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MANTA: Diffusion Mamba for Efficient and Effective Stochastic Long-Term Dense Anticipation

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read MANTA replaces the previous best diffusion generator with a bidirectional Mamba stack that reaches a dense global temporal receptive field at linear cost, and reports state-of-the-art accuracy on Breakfast, 50Salads, and Assembly101 with…

desk verdict MANTA is a solid, efficient architectural win for stochastic dense anticipation, but the paper's story about long-range information flow is under-verified and deserves a look before you take the 'global receptive field' claim at face value. read the letter →

arxiv 2501.08837 v2 pith:23TPZBQ4 submitted 2025-01-15 cs.CV

Olga Zatsarynna , Emad Bahrami , Yazan Abu Farha , Gianpiero Francesca , Juergen Gall This is my paper · ORCID
classification cs.CV
keywords stochasticlong-termactionanticipationdiffusionmodelsMambastatespacedensetemporalmodelingefficiencyvideounderstanding
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Stochastic long-term dense action anticipation asks a model to generate multiple plausible action sequences covering several minutes of future video. The paper identifies the generator's temporal receptive field as the main bottleneck: the previous state of the art, GTDA, uses multi-stage dilated gated convolutions whose receptive field is sparse and becomes global only late. MANTA replaces that generator with a stack of Mamba-based Bidirectional Selective State-Space Layers, which keep a dense global receptive field with linear complexity in sequence length. The paper reports state-of-the-art accuracy on Breakfast, 50Salads, and Assembly101, while cutting inference time per video from 71.8 seconds to 1.1 seconds (65.3×) and training time per epoch from 379 seconds to 57 seconds. If correct, this makes long-horizon stochastic forecasting both more accurate and cheap enough for real-time use.

What carries the argument

The load-bearing component is the Bidirectional Selective State-Space Layer (BSSL), a Mamba-style block that processes the temporal sequence with two S6 scans, one forward and one backward, whose input-dependent matrices (B, C, and Δ) act as data-dependent gates. It carries the argument by replacing the dilated gated temporal convolutions of GTDA: it provides a dense global receptive field with linear cost, and its selectivity lets the model treat the observed and zero-masked halves of the input differently. The 15-block MANTA stack built from BSSL is the whole temporal engine; the diffusion process and DDIM sampling are inherited from GTDA unchanged.

What would settle it

Perturb a single observed frame in a trained MANTA and measure how far into the masked future its influence changes the output, across all 15 blocks and on the longest Assembly101 sequences; if the influence decays before the end of the anticipation horizon in early blocks, or if a model with the backward path removed matches the bidirectional model on the 25-minute videos, the dense-global-receptive-field explanation is wrong.

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Extended reading notes

Core claim

MANTA is a diffusion generator that predicts past and future one-hot action labels jointly, conditioned on observed visual features zero-padded over the future. Its core discovery is that the S6 selective state-space layer, run in both forward and backward temporal directions, gives every position a dense, locally aware global receptive field in a single stage, so the zero-padded future entries can draw on observed context from the very first block. This removes the need for GTDA's multi-stage dilated architecture: a single stack of 15 MANTA blocks outperforms GTDA on three datasets, with the largest gains on longer videos and longer anticipation horizons. The same blocks are also much cheaper: 2.8× fewer parameters, 65.3× faster inference for 25 stochastic samples, and 6.6× faster training.

Load-bearing premise

The S6 selective state-space layer's fixed-size hidden state can carry the observed frames' information across the whole masked future region, and the backward scan fills in what the causal forward scan drops; the evidence for this is the final accuracy and the bidirectionality ablation, not a direct measurement of the receptive field.

Editorial extensions

If this is right

  • If MANTA's claim holds, long-term stochastic anticipation no longer needs multi-stage dilated generators: a single Mamba stage matches or beats GTDA's accuracy on all three datasets.
  • The efficiency gain (65.3× inference, 6.6× training, 2.8× fewer parameters) makes generating 25 stochastic samples per observation practical on long videos, opening the door to real-time anticipation.
  • The reported performance gap over GTDA widens on longer videos (5.5% versus 3.8% Mean MoC improvement), implying that long-horizon forecasting is exactly where dense global receptive fields help most.
  • Removing either the backward scan or the S6 selectivity degrades accuracy substantially (Top-1 MoC drops from 55.5 to 35.8 without the backward path at α=0.2, β=0.1), so both design choices are load-bearing for the reported results.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural extension the paper does not test is to swap the BSSL generator into other masked-fill dense prediction tasks, such as future motion or audio forecasting, where the same observed-context-to-masked-region propagation problem appears.
  • The paper attributes the gain to dense global receptive field but does not measure effective receptive field directly; a head-to-head against a bidirectional linear-attention generator with comparable gating would isolate whether the mechanism is the receptive field or the particular SSM dynamics.
  • Assembly101 contains 25-minute videos, yet the duration-based analysis is only reported on Breakfast; re-running the video-length grouping on Assembly101 would provide the strongest test of the long-range modelling claim.
  • Because 50Salads converges with only 10 DDIM steps while Breakfast and Assembly101 use 50, the interaction between generator capacity and number of sampling steps is unexplored and could yield further speedups.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes MANTA, a diffusion-based stochastic long-term dense action anticipation model that replaces GTDA's multi-stage gated temporal convolutional generator with a single-stage stack of 15 bidirectional Mamba (S6) blocks. The core idea is that the selective state-space layer provides a global receptive field with linear complexity, allowing observed visual features to propagate into the zero-masked future region and improving both accuracy and efficiency. Experiments are conducted on Breakfast, 50Salads, and Assembly101 using the standard alpha/beta protocol, with Mean and Top-1 MoC metrics. The authors report state-of-the-art accuracy on the three datasets, with the exception of Assembly101 Top-1 at beta=0.1, and large speedups over GTDA (65.3x inference, 6.6x training). Ablations study the contributions of selectivity, bidirectionality, block structure, and block count, and supplementary material provides additional ablations and standard deviations for Breakfast.

