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arxiv: 2606.26795 · v1 · pith:JWKYCI4Nnew · submitted 2026-06-25 · 💻 cs.CV · cs.AI· cs.MM

NaviCache: Test-Time Self-Calibration Caching for Video Generation

Zheqi Lv , Zhibo Zhu , Jinke Wang , Qi Tian , Shengyu Zhang , Zhengyu Chen , Chengxi Zang , Zhou Zhao
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Fei Wu
This is my paper

Pith reviewed 2026-06-26 05:51 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.MM
keywords video diffusion modelstest-time self-calibrationcomputation skippinginertial navigation systemcaching accelerationerror-bounded inferencedual-state estimation
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The pith

NaviCache treats diffusion feature evolution as an inertial navigation problem to enable accurate test-time computation skipping in video generation models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Video diffusion models incur high costs that offline calibration methods cannot reliably address due to data needs and distribution shifts. NaviCache instead performs self-calibration at test time by modeling input-output variation coupling through a dual-state inertial system that tracks both feature change ratios and latent drift. A specialized initial alignment plus time-dependent noise scheduling and uncertainty-aware updates then decide when to skip steps while bounding error. This approach directly targets the non-stationary behavior ignored by simpler zero-order approximations. If correct, it yields plug-and-play acceleration that adapts without pre-training or calibration data.

Core claim

By re-conceptualizing feature evolution in video diffusion models as an Inertial Navigation System problem, NaviCache uses a dual-state estimation architecture to adaptively track the feature change ratio and its latent drift, initialized through an Initial Alignment phase, and integrates a time-dependent noise schedule with an uncertainty-aware Measurement Update to provide a theoretically grounded mechanism for error-bounded computation skipping.

What carries the argument

Dual-state estimation architecture that tracks feature change ratio together with latent drift, using inertial navigation modeling of relative input-output coupling.

If this is right

  • Computation skipping becomes more accurate than zero-order approximations because momentum within the trajectory is explicitly tracked.
  • The method remains calibration-free and therefore avoids dependency on offline data or susceptibility to distribution shifts.
  • Error-bounded skipping is achieved through the combination of time-dependent noise scheduling and uncertainty-aware updates.
  • Performance gains appear across HunyuanVideo, Wan, and Open-Sora series without model-specific retraining.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same inertial framing could be tested on image or audio diffusion models to check whether the non-stationary tracking generalizes beyond video.
  • Energy and latency reductions in deployed generative systems would follow directly if skipping accuracy holds at scale.
  • A natural extension is to examine whether the dual-state estimates can also guide dynamic resolution or precision adjustments during generation.

Load-bearing premise

The relative coupling between input and output variations along diffusion trajectories can be captured accurately enough by an inertial navigation dual-state model to bridge domain gaps and non-stationary behavior.

What would settle it

A controlled test on any of the evaluated models where the dual-state tracker produces skipping decisions whose actual output error exceeds the claimed bound under distribution shift would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.26795 by Chengxi Zang, Fei Wu, Jinke Wang, Qi Tian, Shengyu Zhang, Zhengyu Chen, Zheqi Lv, Zhibo Zhu, Zhou Zhao.

