation-
2023
without incurring additional training overhead. Existing caching strategies can broadly classified into two categories based on variations in feature caching intervals: uniform caching and dynamic caching. Uniform caching (Chen et al., 2024c| Zhao et al. Ma et al., 2024b; Selvaraju et al. [024]
Liu et al., 2025b) exploits the high simi larty of model outputs across jacenttt timesteps by storing
and reusing them at regular intervals. In contrast, dynamic caching (Bu et al. 2025 Kahatapitiya
et al. 2024 Liu at. (225a) employs metrics computed during inference to decide whether features at a specific timestep should be recomputed. Nevertheless, these methods are predominantly tailored to synchronous video diffusion models, where all frames undergo consistent denoising levels the same timestep. When transitioning to autoregressive video generation models, it is challenging to achieve an optimal trade-off between acceleration ratio and video generation quality.
As illustrated in Figure 2 we present similarity metrics between adjacent timesteps for different video chunks in the MAGI-1 and SkyReels-V2 models. Across diverse autoregressive models, we identify three consistent patterns: (a) As the denoising process progresses toward the clean video, the tiee-tp similarity of video chunks progressively deteriorates. (b) Within the same timestep, different video chunks exhibit heterogeneous denoising states, leading to substantial variations in their similarity measures. (c) Throughout the entire denoising trajectory, the inputs and outputs of autoregressive models maintain high similarity across all video chunks. These observations reveal that uniform treatment of all features fails to account for the heterogeneity in denoising states across different video chunks. Specifically, state demonstrate low temporal similarity and necessitate recomputation. In contrast, video chunks in early denoising stages exhibit high similarity to their previous timestep features, making them suitable candidates for cache reuse. This contradiction motivates our proposed Flow method, wherein each video chunk maintains an independent caching strategy to accommodate these varying computational requirements. Through extensive experimentation. we demonstrate that our approach not only achieves superior acceleration ratios but also preserves the quality of generated videos.
any given timestep, video chunks approaching their clean
Furthermore, autoregressive models generate substantial key-value (KV) caches during the genera- tion process. When conceptualizing video chunks as analogous to tokens in language models, each completed denoising iteration of video chunk results in a proportional expansion of the KV cache. The KV cache plays a crucial role in autoregressive video generation models by enabling the mod- eling of physical dynamics in the objective world (Motamed et al.) 2025) and controlling temporal consistency of subjects and backgrounds throughout the video sequence (Teng et al. 2025). While existing KV cache compression techniques (Wan et al., 2024 Li et al. 2024e Zhang et al. 2023b Cai et al. 2025) have predominantly focused on large language models, the unique characteristics of video generation present distinct challenges and opportunities. To address this gap, we present the first comprehensive investigation of KV cache compression's 's impact on the generative capabilities of autoregressive video generation models. Our analysis encompasses two critical dimensions: the selection of salient KV cache entries and the quantification of their relative importance. We conduct this analysis across three hierarchical granularities key video segments, key frames, and key pixel regions- to capture the multi-scale nature of video information. Experimental results demonstrate that our proposed KV cache compression method achieves substantial computational savings while preserving the quality of generated videos, establishing its effectiveness for autoregressive video generation tasks.
In summary, our key contributions are as follows:
We uncover fundamental heterogeneity in the denoising process of autoregressive video generation models, empirically demonstrating that different video chunks exhibit distinct similarity difference identical timesteps. Through rigorous theoretical analysis, we
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tablish that video chunks approaching complete denoising manifest significantly weaker similarity compared to their etrly-stage counterparts, providing crucial insights for opti- mization strategies.
We introduce FlowCache, pioneering method that revolutionizes caching in autoregres- sive video generation by treating each video chunk as an independent computational entity. This chunk-specific caching strategy dynamically adapts the varying denoising states, maximizing both computational efficiency and generation flexibility.
We present the first comprchensive study examining the impact of KV cache compression on autoregressive video generation models. Our multi-dimensional analysis encompasses two critical aspects importance computation granularity and selective retention granular- oyeorrttin across three hierarchical levels: video segments, frames, and pixel regions. This systematic investigation offers valuable insights into the memory-quality tradeoffs in efficient video generation.
Comprehensive empirical evaluation across diverse autoregressive video generation mod- els confirms that our approach achieves superior computational efficiency while preserving generation quality. FlowCache accelerates inference by 2.38x and 6.7x compared to base- line methods on MAGI-1 and SkyReels- V2, respectively, with minimal impact on VBench scores (0.87 1 for MAGI-1, 0.79 J. for SkyReels-V2), These results establish FlowCache as the new state-of-the-ar for efficient autoregressive video generation.
2 RELATED WORK
AutoRegressive Video Generation. Our work builds upon recent advances in autoregressive video generation models (Yin et al. 2025] Teng et al.] 2025 Chen et al. (025a)). These methods adapt the successful paradigms of diffusion models and large language models (LLMs) by modeling long video sequence as an autoregressive prediction of discrete chunks, effectively overcoming the quadratic complexity challenge of long-sequence modeling. Each video chunk serves fun- damental generation unit, analogous to a token in LLMs, with the current chunk conditioned on all prior ones. In this paradigm, the chunk generator is typically diffusion model trained with flow matching (Lipman et al. 2022), enabling the autoregressive framework to integrate advanced diffusion-based generation for efficient production of long, high-fidelity video sequences.
Cache Strategy. Recent work on accelerating diffusion models- essecilly Diffusion Transform- ers (DiTs) has centered on training-free free feature caching that exploits temporal redundancy across denoising timesteps. TeaCache (Liu et al.) (2025)) uses timestep embedding modulated inputs and polynomial fitting to estimate output differences for caching decisions. ToCa (Zou et al., (024) performs token-wise caching by assessing temporal redundancy and error sensitivity via attention- based scores. DiCache (Bu al.) 2025) dynamically aligns multi-step cache trajectories using an online shallow-layer probe to estimate output variation.
mnbeedding-moddlated
KV Cache Compression. Key-Value (KV) cache compression reduces memory and computa- tion costs in large language model (LLM) inference by selectively retaining past key and value states. H2O (Zhang et al. 2023b) preserves "heavy-hitter" tokens with high cumulative attention scores. SnapKV (Li al. 2024c) pre-selects critical tokens using fixed observation window at the prompt's end. D20 (Wan et al. 2024) introduces a two-level discriminative framework that dynamically allocates KV cache budgets across layers and includes a compensation mecha- nism. Additionnl,,,lly, recent studies have investigated eetreval-augmenter strategies and cache merg-
ing enhance long-context comprehension in video understanding (Luo et al.) 2025 2024
Zhang
et al. 2024b).
METHODOL.OGY
3.1 PRELIMINARY
Diffusion Model. Diffusion models (Ronneberger al. 2015 Peebles & Xie 2023) achieve high- quality sample generation by establishing a reversible mapping between data and noise distributions. Their theoretical framework comprises two complementary processes: forward diffusion and reverse
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denoising. The forward diffusion process progressively transforms the original data distribution To into a prior distribution #1 tiy-typilllly a standard Gaussian Gtussian-through -trough systematic noise injection. Within the flow matching framework (Lipman al. 2022), this process manifests a deterministic flow in probability space, where the intermediate state at any time is expressed as:
It = (1-a(t)) (t)) Idata + (t) Inoise.
(1)
Here, att) denotes a monotonic scheduling function that governs the smooth interpolation between data and noise domains. The reverse denoising process constitutes the core mechanism of diffusion models, wherein learned velocity field approximates the inverse mapping from noise to data.
de 1ig (21,,, c), where This process is governed by the following ordinary differential equation: dt
represents optional conditioning information. During inference, the model employs numerical integration schemes to solve this ODE, typically utilizing first-order Euler discretization Karras al. 2022 or more sophisticated sampling algorithms [u et al. 2022) Zhao et al. (2023) )
1 3t; + og( c)44
By sampling from the noise distribution and integrating along the learned velocity field, the model generates new samples from the target distribution. This flow matching paradigm effectively cap- tures the intrinsic geometric structure of data manifolds through direct optimization of the velocity field prediction, thereby enabling high-fidelity synthesis.
RegeRsgees Video Model. Autoregressive video models (Yin et al. 2025 Teng et al. 2025 Chen et al. 2025a) circumvent the quadratic complexity of sequence length by decomposing long videos into discrete chunks, treating each as an analogous unit to language model tokens. This chunking strategy enables efficient generation of extended video sequences through autoregressive denoising. Formally, a video is partitioned into & chunks, where each chunk X₃ E R XaxhXw (i (1, k)}) represents latent representation with Can channels, spatial dimensions h w after downsampling, and temporal length 8. Assuming a maximum window size of video chunks per- mitted for the denoising process, the denoising process for the i-th chunk timestep & follows:
X = X + (o( t, c) At.
(3)
Where E (ii - 1)T/L, I1 1)T/U] represents the timestep. vo(-) denotes the output of autore- gressive diffusion model with parameters 0. denotes the conditional inputs (text, image, video).
Cache Strategy. Various caching strategies (Bu et been developed to accelerate diffusion model inference by storing outputs from selected timesteps for reuse in subsequent iterations. These approaches typically quantify the similarity between con- secutive timesteps using importance metrics, such as the relative L1 distance:
2025 Ma et al. 2024b Liu et al. 2025a) have
|| -xll1
Ll (X,t)
|X
Through the accumulation of these similarity measures, the system determines whether to reuse cached outputs (when the cumulative similarity remains below a predefined threshold) or to perform new model inference (when the threshold is exceeded). However, current caching strategies prove incompatible with autoregressive models due their distinctive inference mechanisms.
3.2 FLOWCACHE
In the context of autoregressive video models, we calculate the relative LI distance defined in Equa- tion between consecutive timestep for i-th video chunk:
||
Xill,
|IUo L, At]
LA (Xrrt(Xtt))) t,
(5)
||
||
The relative L.1 distance metric in Equation possesses inherent mathematical properties that govern the temporal dynamics of the denoising process in autoregressive video models. The theoretical framework of flow matching and the underlying structure of the diffusion process naturally lead to specific behavioral patterns in the velocity field throughout the denoising trajectory. By analyzing the mathematical relationship between the velocity predictions and the state evolution from noise to data, can derive fundamental properties of the relative L1 distance across different time steps. The following theorem establishes a key monotonic property that emerges from the optimal velocity field under standard diffusion model assumptions:
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Theorem Assume the diffussion model to has converged to the optimal velocity field of the flow
matching objective. Further, assume the scheduling function f is of the power-law form =tt)= =
(t
for some constant power p >> O and total time T. Given 0 < t, < t2 S T and data chunk X*
whose initial noise state Xi. T is drawn from (0, T), the following inequality holds:
LAret (x,e(X,e0 (1, 2 [X (2,)).).
(6)
The proof appears in the Appendix B Theorem | establishes that the relative LI distance between model outputs increases as video chunks converge toward real video, reflecting diminished chunk similarity. Figure 2 visualizes this relationship by plotting the relative LI distance between consec- utive timesteps across all video chunks for various autoregressive video models, with the denoising progression on the horizontal axis and the relative LI distance on the vertical axis. Distinct colors denote individual video chunks. Three key patterns emerge from this analysis:
The relative L1 distance monotonically increases as denoising progresses toward real video, demonstrating systematic degradation of chunk similarity at later timesteps- a direct con- sequence of Theorem
Cross-sectional analysis at fixed timesteps reveals pronounced divergence among chunks (represented by different colors), indicating heterogeneous denoising stages and conse- quently disparate similarity metrics.
Model inputs and post-sampler outputs exhibit consistently high similarity across all video chunks throughout the denoising trajectory.
Therefore, based on Theorem D we derive the following corollary:
Corollary Assuming that distinct video chunks i + represent heterogeneous content such that
their state norms differ at any intermediate timestep t E (0, T) (i.e., || 1 + || [|x [[2), and that the
model's 's update magnitude [Vo(XX, t.c) Atll2 is approximately invariant across chunks for fixed t, then their relative LI distances are unequal:
(X,e((Xtt,) + Lirel (X,t,.).
The proof appears the Appendix C The preceding theorem and corollary establish the necessity of maintaining independent cache strategies for individual chunks. We therefore introduce FlowCache, a novel caching methodology tailored for autoregressive video models.
Figure 3 illlustrates that conventional caching approaches employ uniform strategies across all video chunks, failing to accommodate the heterogeneous similarity characteristies arising from disparate denoising stages within the same timestep. This uniformity constrains flexibility, limits accelera- tion potential, and ultimately degrades generation quality. In contrast, FlowCache independently evaluates each chunk's s similarity profile. Specifically, for the i-th video chunk:
O O
if (T -m,T", if E (0, m] and f(X,t 1, L1 (X,t,) e, if E [0, m] and f(X, L, LiGel (X,4 (, S
f(X,t,))
((xx (X,14
(8)
where e denotes the threshold value and m represents the number of initial timesteps excluded from cache reuse (m 5 for MAGI-I, m = 4 for SkyReels-V2). When f(X,t, t, O, forward com- putation is performed for i-th video chunk; otherwise, cached activations are reused. Our empirical analysis demonstrates that excluding early timesteps is critical for preserving generation quality. The threshold values are: MAGI-slow: 0.01, MAGI-fast: 0.015, SkyReelss-2-slone 0.1, SkyReels- V2-fast: 0.15.
Theorem D demonstrates that any chunks approaching full denoising exhibit diminished similar- ity at given timestep, warranting reduced reuse. Conversely, chunks proximate to Gaussian noise maintain higher similarity, enabling reuse across multiple consecutive timesteps. By fully consid- ering the inference paradigm of autoregressive models, Flow Cache maximizes caching flexibility and achieves substantial performance gains. Empirically, method delivers 2.38 acceleration on MAGI-I with 0.87% improvement in VBench scores relative to base model, and 6.7 x speedup on SkyReelsV2 with 0. 0.79% VBench score degradation.