Significance. MANTA is a well-executed, incremental improvement over the same authors' GTDA. Its strengths include reproducible code, ablations that isolate key design choices, and substantial efficiency gains (65.3x inference, 6.6x training, 2.8x fewer parameters). If the reported gains are robust, the model provides a more practical stochastic anticipation method. However, the claimed mechanism for long-range observed-to-future information flow is not directly measured, and the empirical gains on Assembly101 are small; the central evidence for the 'very long sequence' claim is therefore incomplete.

major comments (3)
  1. [Sec. 4.3, Table 1] The paper reports no standard deviations or significance tests for Assembly101 and 50Salads. On Assembly101 the Mean MoC improvements over GTDA are small (e.g., 6.7 vs 6.4 at alpha=0.2, beta=0.1; 5.3 vs 4.5 at beta=0.2) and Top-1 MoC is actually worse at beta=0.1 (16.9 vs 18.0). Without variance estimates, it is unclear whether the claimed state-of-the-art results on these two datasets are statistically meaningful. Please add error bars or significance testing for the main comparisons.
  2. [Sec. 3.2.2, Eqs. (11)-(14) and Table 3] The bidirectional layer's backward scan processes the reversed sequence, so for a future position j it only sees positions j, j+1, ..., P+F (after reversing), never the observed block at positions 1,...,P. Only the forward scan can propagate observed information into the masked future region. The bidirectionality ablation in Table 3 therefore demonstrates the value of future context, not the long-range observed-to-future flow that motivates the architecture. Please add an experiment that directly measures information flow from observed inputs to future predictions, such as perturbing observed positions and measuring the change in future outputs at varying distances, or report performance on Assembly101 broken down by anticipation horizon. This would substantiate the 'global receptive field' and 'early propagation' claims.
  3. [Sec. 4.3, Fig. 5] The only duration-based analysis is on Breakfast, whose sequences max out at 10.8 minutes (5.4 minutes of anticipation at beta=0.5), and Fig. 5 aggregates over all future positions. Assembly101, with anticipation horizons up to 12.5 minutes, is not analyzed as a function of horizon or video length. Without such analysis, the claim of effectiveness on 'very long sequences' is not directly supported by the experiments.
minor comments (4)
  1. [Abstract and Conclusion] The statement 'achieves state-of-the-art results on three datasets' is an overstatement given the Assembly101 Top-1 MoC result at beta=0.1 (16.9 vs 18.0 for GTDA); please qualify the claim to reflect the exception noted in Section 4.3.
  2. [Eq. (9)] The formula for bar-B is ambiguous as printed: 'exp(delta A)-1(exp(delta A) - I) delta B' could be misread. Please write the intended expression, e.g., (e^{delta A} - I)(delta A)^{-1} delta B, or (e^{delta A} - I) A^{-1} B, to avoid confusion.
  3. [Supplementary Tab. 10] In the row for 25 samples, the Mean MoC value at beta=0.1 appears to be a typo: 25.5 should likely be 27.7 (compare with Table 1 for the same configuration).
  4. [Fig. 5] Consider labeling the number of videos in each duration group or adding error bars, since group sizes likely vary and the aggregated mean could be dominated by a few long videos.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: MANTA's central claims are empirical results on held-out benchmark splits, not reductions of the method's inputs.

full rationale

The paper's central claim is that replacing GTDA's dilated-convolution generator with a Mamba-based bidirectional selective state-space generator improves stochastic long-term dense anticipation. This claim is evaluated on held-out validation/test splits of the public Breakfast, 50Salads, and Assembly101 datasets under the standard MoC protocol, so no fitted parameter is renamed as a prediction. The training objective in Eq. (16) is a standard L2 diffusion-reconstruction loss against ground-truth one-hot action sequences, and the conditioning vector in Eq. (3) is constructed from observed visual features and zeros, not from the model's own outputs. The diffusion formulation is adopted from the authors' prior GTDA work, but that adoption is an explicit baseline extension rather than a load-bearing citation used to justify the new result; GTDA is also the comparison baseline, and its correctness is not assumed to establish MANTA's superiority. The Mamba/S6 and bidirectional scanning components are inherited from external public work and are tested through ablations (Tables 2 and 3), not imported as an unexamined uniqueness theorem. Claims about long-range temporal modelling are empirical and could be challenged by direct information-flow measurements, but that is a correctness-risk concern, not circularity. No equation, prediction, or benchmark result reduces to the method's own inputs by construction.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