Figure 1
Figure 1. Figure 1: Comparison of prediction accuracy for determining whether to skip the output computation at a denoising step in VDMs. We visualize the relationship between input and output differences as a 2D manifold. The shaded areas are constructed by the segments connecting predicted coordinates and ground-truth coordinates (Raw Points). Based on quantitative evaluation across all prompts in VBench (Huang et al., 2024… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the NaviCache Framework. NaviCache reformulates feature caching as a recursive state-space tracking problem to enable offline calibration-free acceleration for VDMs. Left: Comparison between raw trajectories (top) and NaviCache predicted trajectories (bottom), categorized into the Initial Alignment Stage and the Self-Calibration Stage. Right: Detailed modular workflow. The former stage is dedic… view at source ↗
Figure 3
Figure 3. Figure 3: Visualization and Case Study of video generation. For the prompt “A young man sitting at a desk in a library reading”, compared to existing methods that suffer from blurriness and structural artifacts (highlighted in red), our NaviCache preserves high visual fidelity and motion continuity, closely matching the unaccelerated baseline. representative VDMs: Wan 2.1, HunyuanVideo, and Open￾Sora 1.2. To accommo… view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of skip frequency across timesteps based on Wan 2.1. The distribution illustrates how NaviCache adaptively allocates computation for individual samples. temporal lengths of 51 and 102 frames using Open-Sora 1.2. The results demonstrate that NaviCache maintains highly stable acceleration performance, consistently achieving a speedup between 1.81× and 1.91×. This confirms our test￾time calibration… view at source ↗
Figure 5
Figure 5. Figure 5: Inference latency under varying spatial and temporal configurations. Evaluations are conducted on 40 VBench prompts, with “p” and “f” denoting pixels and frames, respectively. Time Overhead and Calibration Cost. As detailed in Ta￾ble 4, we analyze the auxiliary time costs associated with different acceleration strategies. NaviCache requires zero offline calibration, fundamentally avoiding the risk of bi￾as… view at source ↗
Figure 6
Figure 6. Figure 6: Trajectory visualization across various diffusion models and schedulers. Evaluated under diverse spatial-temporal configurations and ODE solvers to validate the consistent momentum of feature evolution. Solid lines represent the mean paths, while the shaded areas denote marginal variations. A.3. Supplementary Visualization and Case Study HunyuanVideo + TeaCache + MagCache + EasyCache + NaviCache [PITH_FUL… view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative comparison on HunyuanVideo using the prompt: “a book.” TeaCache exhibits severe semantic misalignment, failing to adhere to the prompt by generating an unrelated subject (a dog). Furthermore, MagCache struggles with fine-grained morphological details, showing less distinct hand contours and pronounced ghosting artifacts during the page-turning motion compared to our method. Best viewed with zoo… view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative comparison on HunyuanVideo using the prompt: “An astronaut flying in space, in cyberpunk style.” Notably, the outputs from MagCache and TeaCache exhibit visible ghosting artifacts and temporal inconsistencies around the astronaut’s legs, whereas our method maintains clear structural integrity during motion. Best viewed with zoom-in. generates high-fidelity videos with sharp details and precise … view at source ↗
read the original abstract

Video Diffusion Models (VDMs) is constrained by immense computational costs. While offline calibration-based acceleration suffers from calibration data dependency, prohibitive calibration duration, and susceptibility to distribution shifts, offline calibration-free methods eliminate these hurdles. However, since they rely on instantaneous zero-order approximations where the mapping between input and output differences varies in real-time, they are susceptible to observational noise and ignore the intrinsic momentum within the diffusion trajectory. In this paper, we propose NaviCache, a plug-and-play test-time self-calibration method re-conceptualizing feature evolution as an Inertial Navigation System (INS) problem. NaviCache bridges the fundamental domain gap and the non-stationary nature of diffusion by modeling the relative coupling between input and output variations. We introduce a dual-state estimation architecture that adaptively tracks the feature change ratio and its latent drift, initialized via a specialized Initial Alignment phase. By integrating a time-dependent noise schedule with an uncertainty-aware Measurement Update mechanism, NaviCache provides a theoretically grounded mechanism for error-bounded computation skipping. Extensive experiments on the HunyuanVideo, Wan, and Open-Sora series demonstrate that NaviCache exhibits more accurate error judgment for computation skipping and achieves outstanding comprehensive performance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes NaviCache, a plug-and-play test-time self-calibration caching method for Video Diffusion Models (VDMs). It re-conceptualizes feature evolution during diffusion as an Inertial Navigation System (INS) problem, introducing a dual-state estimation architecture that tracks feature change ratio and latent drift (initialized via Initial Alignment), combined with a time-dependent noise schedule and uncertainty-aware Measurement Update to enable error-bounded computation skipping. Experiments on HunyuanVideo, Wan, and Open-Sora series report improved accuracy in error judgment and overall performance compared to prior calibration-based and calibration-free methods.

Significance. If the INS dual-state model and Measurement Update deliver verifiable error bounds on skipping decisions, the approach would address key limitations of offline calibration (data dependency, distribution shift) and instantaneous zero-order approximations (noise sensitivity, ignored momentum) in VDM acceleration. The test-time, calibration-free design and explicit handling of non-stationary dynamics represent a potentially useful contribution to efficient video generation.