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Slow version: Speedup: 1,89,, VBench: 82.67%
Fast version: Speedup: 2.2x, VBench: 80.06%
Traditional Reuse
Higher Speedup and Generation Quality (Speedup: 6.7x, VBeech: 83.05%)
Video Play back Direction
Flow CowCaclo Cache
X
X
Taken
Selection
Redundancy Score
Timestep
Selected Score
Waiting for chunk
Denobing chunk
Clean KY Cuche
Reserved KY Cuche
Figure 3: Comparison of caching strategies in autoregressive video generation. The top part il- lustrates the Traditional Reuse strategy, which applies uniform caching policy across all video chunks (i.c., all chunks at the same timestep share the same compute/reuse status, except for the newly initialized denoising chunk, which must be computed), In contrast, left) employs chunkwise adaptive caching policy, dynamically deciding for each chunk whether to reuse cached features perform recomputation based its relative L1 distance trajectory. The bottom right panel details FlowCache's KV cache management: it maintains a Reserved K.V Cache that selectively retains historically important and non-redundant tokens.
FlowCache (bottom
3.3 KVCACHE COMPRESSION
Autoregressive video generation models synthesize videos of arbitrary length by sequentially de- noising groups of spatiotemporal chunks-mirroring the autoregressive token generation process in large language models (LLMs). In this paradigm, fully denoised video chunks (i.e., clean video chunks) are stored in a Key-Value (KV) cache, which is attended to by the group of chunks rently undergoing denoising. This mechanism enables the model to condition on historical context and maintain temporal coherence throughout the sequence.
In LLMs, KV cache compression commonly relies on importance scoring: historical tokens are ranked by their attention relevance to recent context, and only the top entries are retained (e.g., H2O (Zhang et al., 20236), D20 (Wan et al., (0244). While effective for text, this strategy performs poorly in video generation under a fixed cache budget. The core issue stems from the high spatial and temporal redundancy inherent in video data: many tokens assigned high importance scores are nearly identical. As a result, importance-only selection retains multiple redundant yet individually salient segments. This inefficient allocation starves the cache of diverse, potentially useful historical content, ultimately degrading temporal consistency.
We argue that effective KV cache compression for video generation must jointly account for im- portance and redundancy principle also adopted in efficient reasoning models (e.g.. R-KV (Cal et al.) 2025)). Our approach selects historical entries that are both relevant to the current denoising tokens and mutually dissimilar, ensuring optimal use of the limited cache budget.
Specifically, we allocate fixed-size KV cache buffer Buaat. partitioned into regions:

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1. A compressed clean-chunk region of size Boudga: storing compressed KV states from all clean chunks.
2. A current denoising region of size B holding the KV states of the group of video chunks currently undergoing denoising.
When Bioat is full, we concatenate the KV cache from the clean chunk region (uncompressed during the first compression step) with the KV states of any chunks in the current region that have just com- pleted denoising. The merged set is then compressed via our selection criterion. After compression, the current denoising region partially freed, creating space for the next undenoised chunk.
Similar to R-KV (Cai et al. 2025), our selection criterion balances importance and redundancy, Let Q E x H. denote the queries from the current denoising tokens, and Kelean E IR Lx xHx xd the keys from clean chunks, where Lg Lx are sequence lengths, Hg, H& are numbers of query and key heads, and d is the head dimension. The importance of each historical token is derived from the attention weights between Q and Kelean- Specifically, we compute attention scores, apply softmax over the key dimension, and average across the query sequence dimension obtain per-head importance distribution over historical tokens:
2 (M) (K
(b) clean J Cj
Imp, (h) 1
Σ
softmax
Ly
vd
where h indexes the key head (with query heads grouped accordingly), and q; (h) responding query and key vectors. The vector e, 1 is one-hot column vector containing a "1" at the j-th position and zeros elsewhere. To enhance robustness, we apply ID max-pooling with kernel size and padding l//2 to Imp("):
1 are the cor-
(M)
(h) Imp
MaxPoolID (Imp()) kernel k, padding k/2)
(10)
For redundancy, we compute per-head cosine similarity matrices over Kelean After l fz-normalizing the keys, we form s
for each head h, explicitly set diagonal entries to zero (s 0), and average across the token dimension and apply softmax:
Red, (N) softmax
(11)
L
This yields per-head redundancy distribution (ed RL4,, where higher values indicate tokens that are, on average, more similar to others in the same head.
Finally, we combine the pooled importance and redundancy into unified per-head selection score:
Score (h) Imp; (h)
(1 A) Red!") !))e
(12)
where À E [0, 1] is a mixing coefficient. For each attention head h, we select the top- tokens with the highest Score()) (h), where B is the compression budget.
By jointly optimizing for relevance and diversity, method preserves long-range temporal consistency while substantially reducing the memory and computational overhead of DiT atten- tion tion-enabling efficient, high-fidelity long-form video generation.
Furthermore, we provide an analysis the efficiency of KV cache compression covering both GPU memory footprint and computational overhead. For details, please see Appendix D.2
4 EXPERIMENTS
4.1
SETTINGS
Base Models To evaluate the efficacy of our proposed method, we selected two representative dif- fusion models based the autoregressive paradigm: MAGI-1-4,5BBdiiss (Teng et al. 2025) and
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Table 1: Quantitative evaluation of inference efficiency and visual quality in autoregressive video generation models.
PFLOPs
Speedup Latency(s)
VBench 1
LPIPS
SSIM
PSNR
Model
Method
+
Vanilla
306
1x
2873
77.06%
TeaCache-slow
294
1.12x
2579
77.50%
0.6211
0.2801
13.26
MAGI-1
TeaCache-fast
225
1.44x
1998
70.11%
0.8160
0.1138
8.94
FlowCache- slow
161
1.86x
1546
78.96%
0.3160
0.6497
22.34
FlowCache fast
140
2.38x
1209
77.93%
0.4311
0.5140
19.27
Vanilla
113
1540
83.84%
TeaCache-slow
58
1.89x
814
82.67%
0.1472
0.7501
21.96
SkyReels-V V2
feaCache-fast
49
2.2x
686
80.06%
0.3063
0.6121
18.39
FlowCache-slow
36
5.88x
262
83.12%
0.1225
0.789
23.74
FlowCCache-fast
28
6.7x
230
83.05%
0.1467
0.7635
22.95
MMyRReels-V-1138-554 (Chen et al., 2025a). Although both models inference autoregressively, their implementations differ significantly. MAGI-1 processes video by dividing it into chunks, ap- plying autoregressive modeling between these chunks. A sliding window mechanism is employed to constrain the number of chunks being denoised simultaneously. In contrast, SkyReels- V2 adopts hierarchical approach: each video chunk is further partitioned into blocks. Autoregression occurs within a chunk, where earlier blocks are denoised for predefined number of steps before subse- quent blocks begin the denoising process. This design results in non-uniform denoising levels across blocks at any given denoising step.
Evaluation Metrics comprehensive evaluation is conducted, focusing
critical aspects:
the perceptual quality of the generated videos and the computational efficiency during inference. For quality assessment, we adopt established metrics including VBench (Huang al., 2024b), LPIPS (Zhang et al. 2018), PSNR, and SSIM, following TeaCache (Liu et al.) (025a). We also acknowledge recent advancements in blind and reference- based image quality assessment which fo- cus on semantic and distortion sensitivity (Li et al., [023] (222bb] 2025t d c). To quantify efficiency, we measure the computational cost in Floating Point Operations (FL.Ps)) and the practical inference latency. For all video generation quality assessments, we utilize the VBench-long benchmark, which we refer to as VBench throughout the paper for brevity.
Implementation Detail All experiments are implemented using PyTorch and executed NVIDIA A800 80GB GPUs For MAGI-1, each chunk consists of 24 frames, denoised for steps per chunk, with total of 10 chunks generated. For SkyReels- V2, the inference configuration uses chunks of 97 frames with an overlap of 17 frames between consecutive chunks. Each block within a chunk is denoised for 50 steps, and total of two chunks generated. Additionally, the execution of KV cache compression and the measurement of speedup ratios are explained in the Appendix D.1
4.2 RESULTS
Quantitative results in Table demonssratee the clear superiority of FlowCache over TeaCache. Eval- uated under both slow and fast configurations, FlowCache delivers higher video quality and reduced latency across diverse models and acceleration ratios. As shown in Table FlowCache demonstrates superior afficiency-qualiin trade-offs compared to TeaCache. On MAGI-T, while TeaCache-fie fast suf- fers a significant quality degradation (VBench drops from 77.50 to 70.11) when accelerating from 1.12x to 1,44x, FlowCache-fast achieves 2.38x speedup while maintaining high visual quality (VBench 77.93), even slightly surpassing the baseline. FlowCache-slow delivers the best quality among all variants with a 1.86x acceleration. The advantage is more pronounced on SkyReels- V2. FlowCache-slond achieves 5.88x acceleration with minimal quality loss (VBench 83. 12), signifi- cantly outperforming TeaCache-slow (1.89x, VBench 82.67). FlowCache-Fii fast maintains excellent quality (VBench 83.05) 6. 7x speedup, whereas TeaCache-fase drops 80.06 2. 2.2x acceleration.

