ICDiffAD: Implicit Conditioning Diffusion Model for Time Series Anomaly Detection
Authors: Fan Zhang
Sinchee Chin
Jing-Hao Xue
Wenming Yang
Venue: ICLR 2026 Poster (2026)
Keywords: Time Series
Anomaly Detection
Diffusion Model
Implicit Conditioning
Primary area: learning on time series and dynamical systems
TL;DR: We propose a fix to current diffusion models in time series anomaly detection, guided by Signal to Noise Ratio both in training and inference, improving current Diffusion Models by 20.2% F1 Scores.

Abstract: Time series anomaly detection (TSAD) faces critical challenges from intrinsic data noisiness and temporal heterogeneity, which undermine the reconstruction fidelity of prevailing generative approaches. 
While diffusion models offer theoretical advantages in capturing complex temporal dynamics, their inherent stochasticity introduces irreducible variance in reconstructions.
We present the ICDiffAD, a novel method that synergizes adaptive noise scheduling with semi-deterministic generation to address these limitations. ICDiffAD introduces two key innovations: 
(1) an *SNR Scheduler* that governs training through quantifiable noise scales, enabling robust learning of normative patterns across non-stationary regimes; and 
(2) an *SNR Implicit Conditioning Mechanism* that initializes reverse diffusion from partially corrupted inputs, preserving signal coherence while attenuating anomalous components. 
This dual strategy ensures high-fidelity reconstructions aligned with the input’s manifold, reconciling generative flexibility with detection accuracy. 
Across five multivariate benchmarks, ICDiffAD improves the F1 score by 19.57\% and reduces false positives by 60.23\% compared to existing diffusion model-based TSAD methods.

Published as a conference paper at ICLR 2026
ICDIFFAD: IMPLICIT CONDITIONING DIFFUSION MODEL FOR TIME SERIES ANOMALY DETECTION
Fan Zhang', Sinchee Chin', Jing-Hav Xue?, Wenming Yang'!
1 Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
2 Department of Statistical Science, University College London, London, United Kingdom f23, chenxz22) @mai 1s ts negua www.nnggh.u....ooeeemm en, jinghao. xue@ucl. ac uk, yang. wenmi wenmingêsze sz ts tsinghua .ed.ccn
ABSTRACT
Time series anomaly detection (TSAD) faces critical challenges from intrinsic data noisiness and temporal heterogeneity, which undermine the reconstruction fidelity prevailing generative approaches. While diffusion models offer theoretical advantages capturing complex temporal dynamies, their inherent stochasticity introduces irreducible variance in reconstructions, We present ICDiffAD,, a novel method that synergizes adaptive noise scheduling with input-consistent generation to address these limitations. ICDiffAD introduces two key innovations: (1) an SNR Scheduler that governs training through quantifiable noise scales, enabling robust learning of normative patterns across non -oa-stationary regimes; and (2) an SNR Implicit Conditioning Mechanism that initializes reverse diffusion from partially corrupted inputs, preserving signal coherence while attenuating anomalous components. ICDiffAD ensures high-fidelity reconstructions aligned with the input's 's manifold, reconciling generative flexibility with detection accuracy. Across five multivariate benchmarks, ICDiffAD improves the F1 score by 19.57% and reduces false positives by 60.23% compared existing diffusion model-based TSAD methods.
INTRODUCTION
Time series anomaly detection (TSAD) a key component many essential applications, such as maintenance and surveillance (Dissanayake et al. 2023] Nguyen-Do Da et al., 2024). However, TSAD faced with two challenges: (i) intrinsic noisiness of temporal data streams, where anomalous signals are obfuscated by stochastic fluctuations, sensor artefacts, and measurement inaccuracies; and (ii) temporal heterogeneity of real-world time series, characterised by -tatioationarttt and regime shifts, which obstruct the learning of robust latent patterns (Liu et al. 2021 Wang and Yin 2024).
