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REVIEW 4 major objections 6 minor 1 cited by

Brain-inspired Action Generation with Spiking Transformer Diffusion Policy Model

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Spiking transformer policy beats transformer diffusion baseline by 8%

desk verdict The architecture is a reasonable recombination, but the paper's own Table 1 contradicts the 'consistently outperforms' claim, and missing error bars and code keep it from being credible as written. read the letter →

arxiv 2411.09953 v2 pith:TJV6HDL6 submitted 2024-11-15 cs.RO

Qianhao Wang , Yinqian Sun , Enmeng Lu , Qian Zhang , Yi Zeng This is my paper · ORCID
classification cs.RO
keywords Brain-inspiredroboticsSpikingNeuralNetworksTransformerDiffusionPolicyModulateDecoderactiontrajectorygenerationrobotmanipulation
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

This paper tries to show that a spiking neural network can be combined with a diffusion-model policy to generate robot action trajectories, and that the right place to put the conditioning is in the decoder rather than the encoder. The proposed model, STMDP, uses a Spiking Transformer encoder and a new Spiking Modulate Decoder (SMD) whose cross-attention and feed-forward blocks receive the timestep and image conditions. Across four manipulation benchmarks, the authors report that STMDP beats the transformer-based diffusion policy DPT on the PushT, Can, and Square tasks, with an 8% gain on Can, while matching it on ToolHang. The authors also report that replacing the DDPM sampler with DDIM yields the best result on Square, hinting that faster sampling is compatible with the spiking architecture. If these results hold, they suggest spiking, brain-inspired networks are a viable backbone for robot action generation, not just for perception and classification.

What carries the argument

The load-bearing component is the Spiking Modulate Decoder (SMD), a replacement for the standard Transformer decoder in which spiking multi-head self-attention, spiking modulated cross-attention, and a modulated feed-forward network process the action-trajectory noise vector X under conditions from timestep embedding T and image embedding C. The SMD applies neural modulation to the cross-attention module in the decoder rather than to the encoder self-attention, and the paper argues that this placement is what drives the performance gain. Supporting machinery includes the Leaky Integrate-and-Fire neuron model with surrogate-gradient training, direct encoding across eight time steps with averaging decoding, and the DDPM/DDIM diffusion framework.

What would settle it

Re-running DPT, SDIT, and STMDP with identical seeds, hyperparameters, and the same checkpoints-averaging protocol on the Can task and finding that STMDP does not exceed DPT by the reported margin, or that the encoder-modulated SDIT performs as well as STMDP, would directly falsify the claim that decoder-side modulation drives the gain.

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Extended reading notes

Core claim

The central claim is that STMDP, built on spiking self-attention and a spiking modulate decoder, generates more accurate robot action trajectories than existing transformer-based diffusion policies. In the paper's experiments on PushT, Can, Square, and ToolHang, STMDP reaches success rates of 75.4%, 94.0%, 80.0%, and 54.0%, compared with 70.7%, 86.0%, 72.0%, and 54.0% for the DPT baseline; the Can gain is the headline 8% improvement. The ablation against SDPT (no modulate block) and SDIT (modulate block in the encoder) attributes the gain to placing modulation in the decoder, since STMDP matches or exceeds SDIT on all four tasks. The paper also explores DDIM in place of DDPM, reporting that the DDIM variant STMDP-I achieves the best Square result (88.0%) while underperforming on PushT and Can, which the authors attribute to unselected DDIM parameters.

Load-bearing premise

The load-bearing assumption is that every model in Table 1 was evaluated under identical, fairly tuned conditions, so the reported differences come from the architecture choices (spiking, modulation placement) rather than from hidden differences in checkpoints, hyperparameters, or environment initialization.

Editorial extensions

If this is right

  • If STMDP's results are reproducible, decoder-side modulation becomes a design choice worth adopting in future spiking or energy-efficient diffusion policies for robot manipulation.
  • The reported 94% Can success rate implies that spiking transformers can handle fine-grained grasping and placement, not merely coarse pushing tasks.
  • STMDP-I's best Square result suggests DDIMs can be compatible with spiking architectures, potentially enabling fewer denoising steps and faster action generation with further parameter tuning.
  • Surpassing DPT while using spiking neurons suggests that biologically plausible, event-driven computation does not necessarily cost accuracy on action-trajectory generation.