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Published as a conference paper at ICLR 2026
Table 2: Ablation Study of FlowCache's Key Components: Reuse Strategy and KV Cache Com- pression.
Reuse
KV Cache
VBench
LPIPS
SSIM
PSNR
Model
Strategy
Compression
77.06%
TeaCache
70.11%
0.8160
0.1138
8.94
MAGI-1
ChunkWise
77.66%
0.4208
0.5226
19.89
ChunkWisc
Enabled
77.93%
0.4311
0.5140
19.27
83.34%
TeaCache ChunkWise
80.06%
0.3063
0.6121
18.39
SkyReels-Vi V2
83. 08.12%
0,1225
0.789
23.74
ChunkWise
Enabled
83.01%
0.1565
0.7425
22.61
4.3 ABLATION STUDY
Effectiveness of Individual Components. conduct ablation studies on two key compo- nents strategy and KV cache compression-to validate the effectiveness of our approach. To ensure a fair comparison, we consistently employ the fast variant across ablation settings. As shown in Table
the MAGI-1 model, applying TeaCache (Liu et al. 2025al reuse strategy leads to a significant degradation in video generation quality, whereas chunk reuse preserves the model's original performance. Moreover, KV cache compression has negligible impact generation quality, confirming the soundness our designed KV cache compression mechanism. Consistent improvements are observed the SkyReels-V2 model. TeaCache reuse strategy results in substantial quality degradation. In contrast, chunkwise reuse achieves acceleration comparable TeaCache but without compromising performance, robustly demonstrating its effectiveness. The incorporation of KV cache compression again incurs only negligible performance loss, further validating the generalizability of our method.
Application on the 16-step distilled model. We conducted comprehensive experiments on the 16- step distilled MAGI-1 model. Due computational constraints, we evaluated performance using representative VBench metrics selected based on established practices in video generation compres- sion research (Zhao et al., 20244 Feng et al. 2025 Yang et al.) [haao et al., 2025). facilitate an intuitive comparison, we compute the average scores of the selected VBench metrics using the of- ficial normalization and weighting methodology provided by the VBench benchmark. For details, please Appendix E.1
CONCLUSION
In this work, we have presented FlowCache, a novel and efficient framework specifically designed to accelerate autoregressive video generation models. Our approach grounded a key empirical and theoretical insight: the denoising trajectories of individual video chunks are highly heterogeneous, exhibiting distinct levels temporal similarity at any given timestep. To address this, FlowCache introduces chunkwise adaptive caching strategy that independently manages the reuse policy for each video chunk based on its unique denoising state. Furthermore, to tackle the substantial mem- Key-Value
ory overhead from the growing Key- Value (KV) cache-ae mechanism crucial for maintaining long- range temporal consistency-ww we propose dedicated KV cache compression method. Extensive experiments representative autoregressive models, MAGI-1 and SkyReels-V V2, demonstrate the effectiveness our approach. These results establish FlowCache as a new state-of-the-ar for ef- ficient, training-free acceleration, effectively bridging the gap between the theoretical promise of autoregressive video models and the practical feasibility of real-time, ultra-long video synthesis.
ACKNOWLEDGMENTS
This work is supported by the National Key Research and Development Program of China (No. 2025YFE01 1 13500), the National Science Fund for Distinguished Young Scholars (No. 62025603), the Fundamental Research Funds for the Central Universities, the National Natural Sci- ence Foun- dation of China (No. 62576299, No. U21B2037, No. U22B2051, No. U23A20383, No. 62176222,
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No. 62176223, No. 62176226, No. 62072386, No. 62072387, No. 62072389, No. 62002305. No. 62272401), and the Natural Science Foundation of Fujian Province of China (No. 2021J06003, No. 2022J06001).
REFERENCES
Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L Croci, Bo Li, Pashmina Cameron, Martin Jaggi. Dan Alistarh, Torsten Hoefler, and James Hensman. Quarot: Outlier-free free 4-bit inference in rotated llms. Advances in Neural Information Processing Systems,
2024.
Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh. Video generation models as world simulators. 2024. URL ptttps://openett com/ remm/reseachh/
deleo-geneeraionnnnon waw.ddeesssssooooaaaaaaaaaaaaa-- simulators
wos-worlde wooordd-io
Jiazi Bu, Pengyang Ling, Yujie Zhou, Yibin Wang, Yuhang Zang, Tong Wu, Dahua Lin, and Jiaqi Wang. Dicache: Let diffusion model determine its own cache. arXiv preprint arXiv:2508. 17356, 2025.
Zefan Cai, Wen Xiao, Hanshi Sun, Cheng Luo, Yikai Zhang, Ke Wan, Yucheng Li, Yeyang Zhou, Li- Wen Chang, Jiuxiang Gu, al. R-kv: Redundancy-awe aware kv cache compression for training- free reasoning models acceleration. arXiv preprint arXiv:2505.24133, 2025.
Boyuan Chen, Diego Martí Monsó, Yilun Du, Max Simchowitz, Russ Tedrake, and Vincent Sitz- mann. Diffusion forcing: Next-token prediction meets full-sequence diffusion. Advances in Neural Information Processing Systems, 37:24081-241255 2024a.
Guibin Chen, Dixuan Lin, Jiangping Yang. Chunze Lin, Junchen Zhu, Mingyuan Fan, Hao Zhang, Sheng Chen, Zheng Chen, Chengcheng Ma, et al. Skyreels-v2: Infinite-length film generative model. arXiv preprint arXiv:2504. 13074, 2025a.
Mengzhao Chen, Yi Liu, Jiahao Wang. Yi Bin. Wenqi Shao, and Ping Luo. Prefixquant: Elim- inating outliers by prefixed tokens for large language models quantization. arXiv preprint arXiv:2410.05265. 2024b.
Mengzhao Chen, Wenqi Shao, Peng Xu, Jiahao Wang, Peng Gao. Kaipeng Zhang, and Ping Luo. Efficientqat: Efficient quantization-aware training for large language models. In Proceedings of the 63rd Annual Meeting the Association for Computational Linguistics (Volume 1: Long Papers), pp. 10081-10100, 2025b.
Mengzhao Chen, Meng Wu, Hui Jin, Zhihang Yuan, Jing Liu, Chaoyi Zhang, Yunshui Li, Jie Huang, Jin Ma, Zeyue Xue, al. Int vs fp: A comprehensive study of fine-grained low-bit quantization formats. arXiv preprint arXiv:2510.25602, 2025c.
Mengzhao Chen, Chaoyi Zhang, Jing Liu, Yutao Zeng. Zeyue Xue, Zhiheng Liu, Yunshui Li, Jin Ma, Jie Huang, Xun Zhou, et al. Scaling law for quantization-awe aware training. arXiv preprint arXiv:2505. 14302, 2025d.
Pengtao Chen, Mingzhu Shen, Peng Ye, Jianjian Cao, Chongjun Tu, Christos-Savvas Bouganis, Yiren Zhao, and Tao Chen. 8-dit: A training- free acceleration method tailored for diffusion transformers. arXiv preprint arXiv:2406.01122 2024c.
Jiaxiang Cheng, Pan Xie, Xin Xia, Jiashi Li, Jie Wu, Yuxi Ren, Huixia Li, Xuefeng Xiao, Shilei Wen, and Lean Fu. Resadapter: Domain consistent resolution adapter for diffussio models. In Proceedings of the AAAI Conference Artificial Intelligence, volume 39, pp. 2438-2446, 2025.
Justin Cui, Jie Wu, Ming Li, Tao Yang, Xiaojie Li, Rui Wang, Andrew Bai, Yuanhao Ban, and Cho- Jui Hsich. Self-forcing+4: Towards minute-scale high-quality video generation. arXiv preprint arXiv:2510.02283. 2025.
Justin Cui, Jie Wu, Ming Li, Tao Yang, Xiaojie Li, Rui Wang, Andrew Bai, Yuanhao Ban, and Cho-Jui Hsieh. Lol: Longer than longer, scaling video generation to hour. arXiv preprint arXiv:2611.16914 16914, 2026.
11