Among prevailing approaches, reconstruction- based methods operationalizing anomaly detection through normative dynamics approximation dominate contemporary TSAD research. These methods leverage deep learning models such as autoencoders, variational autoencoders, and Transformer- based sequence models learn the underlying premise posits of the anomaly-free training samples. However, the practical efficacy of these approaches is severely impeded by the inherent noise and high temporal heterogeneity of time series data. The presence of random background noise in the training data compromises the model's 's capacity to accurately capture the underlying dynamics of normal system behavior, resulting dependencies (Hundman et al. 2018 Saravanan et al.) 2023). Simultancously, the high temporal heterogeneity inherent to real- world time series manifests as non-stationary distributions and regime shifts, destabilizing training dynamics (Zhan et al. 2024). These two limitations fundamentally constrain the reconstruction fidelity, thereby increasing false positives in TSAD.
model memorizing noise artifacts rather than learning causal temporal
Recently, researchers have focused on the use of generative models such as GANs and diffusion models for TSAD, motivated by their ability to asymptotically approximate complex data mani- folds through iterative denoising (Goodfellow et al. 2020; Ho et al. 2020).. Unlike deterministic
Corresponding author.

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Published as a conference paper at ICLR 2026
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Figure 1: Diffusion dynamics for Sinusoidal waveforms. (Top) Sine wave. (Middle) Cosine wave. (Bottom) Sine wave with injected anomaly. The forward diffusion process progressively attenuates anomalous signals; the reverse process reconstructs the original time series. Performance metrics (SNR and Jensen-Shannon Divergence (JSD)) are measured relative to step O. At SNR = -4 dB, the original signal's trends are fully attenuated, leading to stochastic reconstruction (e.g., sine waves reconstructed as cosine waves), degrading TSAD performance. In contrast, at an optimized denoising step (SNR 10.5 dB), anomalous signals suppressed while preserving the underlying trend, enabling faithful reconstruction and improved TSAD accuracy.
autoencoders, diffusion models learn to progressively refine a noise-corrupted sample toward the data distribution, theoretically circumventing the over-constrained reconstruction inherent to traditional methods. However, a critical issue remains under-explored: generative models inherently generate diverse plausible reconstructions from Gaussian noise (e.g., as shown in Figure reconstructing sine waves as cosine waves at low Signal-to-Nor Ratio (SNR)), contradicting reconstruction- TSAD's need for input-consistent reconstructions and degrading TSAD performance. This stochas- ticity significantly increases false positive rates, especially in high-variance regions where multiple reconstructions may seem equally likely. Although some studies attempt to mitigate this by introduc- ing self-conditioning partial interpolation, they risk conditioning anomalous points (Xiao et al., Chen et al., 2023), which undermines temporal consistency and leads to biased reconstructions where local anomalies may be mistakenly preserved.
2023
These limitations motivate our investigation on implicit-conditioning for input-consistent generation to revamp TSAD. Hence in this paper we propose ICDifFAD, new and effective TSAD paradigm equipped with two exciting innovations: (1) SNR Scheduler, a reparameterized training-inference scheme that unifies noise scales under a quantifiable SNR spectrum. Unlike conventional fixed or linear schedules, this new scheduler induces the model to learn normal patterns across diverse. SNR- calibrated corruption levels, enhancing robustness noise perturbations and temporal heterogeneity (33.2): and (2) SNR Implicit Conditioning (SIC) Mechanism, a corruption estimator that jointly predicts (i) the optimal corruption factor 5 and (ii) the denoising step I for given input. By dynamically aligning the reverse process with the input's intrinsic noise scale, SIC ensures that reconstructions balance faithfulness (avoiding unrealistic outputs) and fidelity (preserving non- anomalous trends), resolving the mallism-fielite trade-off inherent to vanilla diffusion in TSAD.
Notably, ICDifIAD's sss design explicitly addresses the TSAD-specific need for reconstruction deter- minism: by conditioning generation on SNR-inferred latent states, we suppress stochastic deviations while attenuating anomalies. As shown in $5 ICDiffAD provides significant improvement over previous diffusion models (+20.2% average F1 score) and reconstruction- based methods (+5.1% % average Fl score) in TSAD. In short, our contributions threefold.