Reading between the lines

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

  • The paper does not test this, but an untested natural extension is that the SMD's decoder-side modulation would also improve non-spiking transformer diffusion policies, since the comparison in the paper only varies network type and modulation placement together; running DPT with an SMD placed in the decoder would isolate whether the gain is spiking-specific or placement-specific.
  • The poor SDIT result on Square (66%) relative to DPT (72%) hints that spiking encoders alone may lose fine-grained information, and that decoder-side modulation may compensate; a spiking encoder with a non-spiking decoder would test this.
  • If the energy-efficiency promise of SNNs is pursued, the 8% improvement on Can might translate into lower-energy policy execution on neuromorphic hardware, since the model's spike-based communication could run event-driven; the paper does not measure energy, so this remains speculative.
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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

4 major / 6 minor

Summary. The paper proposes STMDP, a spiking Transformer-based diffusion policy model for robotic action-trajectory generation, together with a Spiking Modulate Decoder (SMD) module that applies modulation in the decoder rather than in the encoder. The authors evaluate the model on PushT, Can, Square, and ToolHang, and compare it with ANN-based diffusion policies (DPT, DIT) and spiking variants (SDPT, SDIT). They report that STMDP consistently outperforms existing Transformer-based diffusion policies, with an 8% improvement on the Can task, and they also experiment with replacing DDPM with DDIM. The central claim, however, is contradicted by the paper's own Table 1, in which STMDP loses to DIT on ToolHang (0.540 vs. 0.780) and ties with DIT on Square (0.800 vs. 0.800). No error bars, standard deviations, significance tests, or code are provided, and the ablation baseline SDPT collapses to near-zero success on three of four tasks without explanation.

Significance. If the central claim were supported, the paper would make a useful contribution by combining spiking neural networks, the Transformer architecture, and diffusion policies in a single action-generation model, and by showing that decoder-side spiking modulation can improve trajectory generation. The exploration of DDIM in this setting is also potentially interesting for energy-efficient and faster inference. However, the significance is currently undermined by the mismatch between the claimed consistent outperformance and the reported results, by the absence of statistical controls, and by the lack of reproducible experiment details. The architectural idea is worth investigating, but the paper as written does not establish that the proposed SMD module is responsible for the observed numbers.