---

Published as a conference paper at ICLR 2026
Weilun Feng, Chuanguang Yang, Haotong Qin, Xiangqi Li, Yu Wang, Zhulin An, Libo Huang, Boyu Diao, Zixiang Zhao, Yongjun Xu, et al. Q-vdit: Towards accurate quantization and distillation of video-generation diffusion transformers. arXiv preprint arXi::2505.22667 2025.
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. Gptq: Accurate post-training quantization for generative pre-trained transformers. arXiv preprint arXiv:2210. 173323,, 2022.
Ao Fu, Yi Zhou, Tao Zhou, Yi Yang. Bojun Gao, Qun Li, Guobin Wu, and Ling Shao. Exploring the interplay between video generation and world models in autonomous driving: A survey. arXiv preprint 1rXxv:2411.02914. 2024.
Yu Gao, Haoyuan Guo, Tuyen Hoang, Weilin Huang, Lu Jiang, Fangyuan Kong, Huixia Li, Jiashi Li, Liang Li, Xiaojie Li, et al. Seedance 1.0: Exploring the boundaries of video generation models. arXiv preprint wwxx::25060091111 2025.
Yefei He, Jing Liu, Weijia Wu, Hong Zhou, and Bohan Zhuang. Efficientdm: Efficient quantization- aware fine-tuning of low-bit diffusion models. arXiv preprint arXiv:2310.03277 2023a.
Yefei He, Luping Liu, Jing Liu, Weijia Wu, Hong Zhou, and Bohan Zhuang. Ptqd: Accurate post- training quantization for diffusion models. arXiv preprint arXiv:2305. /0657,
Wei Huang, Yangdong Liu, Haotong Qin, Ying Li, Shiming Zhang, Xianglong Liu, Michele Magno, and Xiaojuan Qi. Billm: Pushing the limit of post-training quantization for llms. In International Conference Machine Learning, pp. 20023-20042 PMLR, 2024a.
Weizhong Huang, Yuxin Zhang, Xiawu Zheng. Fei Chao, and Rongrong Ji. Determining layer- wise sparsity for large language models through theoretical perspective. arXiv preprint arXiv:2502. 14770, 2025a.
Weizhong Huang, Yuxin Zhang, Xiawu Zheng, Yang Liu, Jing Lin, Yiwu Yao, and Rongrong Ji. Dy- namic low-rank sparse adaptation for large language models. arXiv preprint arXiv:2502.14816, 2025b.
Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianx- ing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive benchmark suite for video generative models. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pp. 21807-21818, 2024b.
AJAY KUMAR JAISWAL, Yifan Wang, Lu Yin, Shiwei Liu, Runjin Chen, Jiawei Zhao, Ananth Grama, Yuandong Tian, and Zhangyang Wang. From low rank gradient subspace stabilization to low-rank weights: Observations, theories, and applications. In Forty-second International Conference on Machine Learning.
Jiayi Ji, Haowei Wang, Changli Wu, Yiwei Ma, Xiaoshuai Sun, and Rongrong Ji. Jm3d & jm3d-Ilmm Elevating 3d representation with joint multi-modal cues. IEEE Transactions Pattern Analysis and Machine Intelligence, 2024.
Huiqiang Jiang, Yucheng Li, Chengruidong Zhang, Qianhui Wu, Xufang Luo, Surin Ahn, Zhenhua Han, Amir Abdi, Dongsheng Li, Chin-Yeu Lin, al. Minference 1.0: Accelerating pre-filling for cong-context Iims via dynamic sparse attention. Advances in Neural Information Processing Systems, 37:52481-5251 2024.
Kumara Kahatapitiya, Haozhe Liu, Sen He, Ding Liu, Menglin Jia, Chenyang Zhang, Michael Ryoo, and Tian Xie. Adaptive caching for faster video generation with diffusion transformers. arXiv preprint arXiv:2411.02334 2024.
Tero Karras, Miik Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion- based generative models. Advances in neural information processing systems, 02226665-25777 2022.
Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models. arXiv preprint arXiv:2412.03600 2024.
12

---

Published as a conference paper at ICLR 2026
Xunhao Lai, Jianqiao Lu, Yao Luo, Yiyuan Ma, and Xun Zhou. Flexprefill: A context- sparse attention mechanism for efficient unn-sequence inference. arXiv preprint arXiv:2502.20766, 2025.
Muyang Li, Yujun Lin, Zhekai Zhang, Tianle Cai, Xiuyu Li, Junxian Guo, Enze Xie, Chenlin Meng, Jun- Yan Zhu, and Song Han. Svdquant: Absorbing outliers by low-rank components for 4-bit diffusion models. arXiv preprint 2.Xiv:2411.05005 2024a.
Pengxiang Li, Yefan Zhou, Dilxat Muhtar, Lu Yin, Shilin Yan, Shen, Yi Liang, Soroush Vosoughi, and Shiwei Liu. Diffusion language models know the answer before decoding. arXiv preprint arXiv:2508. 19982, 2025a.
Xiaodi Li, Pan Xie, Yi Ren, Qijun Gan, Chen Zhang, Fangyuan Kong, Xiang Yin, Bingyue Peng, and Zehuan Yuan. Infinityhuman: Towards long-term audio-driven human. arXiv preprint arXiv:2508.202111 20210, 2025b.
Xudong Li, Timin Gao, Runze Hu, Yan Zhang, Shengchuan Zhang, Xiawu Zheng, Jingyuan Zheng, Yunhang Shen, Ke Li, Yutao Liu, et al. Adaptive feature selection for no-reference image quality assessment by mitigating semantic noise sensitivity. arXiv preprint arXiv:2312.06158. 2023.
Xudong Li, Runze Hu, Jingyuan Zheng, Yan Zhang, Shengchuan Zhang. Xiawu Zheng, Ke Li, Yunhang Shen, Yutao Liu, Pingyang Dai, et al. Integrating global context contrast and local sensitivity for blind image quality assessment. In Forty-first International Conference on Machine Learning. 2024b.
Xudong Li, Zihao Huang, Yan Zhang, Yunhang Shen, Ke Li, Xiawu Zheng, Liujuan Cao, and Rongrong Ji. Few-shot image quality assessment via adaptation of lision-language models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10442-10452, 2025c.
Xudong Li, Wenjie Nie, Yan Zhang, Runze Hu, Ke Li, Xiawu Zheng, and Liujuan Cao. Distilling spatially-heter -etealle-eeeeooeeeoous distortion perception for blind image quality assessment. In Proceedings of the Computer Vision and Puttern Recognition Conference, pp. 2344-2354, 2025d.
Xudong Li, Mengdan Zhang, Peixian Chen, Xiawu Zheng, Yan Zhang, Jingyuan Zheng, Yunhang Shen, Ke Li, Chaoyou Fu, Xing Sun, et al. Zooming from context to cue: Hierarchical preference optimization for multi-image milms, arXiv preprint arXiv:2505.22390 2025e.
Xudong Li, Yan Zhang, Yunhang Shen, Ke Li, Runze Hu, Xiawu Zheng, and Sicheng Zhao. Fea- ture denoising diffusion model for blind image quality assessment. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 5004-5012, 2025f.
Yuhong Li, Yinghing Huang, Bowen Yang, Bharat Venkitesh, Acyr Locatelli, Hanchen Ye, Tianle Cai, Patrick Lewis, and Deming Chen. Snapkv: Lim knows what you are looking for before generation. Advances in Neural Information Processing Systems, 37:22947-22970, 2024c.
Bin Lin, Yunyang Ge, Xinhua Cheng, Zongjian Li, Bin Zhu, Shaodong Wang, Xianyi He, Yang Ye, Shenghai Yuan, Liuhan Chen, et al. Open-sora plan: Open-source large video generation model. arXiv preprint arXiv:2412.00131,, 2024a.
Haokun Lin, Haobo Xu, Yichen Wu, Jingzhi Cui, Yingtao Zhang, Linzhan Mou, Linqi Song. Zhenan Sun, and Ying Wei. Duquant: Distributing outliers via dual transformation makes stronger quan- tized llms. Advances in Neural Information Processing Systems, 37:87766-8787 2024b.
Haokun Lin, Haobo Xu, Yichen Wu, Ziyu Guo, Renrui Zhang, Zhichao Lu, Ying Wei, Qingfu Zhang, and Zhenan Sun. Quantization meets dlims: A systematic study of post-training quanti- zation for diffusion llms. arXiv preprint ar Xiv: 14896 2025.
Haokun Lin, Xinle Jia, Shaozhen Liu, Shujun Xia, Weitao Huang, Haobo Xu, Junyang Li, Yicheng Xiao, Xingrun Xing, Ziyu Guo, al. Efficient diffusion language models: A comprehensive survey. Authorea Preprints, 2026.
Yaron Lipman, Ricky TQ Chen, Heli Ben- Gen-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. arXiv preprint 21Xiv:2210.02747, 2022.
13