We propose SNR Scheduler that dynamically modulates noise levels during training, enabling the model to adaptively learn robust reconstructions across varying corruption intensities in noisy, heterogeneous time series.
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We introduce SNR Implicit Conditioning Mechanism that adaptively estimates the optimal noise level and denoising steps per input, guiding the reverse process from partially corrupted inputs to achieve faithful and stable reconstructions for accurate TSAD.
We rectify the limitations of diffusion models in TSAD concerning stochastic generation and substitute three associated, yet separately configured hyperparameters with parameters associated with SNR, thereby presenting a more intuitive and efficient framework for iillusion-based TSAD.
RELATED WORK
TSAD pivotal in variety of domains such as finance, loT, and cybersecurity. Classical approaches
rely statistical (Li et al. 2022), decomposition (Schölkopf al., 2001 Liu et al., 2008). distance- based techniques (He et al. 2003) to identify deviations but struggle with temporally heterogeneous data. Modern deep learning approaches to temporal modeling largely fall into two categories. (i) Representation-bu based methods learn a manifold of normal data and detect anomalies through measures such statistical deviations (Ruff al. 2018 Yang al. 2023). These methods assume normal data lies in a compact latent region, an assumption frequently violated in practice due to complex, non-stationary time series, resulting in performance degradation. (ii) Reconstruction soboostructton-baeee methods learn to reconstruct normal training samples, treating reconstruction error as an anomaly score (Wu al. 2022; Fan et al., 2023 Xu et al. 2022, Wang et al., [023] Du al. 2021 Dai al.) 2024). However, they frequently suffer from training instability (e.g., in GANs) or error propagation (e.g.. in autoregressive Transformers). Diffusion-basee methods have recently emerged promising
solution to these issues.
DiffusionAE Pintilic al. (2023) performs diffusion- based reconstruction on top of the autoencoder's outputs. DiffADT et al. (2024) employs state-space model as the denoising network to capture long-term dependencies. MODEM /Zong et al. (2025) incorporates multi-resolution contextual information into coarse-to- fineine diffusion process to refine diffusion trajectories. Imputation- based Xiao et al. )023); Chen et al. (2023); Cao et al. (2024) frameworks adopt a mask-then-impun paradigm, treating part of the input as observable conditioning sequence and using diffusion model to fill in the masked segments for reconstruction. However, these approaches suffer from a critical limitation: they fail to achieve input-consistent reconstruction.
In this work, we adaptively control noise injection via an SNR-aware schedule to preserve critical information, while SIC mechanism determines optimal noise levels and denoising steps per input, guiding input-consistent reconstruction of sequences. This jointly mitigates the effects of stochasticity on anomaly detection.
PRELIMINARIES
3.1 DENOISING DIFFUSION PROBABILISTIC MODELS (DDPM)
DDPM (Ho et al. 2020) comprises a forward phase that incrementally injects Gaussian noise into input data and reverse phase that reconstructs samples by iteratively denoising the perturbed representations. Specifically, let X ~ ((x),, the data distribution of interest. The forward process gradually perturbs Xo over T timesteps by injecting noise scaled by schedule {8e)T-1 where 0 < Br 1, defining a Markov chain: with g(x,/xxxx) = N (xi: /1 3ix 1,
((X1:1(xo)
II t=1
(1)
By recursively applying the reparameterization trick, the state step can be expressed in closed form as
X; = Vāxxa + v1 ārt; < N( I),
where ã; II 3.). When t + T, a, 0, and Xy converges to isotropic Gaussian noise.