major comments (4)
  1. [Abstract and Section 4.2, Table 1] The claim that STMDP 'consistently outperforms existing Transformer-based diffusion policy method' is directly contradicted by the paper's own results. In Table 1, STMDP scores 0.754/0.940/0.800/0.540 on PushT/Can/Square/ToolHang, while the Transformer-based DIT baseline scores 0.657/0.920/0.800/0.780. Thus STMDP is worse than DIT by 24 percentage points on ToolHang and equal on Square. The abstract's emphasis on the Can task is also misleading: the 8% improvement over the 'current best Transformer-based Diffusion Policy model' is not achieved over DIT (0.920 vs. 0.940 is a 2% relative difference), so the baseline used for that claim must be stated unambiguously. This issue is load-bearing because it invalidates the headline contribution as written.
  2. [Section 4.1, experimental protocol] The comparison protocol is not sufficiently controlled. The only statement about comparability is that 'all other settings remain consistent with the previous configurations [6]', but DIT is adapted from an image-generation architecture [27], not from the robot-policy setup of [6]. The paper does not report architecture sizes, numbers of spiking time steps, training epochs, learning rates, observation/action horizons, environment initializations, checkpoint selection, or random seeds for any model. Without these details and without error bars or significance tests, the observed differences, including the 8% Can improvement, cannot be attributed to the proposed SMD module rather than to tuning or implementation choices.
  3. [Section 4.2, Table 2] The ablation study is not convincing as evidence for SMD's effectiveness. SDPT, the spiking Transformer without any modulation block, achieves 0.220 on Can, 0.040 on Square, and 0.000 on ToolHang, while both SDIT and STMDP reach much higher numbers. Such a collapse across multiple tasks suggests that the SDPT baseline may be undertrained, unstable, or improperly configured, rather than that the modulation block alone is responsible for the gains. The paper should report training curves, variance across runs, and hyperparameter settings for each ablation to rule out a broken baseline.
  4. [Section 4.2, Tables 1 and 2] No measure of variability is reported anywhere in the paper. The protocol in Section 4.1 describes averaging over 500 experiments, but the tables give only point estimates. Since the differences between STMDP and DIT are 2% on Can and 0% on Square, and the ToolHang comparison goes the opposite way, standard deviations or confidence intervals are essential to determine whether any claimed improvement is meaningful. The absence of these statistics makes the central comparison untestable.
minor comments (6)
  1. [Throughout] The manuscript contains numerous typographical errors that should be corrected, including 'foucus', 'dffusion', 'Brain-indpired', 'vers eion', 'eliminats', and 'Becaues'. These errors reduce readability.
  2. [Abstract and Conclusion] The phrase 'consistently outperforms' is too strong even relative to the paper's own data; a more cautious wording, such as 'performs competitively on some tasks', would better reflect Table 1.
  3. [Section 3.2, Equations (7) and (8)] The notation is inconsistent: Equation (7) defines M as the output of the encoder, but the text and Figure 1 do not clearly specify the exact inputs and shapes at each stage. The use of 'SM DM' in Equation (8) should be 'SMD' consistently.
  4. [Section 3.3 and Figures 1-2] The figures are dense and contain small, partly illegible labels. The relationship between the spiking self-attention, spiking cross-attention, and modulated feed-forward blocks should be described in the text more explicitly, with a clear statement of which tensors carry spike trains and which carry continuous values.
  5. [Section 4.1, DDIM settings] The statement that alpha is set to 0 to make the forward process deterministic is not explained. Since the paper also proposes STMDP-I, the DDIM parameter choices and their effect on the results should be described more carefully.
  6. [Section 5.1] The 'Data Available' section mentions that the model is part of BrainCog Embot, but no link, repository, or model artifact is provided. To support reproducibility, the authors should release code and trained checkpoints or at least include detailed configuration files.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an empirical benchmark comparison with external baselines and no derivation reduces to its own inputs.

full rationale

The paper's central claims are empirical: STMDP is compared against DPT, DIT, SDPT, SDIT, and STMDP-I on four manipulation tasks, with results reported in Table 1. The method section defines the model through Equations (7) and (8) as a standard noise-prediction diffusion loss, L = ||Y - SMDM(M,X)||^2, with no fitted parameter that is later renamed as a prediction, and no quantity is defined in terms of the outcome it is meant to predict. The ablations in Table 2 vary only the placement of the modulate block (encoder versus decoder), which is a structural comparison, not a circular reduction. The only self-reference is the Data Available statement that the model is part of the BrainCog Embot platform, cited as [30]; this statement is descriptive and does not carry any of the experimental or architectural conclusions. The paper also does not import a uniqueness theorem or an unverified prior result from the authors' own work; reference [27] (DiT) is an external baseline architecture. The inconsistency noted by the reader, in which STMDP loses to DIT on ToolHang according to Table 1, is a correctness and comparability concern about the headline claim, not a circularity concern, because no equation reduces a predicted success rate to an input constant. Accordingly, the derivation chain is self-contained with respect to circularity, and the score is 0.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

This is an empirical systems paper, so the ledger is dominated by tunable hyperparameters and unstated details of the training and evaluation setup rather than by axioms. The main assumptions are the standard DDPM, LIF, and surrogate-gradient machinery, plus the comparability of baseline results. No new physical entities are introduced.