---

Published as a conference paper at ICLR 2026
Feng Liu, Shiwei Zhang. Xiaofeng Wang, Yujie Wei, Haonan Qiu, Yuzhong Zhao, Yingya Zhang, Qixiang Ye, and Fang Wan. Timestep embedding tells: It's time to cache for video diffusion model. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 7353- 7363, 2025a.
Jiacheng Liu, Chang Zou, Yuanhuiyi Lyu, Junjie Chen, and Linfeng Zhang. From reusing to fore- casting: Accelerating diffusion models with taylorseers. arXiv preprint arXiv:2503.0692. 2025b.
Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi. Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, and Decebal Constantin Mocanu. Sparse training via boosting pruning plasticity with neuroregeneration. Advances in Neural Information Processing Systems, 1199908-9922 2021a.
Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, and Mykola Pechenizkiy. Do actually need dense over-parameterivaaion?? in-time over-parameterization in sparse training. In International Conference on Machine Learning, pp. 9989-7000. PMLR, 2021b.
Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decehal Constantin Mocanu, Zhangyang Wang, and Mykola Pechenizkiy. The unreasonable effectiveness of random pruning: Return of the most naive baseline for sparse training. arXiv preprint wxxiv2202.006444 2022.
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. Advances in neural information processing systems, 35:5775-5787, 2022.
Haiquan Lu, Yefan Zhou, Shiwei Liu, Zhangyang Wang, Michael W Mahoney, and Yaoqing Yang. Alphapruning: Using heavy-tailed self regularization theory for improved layer-wish pruning of large language models. Advances in neural information processing systems, 37:9117-9152 2024.
Yanzuo Lu, Yuxi Ren, Xin Xia, Shanchuan Lin, Xing Wang, Xuefeng Xiao, Andy J Ma, Xiaohua Xie, and Jian-Huang Lai. Adversarial distribution matching for diffusion distillation towards efficient image and video synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 168 16888, 2025.
Yongdong Luo, Xiawu Zheng, Guilin Li, Shukang Yin, Haojia Lin, Chaoyou Fu, Jinfa Huang, Jiayi Ji, Fei Chao, Jiebo Luo, et al. Video-rag: Visually-aligned retrieval-augmenned long video comprehension. arXiv preprint arXiv:2411. 13.093,3, 2024.
Yongdong Luo, Wang Chen, Xiawu Zheng, Weizhong Huang, Shukang Yin, Haojia Lin, Chaoyou Fu, Jinfa Huang, Jiayi Ji, Jiebo Luo, et al. Quota: Query-oriented token assignment via query decouple for long video comprehension. arXiv preprint 25X3::5503308699 2025.
Enhui Ma. Lijun Zhou, Tao Tang, Zhan Zhang, Dong Han, Junpeng Jiang, Kun Zhan, Peng Jia, Xianpeng Lang, Haiyang Sun, et al. Unleashing generalization of end-to-end autonomous driving with controllable long video generation. arXiv preprint arXiv:2406. 01349, 2024a.
Qingchuan Ma, Yuhang WU, Xiawu Zheng, and Rongrong Ji. Benchmarking abstract and reasoning abilities through theoretical perspective. arXiv preprint arXiv:2505.23883, 2025.
Xinyin Ma. Gongfan Fang, and Xinchao Wang. Deepcache: Accelerating diffusion models for free. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15762-15772, June 2024b.
Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans. On distillation of guided diffusion models. In Proceedings of the IEFF/CVF conference on computer vision and pattern recognition, pp. 14297-14306. 2023.
Saman Motamed, Laura Culp, Kevin Swersky, Priyank Jaini, and Robert Geirhos. Do generative video models understand physical principles? arXiv preprint a0X9022501.09038, 2025.
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEFE/CVF international conference on computer vision, pp. 4195-4205. 2023.
14

---

Published as a conference paper at ICLR 2026
Xiangyu Peng, Zangwei Zheng, Chenhui Shen, Tom Young, Xinying Guo, Binluo Wang. Hang Xu, Hongxin Liu, Mingyan Jiang, Wenjun Li, Yuhui Wang, Anbang Ye, Gang Ren, Qianran Ma, Wanying Liang. Xiang Lian, Xiwen Wu, Yuting Zhong, Zhuangyan Li, Chaoyu Gong, Guo- jun Lei, Leijun Cheng, Limin Zhang, Minghao Li, Ruijie Zhang, Silan Hu, Shijie Huang, Xi- aokang Wang, Yuanheng Zhao, Yuqi Wang, Ziang Wei, and Yang You. Open-sora 2.0: Training commercial-level video generation model in $200k. arXiv preprint arXiv:2503 09642, 2025.
Haotong Qin, Ruihao Gong, Xianglong Liu, Mingzhu Shen, Ziran Wei, Fengwei Yu, and Jingkuan Song. Forward and backward information retention for accurate binary neural networks. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 2250- 2259, 2020.
Haotong Qin, Yifu Ding, Mingyuan Zhang, Qinghua YAN, Aishan Liu, Qingqing Dang, Ziwei Liu, and Xianglong Liu. Bibert: Accurate fully binarized bert. In International Conference on Learning Representations, 2022.
Haotong Qin, Xudong Ma, Xingyu Zheng, Xiaoyang Li, Yang Zhang, Shouda Liu, Jie Luo, Xian- glong Liu, and Michele Magno. Accurate lora-finetuning quantization of Ilms via information retention. In International Conference on Machine Learning, pp. 4 41498-41516. PMLR, 2024.
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedi- image segmentation. In International Conference on Medical image computing and computer- assisted intervention, pp. 234 241. Springer, 2015.
Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach. Adversarial diffusion dis- tillation. In European Conference on Computer Vision, pp. 87-103. Springer, 2024.
Pratheba Selvaraju, Tianyu Ding, Tianyi Chen, Ilya Zharkov, and Luming Liang. Fora: Fost-forward caching in diffusion transformer acceleration. arXiv preprint 2407::240..01425, 2024.
Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu, Lirui Zhao, Zhiqian Li, Kaipeng Zhang, Peng Gao, Yu Qiao, and Ping Luo. Omniquant: Omnidirectionally calibrated quantization for large language models. arXiv preprint WxXiv:2308.13133 2023.
Yihua Shao, Deyang Lin, Fanhu Zeng, Minxi Yan, Muyang Zhang, Siyu Chen, Yuxuan Fan, Ziyang Yan, Haozhe Wang, Jingcai Guo, et al. Tr-dq: Time-rotation diffusion quantization. arXiv preprint arXiv:2503.0665 065564 2025.
Kiwhan Song, Boyuan Chen, Max Simehowitz, Yilun Du, Russ Tedrake, and Vincent Sitzmann. History-guided video diffusion. arXiv preprint arXiv:2502.06764, 2025.
Wenfang Sun, Xinyuan Song, Pengxiang Li, Lu Yin, Yefeng Zheng, and Shiwei Liu. The curse of depth in large language models. arXiv preprint arXiv:2502.057995 2025.
Hansi Teng, Hongyu Jia, Lei Sun, Lingzhi Li, Maolin Li, Mingqiu Tang, Shuai Han, Tianning Zhang, WQ Zhang, Weifeng Luo, et al. Magi-l: Autoregressive video generation at scale. arXiv preprint ar arXiv:250..13211 2025.
Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, Jianyuan Zeng. Jiayu Wang, Jingfeng Zhang, Jingren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng. Ningyi Zhang, Pandeng Li, Pingyu Wu, Ruihang Chu, Ruili Feng. Shiwei Zhang, Siyang Sun, Tao Fang, Tianxing Wang. Tianyi Gui, Tingyu Weng. Tong Shen, Wei Lin, Wei Wang, Wei Wang, Wenmeng Zhou, Wente Wang, Wenting Shen, Wenyuan Yu, Xianzhong Shi, Xiaoming Huang, Xin Xu, Yan Kou, Yangyu Lv, Yifei Li, Yijing Liu, Yiming Wang. Yingya Zhang, Yitong Huang. Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng, Yuntao Hong, Yupeng Shi, Yutong Feng, Zeyinzi Jiang, Zhen Han, Zhi-Fan Wu, and Ziyu Liu. Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv:2503 20314, 2025.
Zhongwei Wan, Xinjian Wu, Yu Zhang, Yi Xin, Chaofan Tao, Zhihong Zhu, Xin Wang, Siqi Luo, Jing Xiong, and Mi Zhang. D2o: Dynamic discriminative operations for efficient generative inference of large language models. arXiv preprint arXiv:2406. 13035, 2024.
15

---

Published as a conference paper at ICLR 2026
Haowei Wang, Jiayi Ji, Tianyu Guo, Yilong Yang, Xiaoshuai Sun, and Rongrong Ji. Nice: improv- ing panoptic narrative detection and segmentation with cascading collaborative learning. 1EEE Transactions Pattern Analysis and Machine Intelligence, 2025.
Xiang Wang, Shiwei Zhang, Han Zhang, Yu Liu, Yingya Zhang, Changxin Gao, and Nong Sang. Videolem: Video latent consistency model. arXiv preprint 2312:2231..09109, 2023.
Mingrui Wu, Xinyue Cai, Jiayi Ji, Jiale Li, Oucheng Huang, Gen Luo, Hao Fei, Guannan Jiang, Xi- aoshuai Sun, and Rongrong Ji. Controlmllm: Training- free visual prompt learning for multimodal large language models. Advances in Neural Information Processing Systems, 37:45206-4555 45234, 2024.
Mingrui Wu, Lu Wang, Pu Zhao, Fangkai Yang, Jianjin Zhang, Jianfeng Liu, Yuefeng Zhan, Weihao Han, Hao Sun, Jiayi Ji, et al. Reprompt: Reasoning- augmented reprompting for text-to-image generation via reinforcement learning. arXiv preprint arXiv:2505. /7540, 2025.
Haocheng Xi, Shuo Yang. Yilong Zhao, Chenfeng Xu, Muyang Li, Xiuyu Li, Yujun Lin, Han Cai, Jintao Zhang, Dacheng Li, et al. Sparse videogen: Accelerating video diffusion transformers with spatial-temporal sparsity. arXiv preprint arXiv:2502.01776 2025.
Ruyi Xu, Guangxuan Xiao, Haofeng Huang, Junxian Guo, and Song Han. Xattention: Block sparse attention with antidiagonal scoring. arXiv preprint arXiv:2503. 164288 2025.
Weiqi Yan, Lvhai Chen, Huaijia Kou, Shengchuan Zhang, Yan Zhang, and Liujuan Cao. Ucod-dpl: Unsupervised camouflaged object detection via dynamic pscudo-label learning. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 30365-30375, 2025a.
Weiqi Yan, Lvhai Chen, Shengchuan Zhang, Yan Zhang, and Liujuan Cao. Scout: Semi-supervised camouflaged object detection by utilizing text and adaptive data selection. arXiv preprint arXiv:2508. 17843, 2025b.
Lianwei Yang, Haokun Lin, Tianchen Zhao, Yichen Wu, Hongyu Zhu, Ruiqi Xie, Zhenan Sun, Yu Wang, and Qingyi Gu. Lrq-dit: Log-rotation post-training quantization of diffusion trans- formers for image and video generation. arXiv preprint ar arXiv:2508. 250.05550803485,, 2025a.
Lin Yang. Li, Yuxiang Fu, et al. Veta-dit: Variance-equallized and temporally adaptive quantiza- tion for efficient 4-bit diffusion transformers. In The Thirty-ninth Annual Conference on Neural Information Processing Systems.
Shuo Yang, Haocheng Xi, Yilong Zhao, Muyang Li, Jintao Zhang, Han Cai, Yujun Lin, Xiuyu Li, Chenfeng Xu, Kelly Peng, al. Sparse videogen2: Accelerate video generation with sparse attention via semantic-ane aware permutation. arXiv preprint arXiv:2505. 18875, 2025b.
Lu Yin, You Wu, Zhenyu Zhang, Cheng- Yu Hsieh, Yaqing Wang, Yiling Jia, Gen Li, Ajay Jaiswal, Mykola Pechenizkiy, Yi Liang, et al. Outlier weighed layerwise sparsity (owl): A missing secret sauce for pruning Iims high sparsity. arXiv preprint 1rX1v:2310.05115. 2023.
Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William Freeman, and Taesung Park. One-step diffusion with distribution matching distillation. In Proceedings of the IEEE/CVF conference on computer vision and patlern recognition, pp. 6613-6623, 2024.
Tianwei Yin, Qiang Zhang, Richard Zhang, William T Freeman, Fredo Durand, Eli Shechtman, and Xun Huang, From slow bidirectional to fast autoregressive video diffusion models. In Proceed- ings of the Computer Vision and Patlern Recognition Conference, pp. 22963-22974, 2025.
Zhihang Yuan, Hanling Zhang, Lu Pu, Xuefei Ning, Linfeng Zhang, Tianchen Zhao, Shengen Yan, Guohao Dai, and Yu Wang. Ditfastattn: Attention compression for diffusion transformer models. Advances in Neural Information Processing Systems, 37:1 1269, 2024.
Zhihang Yuan, Rui Xie, Yuzhang Shang, Hanling Zhang, Siyuan Wang, Shengen Yan, Guohao Dai, and Yu Wang. Vgdfr: Diffusion--asee video generation with dynamic latent frame rate. arXiv preprint 2504v:2504 12259, 2025.
16