major comments (2)
  1. [Abstract and §3] Abstract and §3 (Method): The central claim that NaviCache 'provides a theoretically grounded mechanism for error-bounded computation skipping' requires explicit derivation of the error bounds. The manuscript must show the assumptions under which the dual-state estimation (feature change ratio + latent drift) plus time-dependent noise schedule and uncertainty-aware update yield provable bounds, rather than heuristic approximations; without these, it is impossible to verify internal consistency of the relative-coupling model or whether the INS analogy correctly captures non-stationary diffusion trajectories.
  2. [§3.2] §3.2 (Dual-State Estimation): The assumption that relative coupling between input and output variations can be captured by the INS dual-state model needs a concrete check against the non-stationary behavior of diffusion trajectories. The paper should demonstrate (via derivation or counter-example) that the model bridges the domain gap without reducing to fitted parameters or self-referential definitions.
minor comments (2)
  1. [§4] §4 (Experiments): Clarify the exact baselines, skipping thresholds, and quantitative metrics (e.g., FID, CLIP score, speedup) used for the HunyuanVideo, Wan, and Open-Sora comparisons; include ablation on the Initial Alignment phase and uncertainty-aware update.
  2. [Notation] Notation throughout: Define all symbols (e.g., states in the dual-state model, noise schedule parameters) at first use and ensure consistency between text and any equations or algorithms.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and detailed review. The comments highlight important aspects of the theoretical grounding in NaviCache. We address each major comment below and commit to revisions that strengthen the presentation without altering the core contributions.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (Method): The central claim that NaviCache 'provides a theoretically grounded mechanism for error-bounded computation skipping' requires explicit derivation of the error bounds. The manuscript must show the assumptions under which the dual-state estimation (feature change ratio + latent drift) plus time-dependent noise schedule and uncertainty-aware update yield provable bounds, rather than heuristic approximations; without these, it is impossible to verify internal consistency of the relative-coupling model or whether the INS analogy correctly captures non-stationary diffusion trajectories.

    Authors: We agree that the manuscript would benefit from an explicit derivation of the error bounds to make the theoretical grounding fully verifiable. The dual-state estimation draws from INS principles, with feature change ratio as the primary state (analogous to velocity) and latent drift as the secondary state (analogous to acceleration), updated via the uncertainty-aware Measurement Update. The time-dependent noise schedule modulates process noise to account for timestep-varying non-stationarity. In the revised manuscript, we will add a new subsection in §3 deriving the recursive error propagation under stated assumptions (bounded trajectory momentum and measurement noise covariance), showing how the relative-coupling model yields error bounds on skipping decisions. This will include the state transition and measurement equations to confirm internal consistency. revision: yes

  2. Referee: [§3.2] §3.2 (Dual-State Estimation): The assumption that relative coupling between input and output variations can be captured by the INS dual-state model needs a concrete check against the non-stationary behavior of diffusion trajectories. The paper should demonstrate (via derivation or counter-example) that the model bridges the domain gap without reducing to fitted parameters or self-referential definitions.

    Authors: The dual-state model is constructed from first principles of the INS framework rather than empirical fitting, with the Initial Alignment phase providing trajectory-specific initialization from observable feature statistics at early timesteps. The relative coupling is enforced through the joint state vector and the uncertainty-aware update, which uses real-time measurements to correct drift without self-reference. To directly address non-stationarity, the time-dependent noise schedule scales the process noise covariance proportionally to the diffusion timestep. In revision, we will expand §3.2 with a short derivation of the state transition matrix and include a brief analysis (with a simple counter-example trajectory) demonstrating that the model remains grounded in observable quantities and does not collapse to fitted parameters. revision: yes

Circularity Check

0 steps flagged

No circularity detected; derivation not exhibited for inspection

full rationale

The abstract claims a 'theoretically grounded mechanism for error-bounded computation skipping' via time-dependent noise schedule, INS dual-state estimation, and uncertainty-aware Measurement Update, but supplies no equations, no explicit derivation of error bounds, and no self-citations or fitted parameters that reduce to the target result by construction. Without load-bearing steps that can be quoted and shown to collapse (e.g., a prediction equivalent to a fit or a uniqueness theorem imported from the same authors), the derivation cannot be classified as circular. The modeling choice of re-conceptualizing diffusion as INS is presented as an analogy rather than a self-referential definition.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only abstract available; no free parameters, axioms, or invented entities can be identified from the provided text.

pith-pipeline@v0.9.1-grok · 5768 in / 1052 out tokens · 26376 ms · 2026-06-26T05:51:39.043838+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

29 extracted references · 6 canonical work pages · 3 internal anchors

  1. [1]

    Advances in neural information processing systems , volume=

    Denoising diffusion probabilistic models , author=. Advances in neural information processing systems , volume=

  2. [2]