This is an empirical architecture paper. The ledger records hand-chosen hyperparameters and background assumptions inherited from prior work (Mamba, GTDA diffusion formulation, benchmark protocol). No new theoretical entities are introduced.

free parameters (4)
  • Number of MANTA blocks (B) = 15
    Chosen via ablation on Breakfast (Table 9 in supplement). The SOTA results depend on this architectural hyperparameter.
  • Number of DDIM inference steps (D) = 50 for Breakfast/Assembly101, 10 for 50Salads
    Inherited from GTDA [57]; shorter inference for 50Salads. Affects the accuracy and speed claims.
  • Number of diffusion training steps (T) = 1000
    Standard DDPM setting from [20] and [57], not tuned in this paper.
  • Number of evaluation samples (S) = 25
    Fixed evaluation protocol from [7]; used by all compared methods.
assumptions (4)
  • domain assumption The Mamba S6 layer provides a global receptive field with linear complexity in sequence length.
    Invoked in Sec. 3.2.2 to justify early propagation of observed information into masked frames. The effective receptive field is not directly measured.
  • domain assumption Zero-padding the future visual features in the conditioning vector X is a valid way to represent unobserved frames, and the network can learn to ignore the zero entries.
    Inherited from GTDA [57], used in Eq. (3). If the model treated zeros as informative features, the conditioning would be incorrect.
  • domain assumption The L2 reconstruction loss on one-hot encoded action sequences is appropriate for training the diffusion generator.
    Used in Eq. (16) following [57] and [20]. The paper does not test alternative losses such as cross-entropy or soft targets.
  • domain assumption Pre-extracted I3D and TSM features and the MoC protocol with S=25 samples provide a valid measure of anticipation quality.
    Adopted in Sec. 4.2 from [7,44,57]. The paper does not re-verify the features or the metric.

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Cite this review

Pith. "Pith review of MANTA: Diffusion Mamba for Efficient and Effective Stochastic Long-Term Dense Anticipation." pith.science (2026). https://pith.science/paper/23TPZBQ4

@misc{pith2026250108837,
  author       = {Pith},
  title        = {Pith review of: MANTA: Diffusion Mamba for Efficient and Effective Stochastic Long-Term Dense Anticipation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/23TPZBQ4}},
  note         = {Machine review of arXiv:2501.08837}
}
read the original abstract

Long-term dense action anticipation is very challenging since it requires predicting actions and their durations several minutes into the future based on provided video observations. To model the uncertainty of future outcomes, stochastic models predict several potential future action sequences for the same observation. Recent work has further proposed to incorporate uncertainty modelling for observed frames by simultaneously predicting per-frame past and future actions in a unified manner. While such joint modelling of actions is beneficial, it requires long-range temporal capabilities to connect events across distant past and future time points. However, the previous work struggles to achieve such a long-range understanding due to its limited and/or sparse receptive field. To alleviate this issue, we propose a novel MANTA (MAmba for ANTicipation) network. Our model enables effective long-term temporal modelling even for very long sequences while maintaining linear complexity in sequence length. We demonstrate that our approach achieves state-of-the-art results on three datasets - Breakfast, 50Salads, and Assembly101 - while also significantly improving computational and memory efficiency. Our code is available at https://github.com/olga-zats/DIFF_MANTA .

Figures

Figures reproduced from arXiv: 2501.08837 by the authors.

Figure 1
Figure 1. We propose a novel MANTA diffusion generator that [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. (Top) Overview of the proposed MANTA model. Given a noise vector Yt and a conditioning vector X , constructed by extending the features of the P observed frames with zero padding in place of the F future frames, we concatenate and forward them through our proposed MANTA model. As output, MANTA predicts action classes for both observed and future frames. (Bottom) Illustration of the structure of the (a) MANTA block, … view at source ↗
Figure 3
Figure 3. Qualitative comparison of MANTA and GTDA on Breakfast. Best viewed zoomed in. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: (Left) Mean time for generating 25 samples measured for different models on Breakfast, with all models performing 50 inference diffusion steps; (Right) Mean time required for training different models for one epoch on Breakfast. (Both) The batch size is equal across mo…
Figure 5
Figure 5. Figure 5: Comparison of Average Mean MoC accuracy for differ [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Qualitative comparison of MANTA (top) to GTDA [57] (bottom) on the Breakfast dataset. Best viewed zoomed in [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Qualitative comparison of MANTA (top) to GTDA [57] (bottom) on the Breakfast dataset. Best viewed zoomed in [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Qualitative comparison of MANTA (top) to GTDA [57] (bottom) on the Breakfast dataset. Best viewed zoomed in [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: Qualitative comparison of MANTA (top) to GTDA [57] (bottom) on the 50Salads dataset. Best viewed zoomed in [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: Qualitative comparison of MANTA (top) to GTDA [57] (bottom) on the Assembly101 dataset. Best viewed zoomed in [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]

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Forward citations

Cited by 1 Pith paper

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.