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Published as a conference paper at ICLR 2026
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 586-595, 2018.
Shaokun Zhang, Jieyu Zhang, Jiale Liu, Linxin Song, Chi Wang, Ranjay Krishna, and Qingyun Wu. Offline training of language model agents with functions as learnable weights. In Forty-first International Conference Machine Learning, 2024a.
Shaokun Zhang, Yi Dong, Jieyu Zhang, Jan Kautz, Bryan Catanzaro, Andrew Tao, Qingyun Wu, Zhiding Yu, and Guilin Liu. with reinforced reasoning. arXiv preprint ar arXiv:2505.0000 00024, 2025a.
Exploring tool-using language models
Shaokun Zhang, Ming Yin, Jieyu Zhang, Jiale Liu, Zhiguang Han, Jingyang Zhang, Beibin Li, Chi Wang. Huazheng Wang, Yiran Chen, et al. Which agent causes task failures and when? on automated failure attribution of Ilm multi-agent systems. arXiv preprint arXiv:2505.00212, 2025b.
Yuxin Zhang, Lirui Zhao, Mingbao Lin, Yunyun Sun, Yiwu Yao, Xingjia Han, Jared Tanner, Shiwei Liu, and Rongrong Ji. Dynamic sparse no training: Training-fie free fine-tuning for sparse Ilms. arXiv preprint arXiv:2310.089) 2023a.
Yuxin Zhang, Yuxuan Du, Gen Luo, Yunshan Zhong, Zhenyu Zhang, Shiwei Liu, and Rongrong Ji. Cam: Cache merging for memory-efiiciet Ilms inference. In Forty-first international conference on machine learning. 2024b.
Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Ré, Clark Barrett, al. H2o: Heavy-hitter oracle for efficient gen- erative inference of large language models. Advances in Neural Information Processing Systems, 36:34661-3477 10, 2023b.
Tianchen Zhao, Tongcheng Fang, Haofeng Huang, Enshu Liu, Rui Wan. Widyadewi Soedarmadji, Shiyao Li, Zinan Lin, Guohao Dai, Shengen Yan, et al. Vidit-q: Efficient and accurate quantiza- tion of diffusion transformers for image and video generation. arXiv preprint urXiv:2406. 200x:2240600250000 2024.
Wenliang Zhao, Lujia Bai, Yongming Rao, Jie Zhou, and Jiwen Lu. Unipe: A unified predictor- corrector framework for fast sampling of diffusion models. Advances in Neural Information Processing Systems, 36:49842-49866 2023.
Xuanlei Zhao, Xiaolong Jin, Kai Wang, and Yang You. Real-time video generation with pyramid attention broadcast, 2024b. URL https://axxii. org/abs/2408.12 12588.
Zangwei Zheng, Xiangyu Peng, Tianji Yang, Chenhui Shen, Shenggui Li, Hongxin Liu, Yukun Zhou, Tianyi Li, and Yang You. Open-sora: Democratizing efficient video production for all. arXiv preprint arXiv:2412.2044 2024.
Yunshan Zhong, Jiawei Hu, Mengzhao Chen, Rongrong Ji, et al. I&s-vit: An inclusive stable method for pushing the limit of post-training vits quantization. arXiv preprint arXiv:2311.10126, 2023.
Yunshan Zhong, Jiawei Hu, You Huang, Yuxin Zhang, and Rongrong Ji. Erq: Error reduction for post-training quantization of vision transformers. In Forty-first International Conference Machine Learning, 2024.
Yunshan Zhong, You Huang, Jiawei Hu, Yuxin Zhang, and Rongrong Ji. Towards accurate post- training quantization of vision transformers via error reduction. IEEE Transactions Pattern Analysis and Machine Intelligence, 2025a
Yunshan Zhong, Yanwei Qi, and Yuxin Zhang. Test-time iterative error correction for efficient diffusion models. arXiv preprint arXiv:25/ 2.025....005.... 2025b.
Chenyu Zhou, Xiaoming Shi, Hui Qiu, Xiawu Zheng, Haitao Leng, Yankai Jiang, Shaoguo Liu, Tingting Gao, and Rongrong Ji. Mix-ecom: Towards mixed-type e-commerce dialogues with complex domain rules. arXiv preprint arXiv: 23836, 2025.
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Chang Zou, Xuyang Liu, Ting Liu, Siteng Huang, and Linfeng Zhang. Accelerating diffusion transformers with token-war wise feature caching. arXiv preprint arX1v:2410.05317,220 2024.
A LLM USAGE STATEMENT
Large Language Models (LLMs) were used to aid in the writing and polishing of the manuscript. Specifically, we used an LLM to assist in refining the language, improving readability, and ensuring clarity in various sections of the paper. The model helped with tasks such sentence rephrasing, grammar checking, and enhancing the overall flow of the text.
important to note that the L.M was not involved in the ideation, research methodology, or experimental design. All research concepts, ideas, and analyses were developed and conducted by the authors. The contributions of the LLM were solely focused on improving the linguistic quality of the paper, with involvement in the scientific content or data analysis.
The authors take full responsibility for the content of the manuscript, including any text generated or polished by the LLM. We have ensured that the LLM-generated text adheres to ethical guidelines and does not contribute to plagiarism or scientific misconduct.
B PROOF OF THEOREM
Theorem Assume the diffusion model vg has converged to the optimal velocity field of the flow matching objective. Further, assume the scheduling seheculiinfuunciion is of the power-law form a (1) = (1/T)P for some constant power p O and total time T.
Given 0 < t < 12 < T and a data chunk X' whose initial noise state Xip. is drawn from N(0, I), the following inequality holds:
Llra(XX,t, 2 Llyet(X,tz,)
(13)
Proof The relative LI distance is defined (X,
[X: To prove the theo-
rem, we will analyze the monotonicity of this function with respect to time t.
First, according the theorem's assumption that the model is optimal, we can replace vo with its ideal form in the flow matching framework:
d'(1) (X; Xx)
Co (x t, o(t)
Substituting this into the LArel expression gives:
|| o'(t) (Xi - Xb) A|1,, a(1)
[x,ttXXt
|X
o')
||]
Xtll,
|At
o(1)
||X|||
Since at) is monotonically increasing for > both o'( and (t) are positive, as is At. We can therefore remove the absolute value signs. Let's analyze the expression a product of two time-dependent functions, A(t) and B(I):
o'(()
[|X - Xilh
L1 (X,,,XX11)))
A
(t)
||
A(E)
B(O)
Next, we analyze the behavior of A(l) and B(I) individually.
Analysis of A(t): According to the theorem's assumption, (()) = (1/T)P. Its derivative is o'(1)
p-p-- 7 Tv
Therefore, A(t) becomes:
o'(t)
p. p. 1TT
A(t) a(t)
1o 1//T9
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Fort E (), the function A(t) p/t is strictly monotonically decreasing function.
Analysis of B(t): We expand the norms B(t) using the forward process definition X = (1
(tt)) t (t))X The numerator term becomes:
|x -;ila (lo(t) noise x1)112 o [t] -Xollı
Since (t) is monotonically increasing with t, the numerator is a monotonically increasing function of
The denominator is |Xilli= - - + (t) In high-dimensional latent spaces representing natural data (like video chunks), the structured data Xi generally possesses higher norm than an unstructured Gaussian noise vector X Xnoise of the same dimension. The interpolation X; represents a smooth transition from the data manifold the noise distribution. Consequently. is standard observation that the LI norm [|x 1 is monotonically decreasing function as i goes from 0 to
Thus, (t)) is the ratio of monotonically increasing function (numerator) and monotonically de- creasing function functinn (denominator). This implies that (t) itself is a monotonically increasing function t.
Combined Analysis:
We need to determine the behavior of the product Llret X A(I) B()).
have strictly decreasing function A(t) ~ 1/tt and an increasing function B(t). To show that their product is decreasing, we must argue that the decay of A(t) dominates the growth of B(t).
Let's consider the derivative of their product f(t) ((t)
('(1) (I) + (())B(()
Substituting A(t) = p/t and A'(t) = -p/P,, the condition becomes:
(t) 00
P B P B(t) t
B3(1) B(t) -
This inequality, stating that the relative growth rate of B(t) is less than 1/t, holds for wide range of functions that exhibit sub-linear or saturating growth. Given that B(t) starts from B(0) = 0 and increases towards a finite constant BT)), its growth rate (') diminishes 1/t decays. The strong hyperbolic decay factor A(t) = p/t from the scheduler's design effectively dominates the milder, saturating growth of the norm ratio Bt)). This ensures that their product is monotonically decreasing function for & E (0,T).
time, while the term
Since Ll (X,t,, is proportional this monotonically decreasing product, for any < t1
t2 $ T, we have:
(X,a(X Llya(XX12.0)
This completes the proof.
C PROOF OF COROLLARY
Corollary (Cross-Chuul Chunk Divergence of Relative L1 Distance) Assuming that distinct video chunks # j represent heterogeneous content such that their state norms differ any inter- mediate timestep & (0,T) (i.e., ||]]|| # || and that the model's update magnitude [|vo c) Atll1 is approximately invariant across chunks for fixed i, then their relative L. distances are unequal:
(X, i) + (lret(X
(14)
Proof 2 Our objective is to prove that Alra(XX1,) 1, + (X,et(X,t,)) t,j) for + under the corol- lary's 's assumptions. We begin by recalling the definition from Equation 5 two distinct chunks, i and j:
(X, ||vo(X t) Atll,
||vo(X), t,c) Atll, (15)
(X,t,))
||x:11
|| ||
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We analyze the ratio of these quantities:
re | (,) Atll1 Livel (X,t,)) (1o t,)) Adll [X
|X
(16)
First, we consider the numerators of the original expressions. By the assumption of approximately constant update magnitude, the model's oulput norm is largely determined by the timestep I rather than the specific chunk content. This is because conditioning information from prior chunks, pro- cessed through mechanisms like attention with Softmax normalization, primarily steers the direction of the update vector rather than its overall magnitude. Thus, we can posit that for a fixed t:
| t, c) Atll, = 11 (x),XXtt,,) Atll
(17)
Therefore, the first term in Equation 16 approximately equal to 1.
Next, examine the denominators. The assumption of content heterogeneity implies that the target data distributions for each chunk, X and Xd. are different. Since the flow matching process di (1-o(f)) + (t) defines a unique trajectory from the initial noise TT to the final data Vo, the distinctness of X and X guarantees that their entire denoising paths are different. Consequently, any intermediate time (O,T), their states are unequal, leading to different LI norms:
|xiil| * ||x/|11
(18)
Substituting the approximation from Equation 17 into Equation 16 get:
Liret Ll (X,1,)
||
(19)
Given the inequality in Equation 18 the right-hand side of this approximation cannot be equal to 1. It follows directly that (X, & (X, t,,). This demonstrates that the relative LI distance metric inherently diverges across chunks at fixed timestep, reflecting their distinct underlying content and corresponding states along the denoising trajectory.
D IMPLEMENTATION AND EFFICIENCY DETAILS
D.1 IMPLEMENTATION DETAILS OF KV CACHE COMPRESSION
KV Cache compression begins only after the cache budget is reached. The process operates two phases:
1. A Cache Filling Phase: New KV cache entries are added to the cache until the budget is reached, with compression occurring during this phase.
2. A Compression Phase:
Once the cache is full, the compression mechanism is triggered upon each new KV cache entry arrival. Our method then compresses existing entries within the budget accommodate the new entry.
While KV cache compression is triggered upon reaching the specified budget, FlowCache also incor- porates cache reuse for input video chunk activations, which applies all video chunks throughout the generation pipeline. To assess the combined speedup, we measure end-to-end acceleration.
D.2 EFFICIENCY ANALYSIS OF KV CACHE COMPRESSION AND FEATURE REUSE