    Proceedings of the IEEE/CVF international conference on computer vision , pages=

    Scalable diffusion models with transformers , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=

  3. [3]

    Kong, Weijie and Tian, Qi and Zhang, Zijian and others , journal=

  4. [4]

    arXiv preprint arXiv:2503.20314 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  5. [5]

    Zheng, Zangwei and Peng, Xiangyu and Yang, Tianji and Shen, Chenhui and Li, Shenggui and Liu, Hongxin and Zhou, Yukun and Li, Tianyi and You, Yang , journal=

  6. [6]

    Zheng, Zangwei and Peng, Xiangyu and Lou, Yuxuan and Shen, Chenhui and Young, Tom and Guo, Xinying and Wang, Binluo and Xu, Hang and Liu, Hongxin and Jiang, Mingyan and others , journal=

  7. [7]

    Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

    Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

  8. [8]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , year=

    DeepCache: Accelerating Diffusion Models for Free , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , year=

  9. [9]

    arXiv preprint arXiv:2412.18911 , year=

    Accelerating Diffusion Transformers with Dual Feature Caching , author=. arXiv preprint arXiv:2412.18911 , year=

    work page arXiv

  10. [10]

    arXiv preprint arXiv:2407.01425 , year=

    FORA: Fast-Forward Caching in Diffusion Transformer Acceleration , author=. arXiv preprint arXiv:2407.01425 , year=

    work page arXiv

  11. [11]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    On distillation of guided diffusion models , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  12. [12]

    Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

    Latent consistency models: Synthesizing high-resolution images with few-step inference , author=. arXiv preprint arXiv:2310.04378 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  13. [13]

    Advances in neural information processing systems , volume=

    Video diffusion models , author=. Advances in neural information processing systems , volume=

  14. [14]

    Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

    Stable video diffusion: Scaling latent video diffusion models to large datasets , author=. arXiv preprint arXiv:2311.15127 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  15. [15]

    International Conference on Learning Representations , year=

    Progressive distillation for fast sampling of diffusion models , author=. International Conference on Learning Representations , year=

  16. [16]

    Advances in Neural Information Processing Systems , volume=

    Q-dm: An efficient low-bit quantized diffusion model , author=. Advances in Neural Information Processing Systems , volume=

  17. [17]

    Advances in Neural Information Processing Systems , volume=

    Ptqd: Accurate post-training quantization for diffusion models , author=. Advances in Neural Information Processing Systems , volume=

  18. [18]

    Training-free and hardware-friendly acceleration for diffusion models via similarity-based token pruning , author=

  19. [19]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Attention-driven training-free efficiency enhancement of diffusion models , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  20. [20]

    International Conference on Learning Representations , volume=

    Real-time video generation with pyramid attention broadcast , author=. International Conference on Learning Representations , volume=

  21. [21]

    Advances in Neural Information Processing Systems , volume=

    Magcache: Fast video generation with magnitude-aware cache , author=. Advances in Neural Information Processing Systems , volume=

  22. [22]

    arXiv preprint arXiv:2507.02860 , year=

    Less is Enough: Training-Free Video Diffusion Acceleration via Runtime-Adaptive Caching , author=. arXiv preprint arXiv:2507.02860 , year=

    work page arXiv

  23. [23]

    Huang, Ziqi and He, Yinan and Yu, Jiashuo and Zhang, Fan and Si, Chenyang and Jiang, Yuming and Zhang, Yuanhan and Wu, Tianxing and Jin, Qingyang and Chanpaisit, Nattapol and others , booktitle=

  24. [24]

    Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

    The unreasonable effectiveness of deep features as a perceptual metric , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

  25. [25]

    OpenAI Blog , volume=

    Video generation models as world simulators , author=. OpenAI Blog , volume=

  26. [26]

    2024 , url=

    Kling: A Next-Generation Video Generation Model , author=. 2024 , url=

    2024

  27. [27]

    The Twelfth International Conference on Learning Representations , year=

    AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning , author=. The Twelfth International Conference on Learning Representations , year=

  28. [28]

    International Conference on Machine Learning , pages=

    Sparse Video-Gen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity , author=. International Conference on Machine Learning , pages=. 2025 , organization=

    2025

  29. [29]

    Advances in Neural Information Processing Systems , volume=

    InfinityStar: Unified Spacetime AutoRegressive Modeling for Visual Generation , author=. Advances in Neural Information Processing Systems , volume=