The reverse process learns to invert this corruption by training a neural network μ4 (x,, t) to ap- proximate the posterior ((x)-1[x,), Starting from Xy -N(0, I), the model reconstructs Xo via the
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(4)
SNR Schoduder
Impur
Impicit-Guidee
(P)
Oener
Cooo Aoppe
Denolsing Ulleet
Figure 2: Diagram of ICDifIAD. During inference, the input instance is processed by (b) the SIC Module ($4.)) which dynamically estimates (1) the corruption factor ai. and (2) the denoising timestep T. guided by (a) the SNR Scheduler 4.44. These parameters drive the denoising of implicitly corrupted inputs, enabling input-consisten Implicit-Guided Generation to reconstruct the target signal. As illustrated the SNR Scheduler visualization, anomalies in one channel are entirely suppressed near ISNRas value of dB, estimated from Eq. 12
learned transitions:
Po Po(x1-1 N (o
where of is often fixed to 3e or 1- Be for analytical tractability. The joint distribution is thus I-ā,
po(Xo:T) = ((xx)IIf__10/ (xt- (x |x.). The model is trained by minimizing the variational lower bound
(VLB) of the negative log-likelihoon which simplifies to a weighted sum of E.,, |||e 6o <0 [ when predicting noise residuals 6.
3.2 DIFFUSION MODELS FOR TSAD
Most existing diffusion based time series anomaly detection methods employ conditional diffusion models. These approaches partition data into observed x and masked sequences x introduc- ing auxiliary information x during training to guide the model in learning to reconstruct the masked sequences. Formally, the goal these methods is to estimate the true conditional data distribution q(xx)" (x)))) using the model distribution pa (xb"" (x)",,, where the subscript of denotes the diffusson
timestamp with 0 indicating the original data. Let us consider the following distribution (Cao et al.
2024
Tashiro et al. 2021):
Ear Po
x
(xT II po (x(_ir
x xi
con)
tar ~ N)..I)..
(4)
Correspondingly. the denoising process at step i: is given by
(x
x xx" N (xx (xX" &s xx"") og (x xx
(5)
Although this approach introduces partial original information and provides some guidance for input- consistent reconstruction, increases the risk of incorporating anomalous segments conditional inputs, which may lead to continuous accumulation erroneous information during the reconstruction process.
4 METHODOLOGY
The input to TSAD can be represented as matrix X E RRLKK, where I. denotes the length of the timestamps and K is the number of variables. The objective to produce an output vector y E R4 where yı E {0, 1} indicates whether the observation at the i-th timestamp is anomalous.
4.1
OVERVIEW
The ICDiffAD schematized in Figure 2 It incorporates two core innovations that jointly enable input-consistent reconstruction, property essential for reconstruction-base. TSAD methods.
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First, propose reparameteriize the noise scheduler with SNR. For diffusion processes time series, SNR provides a measurable and intuitive metric of signal energy retention. Our SNR scheduler redefines the diffusion noise curriculum by strategically modulating the noise injection rate as a function of SNR, ensuring consistent signal energy retention across datasets with different characteristics. This SNR-calibrated adaptive noise scheduling enhances the model's reconstruction capability under varying degrees of signal energy retention.
critical parameters: the corruption factor aij. which quantifies the optimal noise intensity for a given input; and the denoising step ń - T, which identifies the optimal reverse denoising trajectory. The implicit conditioning controlled by these two key parameters enables the diffusion model to achieve input-consistent generation. Formally, suppose Z, is the controlled corrupted input at time I, the SNR-guided generation is defined as Zo (o() II pa (Z.)
Second, our SIC Mechanism estimates
The final anomaly detection computes the L2 norm, s 12 Zol12. between the original time series Zo and its implicitly guided reconstruction Zo generated through the learned diffusion process. This deviation metric serves the anomaly score, with larger values indicating higher likelihood of temporal anomalies. The formal definition of Zo will be elaborated $4.2
4.2 DATA PREPROCESSING
To capture inter-variate dependencies while preserving temporal coherence, we adopt the dimensional transformation from DiffAD (Xiao et al. 2023), reformulating the multivariate time series X E R 1xx K 2D matrix Z R Lxc through channel-wise concatenation:
Z = + (+) E LRLK 1=1
where + denotes concatenation along the feature dimension. This transformation enables the diffu- sion model learn cross-vare variate correlations through convolutional filters. The detailed algorithm of this transformation is shown in Appendix 8.2
4.3 SNR SCHEDULER
Traditional noise scheduler is parameterized by Pmins Smax. and T, which independently defines different but related physical dynamics. For example, larger T and smaller 3max Smin result in finer denoising process. However, larger 7 might result in a longer inference time, and smaller 0max Amin might cause the corruption process be insufficient for training a diffusion model.