The importance and similarity judgments during KV cache compression, as well as the input cache retention in FlowCache, may raise questions about increased memory usage during inference. How- ever, our theoretical analysis and experimental results confirm that both types of additional overhead negligible.
According to Equations 9 and IT in the paper. KV cache compression introduces additional online dynamic overhead for computing KV cache importance. For Equation g
only last 50 query
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tokens to compute attention scores following previous work MInference Jiang et al. 2024 and Flexprefill (Lai et al,, 2023), allowing us to identify important elements in the KV cache without significant additional memory or computational overhead. For Equation IT derive an equivalent form that substantially reduces both additional memory (17.79 GB + 0.30 GB) and computational overhead (10.26s 0.077s) by exchanging the order of matrix multiplication and summation:
1 L
Σ
(Kll) k(b) T. )is
1 L
( clean Ki
(h))) T clean
(20)
1
LE
clean
i=1
Naive: 17.79 GB, 10.26s
Optimized: 0.30 GB, 0.077s
clean E Luxd In the formula, memory and time costs are evaluated at L = 24,300 and d 128 for illustrative purposes. Through this mathematical transformation, the memory and time overhead of our online dynamic KV cache importance computation becomes negligible.
where
(k)
For the input cache, given X E R4y staee the caching cost at different timesteps is fixed. When Lgg = 108000 and hidden_states states = 3072, the corresponding memory footprint is only 0.6 GB, which is negligible in the overall inference overhead.
Regarding computational overhead, we profiled the FLOPs mooating-point operations) of the oper- ation for selecting and retaining tokens in the KV cache. When generating a 10-second, 720x720 text-to-video sequence and with a KV cache budget of 5 chunks, this operation is performed only times throughout the entire generation process. The actual computational overhead of this op- eration is 0.0013 PFLOPs with token-level query granularity and 0.05 PFLOPs with frame-level query granularity, respectively- both negligible compared the total FLOPs of FlowCache-fast (140 PFLOPs).
The additional memory footprint and computational overhead introduced by FlowCache are there- fore negligible. Memory usage experimental results are shown in Table
E ABLATION STUDY DETAILS
E.1 APPLICATION ON THE 16-STEP DISTILLED MODEL.
Specifically, the VBench metries we selected are: Imaging Quality, Aesthetic Quality, Motion Smoothness, Dynamic Degree, Background Consistency, Subject Consistency, Scene, Overall Con- sistency. Our evaluation consists of two parts: (1) benchmarking the 16-step distilled baseline against FlowCache-fass applied to the 64-step model, and (2) comparing TeaCache and FlowCache when applied to the 16-step distilled version. Key findings include:
Distillation vs. Acceleration Trade-offs. The 16-step distilled baseline achieves faster inference (2.38x vs. 3.56x speedup) but compromises quality compared FlowCache-Fii on the 64-step model (71.69% vs. 70.26%).
Complementary Benefits. FlowCache applied the distilled model achieves superior speedup over TeaCache (1.92x vs. 1.17x) while preserving generation quality (70.72% vs. 70.60%).
These results Table demonstrate that FlowCache complements distillation techniques, offering orthogonal performance gains. Notably. FlowCache requires no large-scale distillation training of autoregressive models and operates as a plug-ande and-play online method.
E.2 PERFORMANCE UNDER DIFFERENT KV CACHE COMPRESSION SETTINGS.
MAGI-1 (Teng et al. 2025) has conducted an investigation into the Key-Value (KV) range mecha- nism on the Physics-IO IQ Benchmark (Motamed et al. 2025). This task imposes stringent demands on both physical commonsense reasoning and temporal modeling capabilities. To produce physically plausible videos, the model must effectively leverage and retain sufficiently long historical context.
Motivated by this, work systematically investigates how to maximally preserve critical historical context during KV cache compression in autoregressive video generation models, using the Physics- IQ benchmark as the evaluation setting.
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First, conduct granular analysis of queries and keys during the KV cache compression process, specifically investigating: (1) the appropriate granularity at which queries should be used to select salient KV cache entries, and (2) the appropriate granularity for keys to be retrieved. For further details, please refer to 1
Second, We perform ablation study the hyperparameter À in Equation 12 to systematically examine how to balance importance and redundancy reduction KV cache compression. For further details, please refer
E.2.2
Third, we evaluate our method under varying KV cache budgets to quantitatively analyze the trade- off between video generation quality and GPU memory consumption. For further details, please refer to E.2.3
E.2.1 COMPRESSION GRANULARITY
Experimental results are summarized Table Under the baseline configuration without any accel- eration compression strategy, MAGI-1 achieves accuracy of 47.660 47.60% on Physics-IQ. However, applying a non-discriminative global reuse strategy (TeaCache) causes a drastic performance drop to 13.69%, indicating that coarse-grained reuse severely degrades model performance. In contrast, the Chunk Wise reuse strategy maintains a substantially higher accuracy of 43.10%, significantly outperforming TeaCache.
Our experiments further reveal that, when the query granularity is fixed at the token level (specif- ically, select the last 50 tokens of the last denoising chunk the query), the choice of key granularity has a pronounced impact on performance. Preserving keys at the token level-rather than aggregating them into frame- or chunk-level representations- enables finer- iner-grained matching with historical context and yields higher accuracy (39.34% vs. 38.62% and 38.99% respectively). This finding underscores the importance of maintaining fine-grained key representations during KV cache compression to retain historically relevant information critical for accurate prediction.
Moreover, when key granularity fixed the token level, using frame-level queries achieves higher accuracy (39.53%) compared to token-level queries (39.34%). This suggests that retrieving histori- cal KV pairs at the semantically richer frame level facilitates more effective aggregation of context relevant to the current prediction, thereby better preserving physical consistency in the generated video. However, the experimental results indicate that this improvement is marginal, while using frame-level queries retrieve important key-value (KV) cache entries incurs substantial increase in GPU memory consumption. Therefore, we argue that using token-level queries to retrieve im- portant KV cache entries is sufficient- design choice that shares conceptual similarities with prior approaches such as Minference (Jiang et al. 2024) and FlexPrefill (Lai et al. 2025
E.2.2 HYPERPARAMETER
We conducted an ablation study on the hyperparameter Equation Results in T strate that Physics-IQ scores increase as À decreases, then stabilize beyond threshold value. This trend indicates that redundancy between KV caches (Equation [T] more effectively captures useful information from past video frames than the importance metric (Equation 9 alone. Based on these findings, we adopt À =0.07 uniformly across all video types and experiments.
E.2.3 DIFFERENT KV CACHE BUDGET
We have conducted additional experiments explicitly demonstrate the memory-quality trade-offs. Notably, all experiments exclude cache reuse mechanisms for fair comparison. Specifically, we evaluated Physics-IQ scores across varying peak memory budgets during inference. The results in Table 6 show that FlowCache reduces peak memory by up to 33.6% while maintaining stable Physics-IC scores (#0.1), even slightly outperforming Vanilla at chunks budget. This demonstrates its effectiveness in achieving substantial memory savings without compromising physics reasoning quality. These findings provide concrete evidence of the trade-off between memory efficiency and generation quality, strengthening contribution claimed.
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Table Quantitative evaluation of inference efficiency and visual quality between 16-step and 64- step in MAGI-I. (VBench' refers to the weighted sum of the scores across the eight representative metries we selected.)
PFLOPs
Speedup
Latency(s)
VBench t
Model
Method
Steps
+
t
Vanilla
64
306
1X
2873
69.12%
FlowCache
64
140
2.38x
1209
71.69%
MAGI-1
Vanilla
16
77
3.56x
808
70.26%
TeaCache
16
74
4.16x
691
70.60%
FlowCache 16
63
6.84x
420
70.72%
Table Ablation study of KV cache compression granularity for MAGI-1 on Physics-IQ Bench-
mark.
Reuse
KV Cache
Query Cranularity
Key Granularity
Physics-IQ
Strategy
Compression
47. 47.60%
TeaCache
13.69%
ChunkWiz
43.10%
ChunkWise
Enabled
Token
Token
39.34%
Chunk
Enabled
Token
Frame
28.62%
ChunkWir Enabled
Token Frame
Chunk
38.99%
Chunk Wise
Enabled
Token
39.53%
Table 5: Ablation study of hyperparameter for MAGI-1 on Physics-IQ Benchmark.
Physics-IQ
0.03
39.38%
0.07
39.53%
0.15
37.11%
0.20
38.42%
Table 6: Ablation study of different KV cache budget for MAGI-1 on Physics-IQ Benchmark.
Physics-IQ
Method
KV Cache Budget (Chunks)
Peak Memory (GB)
MAGI-1
8
42.84
47.60%
FlowCache w/o cache reuse FlowCache w/o cache reuse FlowCack w/o cache reuse
34,05
47.55%
31.25
46.72%
5
28.45
47.65%
F VISUALIZATION
We visualize the qualitative results on both MAGI-I and SkyReels-V2 V2 in Figure 4 and Figure 5 reporting the specific speedup ratios and VBench scores for each method (Vanilla, TeaCache. FlowCache-slow, and FlowCache-fastl As observed, FlowCache outperforms TeaCache in terms of fine-gre line-grained details and overall image quality, avoiding the visible noise artifacts that TeaCache tends to introduce. Moreover, videos featuring complex scenes or significant motion. both FlowCache variants demonstrate superior robustness compared TeaCache. Furthermore, while FlowCache-
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Prompt: corgt playing drum kit.
Prompt: teddy bear.
Yanill
Speedug: 1+
VBench: 77.06
TruCache
Speedags 1.2k
VBeach: 72.50
Flan Cechaahee
Speedeg:
Vienchi
flswCacho
Spealup: 2.8
VBench: 77.53
Prompl: A cal wearing sunglasses as lileguard pool.
Prompt: A fantasy landscape.
Vanilb
Speedug: 1x
77.00
TruCache Speddag: L.1ss
VDench: 77.50
Speedep:
Vilench
FlanCache Gast
Spodda:: 2.38
ViBechh 7738
Figure 4: Qualitative results of text-to-video generation on MAGI-I. We present TeaCache, FlchCsche-,,ow, flow and the Vanilla model. The frames are randomly sampled from the generated video.
fast achieves higher acceleration, FlowCache-slow preserves higher fidelity, maintaining greater consistency with the original video generation.
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Prompt: A white sherp grazes procefully in mendow.
Prompt: An elephant and bear interact peacefully in Eorest.
Vanilb
Spendog: Ix
Spadug: 22+
VBench: BUUl
Cachheces
Speedag: S.B
Spredogi
Prompt: fire hydrant.
Prompt: playing guitar.
Vanila
Vileeris RUHH
TeaCache
Vitackk B312
Spedap: 8.7x
Vilechs $105
Figure 5: Qualitative results of text-to-video generation SkyReels- We present TeaCache, CrowCache-slow, FlowCache-fast, and the Vanilla model. The frames are randomly sampled from the generated video.
25