free parameters (5)
  • Number of spiking time steps for direct encoding = 8
    Inputs are replicated over eight time steps and decoded by averaging (Section 3.2); no sensitivity analysis is given.
  • Surrogate gradient constant epsilon = 2
    Used in the surrogate function g(x) in Eq. (6); the value is stated without tuning.
  • DDIM parameter alpha = 0
    Set so the DDIM forward process is deterministic (Section 4.1); the authors later say this choice may be wrong, which affects the STMDP-I results.
  • DDIM inference timesteps = 50
    DDIM uses 50 inference steps versus 100 for DDPM (Section 4.1); the paper attributes inconsistencies in STMDP-I to this selection.
  • Architecture size (number of blocks, hidden dims) = not reported
    The number of encoder and SMD blocks and layer dimensions are not listed in the text, though results likely depend on them.
assumptions (5)
  • domain assumption LIF neuron discretization with surrogate gradient permits direct training of the spiking network.
    Invoked in Eqs. (2)-(6); the surrogate is an approximation with epsilon=2, and no analysis shows gradient quality is adequate for this task.
  • domain assumption The reverse diffusion process can be learned with a fixed variance prediction of beta I and an MSE noise loss.
    Standard DDPM simplification from [25], used in Eq. (8); the paper provides no evidence this assumption is harmless for action trajectories.
  • domain assumption Direct encoding by replicating real-valued inputs over 8 timesteps and averaging outputs preserves the information needed for manipulation.
    The encoding strategy is stated in Section 3.2 without ablation or theoretical justification.
  • domain assumption The benchmark evaluation protocol of [6], including checkpoint selection and environment initializations, transfers to the spiking models and baseline numbers.
    Section 4.1 says 'all other settings remain consistent with previous configurations [6]' but gives no concrete list.
  • domain assumption The four simulated tasks and success definitions are accepted as in the Diffusion Policy suite.
    Tasks described in Section 4.1; no modification or validation is discussed.

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Cite this review

Pith. "Pith review of Brain-inspired Action Generation with Spiking Transformer Diffusion Policy Model." pith.science (2026). https://pith.science/paper/TJV6HDL6

@misc{pith2026241109953,
  author       = {Pith},
  title        = {Pith review of: Brain-inspired Action Generation with Spiking Transformer Diffusion Policy Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TJV6HDL6}},
  note         = {Machine review of arXiv:2411.09953}
}
read the original abstract

Spiking Neural Networks (SNNs) has the ability to extract spatio-temporal features due to their spiking sequence. While previous research has primarily foucus on the classification of image and reinforcement learning. In our paper, we put forward novel diffusion policy model based on Spiking Transformer Neural Networks and Denoising Diffusion Probabilistic Model (DDPM): Spiking Transformer Modulate Diffusion Policy Model (STMDP), a new brain-inspired model for generating robot action trajectories. In order to improve the performance of this model, we develop a novel decoder module: Spiking Modulate De coder (SMD), which replaces the traditional Decoder module within the Transformer architecture. Additionally, we explored the substitution of DDPM with Denoising Diffusion Implicit Models (DDIM) in our frame work. We conducted experiments across four robotic manipulation tasks and performed ablation studies on the modulate block. Our model consistently outperforms existing Transformer-based diffusion policy method. Especially in Can task, we achieved an improvement of 8%. The proposed STMDP method integrates SNNs, dffusion model and Transformer architecture, which offers new perspectives and promising directions for exploration in brain-inspired robotics.

Figures

Figures reproduced from arXiv: 2411.09953 by the authors.

Figure 1
Figure 1. The architect of spiking transformer modulate diffusion policy module. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. The architect of base block.(a) represents the architecture of Spiking [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Tasks used for the experiment. 4.2 Experiment Results To ensure a comprehensive comparison, the experiments have implemented dif￾fusion policies based on spiking Transformer (SDPT), which utilizing the same Transformer architecture as the diffusion policy based on ANN described (DPT) in [6], and those based on spiking DIT (SDIT) structures as in [27]. The main difference between them is whether there is Spiking Tran… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: The results of the ablation study. 5 Conclusion In this work, we proposed the Spiking Transformer Modulate Diffusion Policy Model, a new diffusion policy architecture based on the Spiking Transformer. This model effectively integrates SNNs with diffusion policy’s capab…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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    Combining scale-shift conditioning with cross-attention in diffusion policies yields success-rate gains of 0 to 12 percentage points on simulated robot manipulation benchmarks, with the largest gain on Toolhang.

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