Reviews:
[{"review_id": "V6e8BdbKPZ", "reviewer": "Reviewer_xZAs", "summary": "This paper proposed a caching framework for autoregressive video generation. The key idea is to change existing uniform caching strategies used in common video diffusion models. Firstly, it applies dynamic caching strategies to different noise levels. Higher noise level is more likely to reuse cached feature, while lower noise level is more likely to recompute. It also introduce a compression mechanism to kv cache due to large memory consumption based on importance and redundancy.", "questions": "- For the dynamic chunk caching and reuse mechanism, is it adaptively applied across different videos based on the degree of motion dynamics? As mentioned in Weakness 1, the decision-making process remains unclear — it would be helpful to provide a concrete inference example illustrating how this adaptation works.\n- Are there any known failure cases for the two proposed designs? For instance, how do they perform on videos with large or complex motion? In such cases, is the speed up improvement less significant?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The dynamic assignment of calculate or reuse significant improves the efficiency of the model i.e., more than 2x comparing to TeaCache-fast.\n- The proposed KV cache compression method balances both past token visual importance and redundancy. It is specifically designed for video token characteristics.", "weaknesses": "- The idea of reuse or recompute based on L1 similarity  of L1rel is clearly stated and proved, but the detail of how to decide to reuse or recompute the cache is not clear. Is there any threshold or decision making module for this part?\n- It is not clear why the proposed method both out perform the baseline method, TeaCache both in terms of speed and video quality. The reuse operation accelerates the speed, but why it also achieve better frame quality. It would better to have more detailed comparison and discussion between the baseline method.", "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": "BiapuQ0QA6", "reviewer": "Reviewer_LS46", "summary": "The paper studies training-free acceleration for autoregressive video diffusion. The authors empirically demonstrate that different chunks should have independent feature caching policies rather than a single global caching policy. A redundancy-aware KV-cache compression scheme is also adopted for long-video generation.\nExperiments on MAGI-1 and SkyReels-V2 demonstrate a noticeable speedup with a minor drop in VBench score.", "questions": "- line 99-101: Please clarify the experimental/testing setup for this result (e.g., GPU type, memory, batch size, video length). Without this, it’s hard to judge how generalizable the observation is.\n- The paper claims to “provide insights into memory–quality trade-offs,” but the experiments do not actually show memory vs. quality curves/tables (e.g., VBench vs. cache size/compression ratio). This weakens the contribution.\n- How is the chunkwise caching policy obtained in practice? Is it derived offline from a calibration set, or learned/heuristic? When generating videos of different lengths or motion patterns, does the policy need to be recomputed or adapted?\n- Do you have numerical benchmarks (peak memory, KV size per frame/chunk, vs. baseline) to substantiate the claim of reducing the memory with KV-cache compression?\n- misc:\n  - The citation of [1] is not about diffusion and seems unrelated to the context in which it is cited.\n  - VBench is a scaled score, not a percentage\n\n[1] Mengwei Xu, et la, Deepcache: Principled cache for mobile deep vision. 2018", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "- The paper proposed training-free, plug-in acceleration for causal video diffusion. Treating each video chunk as its own, with an independent reuse policy, is well motivated by the observed heterogeneity across chunks at the same timesteps.\n- Experiment isolates the benefit of chunkwise feature reuse over full reuse and shows that kv-compression has a small impact.", "weaknesses": "- Memory claims lack evidence. Memory usage is stated to be fixed, but no benchmark on memory usage is exhibited in the paper.\n- Lack of quality comparison. Only a few images are displayed in the paper. No video clip was provided, making it hard to evaluate the visual quality.\n- Lack of evaluation on long-video benchmark, e.g., VBench-long, since the method is claimed to be helpful for long-video generation.\n- MAGI-1 already applied window attention (8-second preceding video content). Weakening the motivation for KV-cache compression.\n- MAGI-1 has a shortcut step-distill version. The proposed method does not compare with it, nor apply the feature reuse to it.\n- Feature reuse necessarily increases memory consumption because the cache has to be retained. This seems to conflict with the stated motivation of KVcache compression, which is to reduce memory usage.", "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": "GUGMpcDI5v", "reviewer": "Reviewer_fkWp", "summary": "This paper introduces FlowCache, a novel caching and compression framework for autoregressive video generation that addresses the unique challenge of heterogeneous chunk denoising states. The method is theoretically grounded, empirically strong, and significantly advances the state of the art in efficient long-video synthesis.", "questions": "How sensitive is the KV cache compression performance to the choice of λ and the granularity settings? Is there a recommended configuration for different video types?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1.  The paper's primary strength is its clear identification and empirical validation of *why* existing caching methods fail for autoregressive models: the \"heterogeneous denoising states.\" This is a sharp and important insight.\n2. The \"chunkwise caching\" policy is a direct, logical, and highly effective solution to the problem identified. The ablation study decisively proves that this chunk-specific strategy is the main reason for the method's success in preserving quality.\n3. Additionally, the KV cache compression method is also well-motivated. It correctly identifies the high-redundancy problem in video data and proposes a solution that intelligently balances both importance and redundancy, which is a clear improvement over importance-only methods from language modeling. From my perspective, this is an excellent work overall.", "weaknesses": "1. There is an internal contradiction between the paper's theory and its empirical results. **Theorem 1** (and its proof in Appendix B) is used to establish that the relative L1 distance is a *monotonically decreasing* function of time (as $t$ goes from 0 to T). However, the paper's *own empirical plot* (Figure 2) and *main text* (e.g., \"relative L1 distance monotonically increases as denoising progresses\" in line 302) show the exact opposite. This contradiction undermines the stated theoretical foundation.\n2. The ablation in Table 2 suggests that the complex KV cache compression adds very little performance on top of the main chunkwise policy. On MAGI-1, the chunkwise policy *alone* achieves a 77.66% VBench score, while the *full* method with compression gets 77.93%. This small benefit may not justify the added complexity of the importance-redundancy scoring mechanism; more discussion is required.", "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": "gGZBL1XeJF", "reviewer": "Reviewer_3Ldg", "summary": "This paper proposes a **cache compression method** designed to significantly reduce the KV cache size in autoregressive (AR) video generation. The proposed approach effectively improves generation speed while largely maintaining output quality.", "questions": "See weakness above", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is **well-organized**, **logically structured**, and clearly highlights its main contributions.  \n- The proposed method introduces an **adaptive criterion** for KV cache compression, enabling dynamic adjustment of the compression ratio.  \n- This adaptive mechanism effectively improves cache compression efficiency and leads to faster video generation without significant quality degradation.", "weaknesses": "1. **Equation (8)** appears potentially ambiguous. It seems that the Softmax operation should be applied along the \\( L_k \\) dimension, but the current formulation places Softmax directly outside \\( q_i \\) and \\( k_j \\), which may be mathematically inconsistent.  \n2. It is unclear **when compression begins**—does it occur before reaching the cache budget, or only after exceeding it? If the method only keeps top-B tokens, then no compression happens before reaching the budget. The paper should clarify that the reported speedup comparisons are made **after the cache reaches the specified budget**.  \n3. Although the proposed approach achieves high compression rates, each compression step requires a **pre-computation of attention scores** to determine which tokens to keep, followed by another computation of the true attention scores for the retained tokens. This effectively adds an extra forward pass. The paper should report the **computational overhead ratio** introduced by this additional calculation.", "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)