Hence, we reparameterize these two parameters with SNR, an intuitive, simple, and relatable metric for time series data. Let TSNRa denote the target signal-to-noise ratio as
MT
TSNR. 10
Mo MT
where Mr is that the terminal signal energy and Mo = ||| Doing so will ensure that the final SNR of the noise scheduler exactly matches the user- specifiee TSNR.B. We then define M, a with a₂ (0, The recursive relation gives
Mr Mo II at = log Mr log My Σ log at.
(8)
This recursive relation ensures that the total energy dissipated across all diffusion steps equals the predefined TSNR, guaranteeing that terminal noise levels match the target. To ensure monotonic energy decay (M, < M,-1, VI), we introduce temporal corruption scheduler g Zt R+ (e.g.,
linear scheduler g(t) = t) that explicitly modulates phase-space trajectory dispersion:
log(Mr /Mo) 19-11(8) g(()
01 = exp
(9)
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This parameterization of {az} through TSNRas achieves two critical advancements over conventional approaches. First, we eliminate heuristic tuning of Suin. 9max. and 7 by deriving them from the physically interpretable as Second, by introducing controllable noise scaling, enable quantifiable noise injection at each diffusion step via the instantaneous SNR metric (SNR(L) S TSNRa, E 7)):
SNR
1,
II as
(10)
where oe represents the cumulative signal retention up to step t.
4.4 SNR IMPLICIT CONDITIONING
The inherent stochasticity of diffusion models poses significant challenges to TSAD, where input- consistent reconstruction of normative patterns is a prerequisite for accurate desidual-basee anomaly scoring. Conventional denoising processes permit multiple trajectory realizations {2,-) J from single noise instance Zr, leading to irreducible variance in anomaly scores s(Z)) [ 2o1131
To mitigate this, we introduce SIC, a spectral alignment mechanism that estimates the optimal corruption level for each imput instance during inference.
SIC mechanism identifies the optimal corruption factor and denoising step by estimating the amount of original signal information that is sufficient to guide the diffusion model toward input-consistent generation. The SNR provides a physically interpretable measure of signal fidelity across frequency bands. By modulating the SNR, we obtain an implicit conditioning that guides the diffusion model to input-consistent reconstruction while preserving its flexibility. Let Ziest
R W x denote test
instance with W timesteps and variates. We first decompose Zusst via spectral Gaussian filtering:
Zrett
Zooe
Zoo = Go (Zost)
= Zust Go (zss),
(11)
Low Prequency
where G, RRW- R is zero-phase Gaussian low-pass filter with bandwidth æ. The residual N contains all components other than the low-frequency part. From this decomposition, we estimate the inference signal-to-noise ratio (ISNR), an instance-specific metric of signal energy retention that quantifies the optimal noise corruption for implicit conditioning;
1Z I +
||G (Zees) 13-
ISNR
(12)
Z - Go (Zrest)
where > prevents division by zero in noiseless edge cases. The Frobenius norm aggregates energy across all K variates. The ISNR guides the adaptive initialization of the reverse diffusion process through two estimation phases: estimating the optimal noise intensity a** from ISNR; and aligning a* to the nearest diffusion step.
Phase 1: Optimal Corruption Factor Estimation Given the instantancous ISNR from Eq. 12]
we derive the instance-optine corruption factor a" leveraging the statistical properties of Z (see detailed proof in Appendix 8.1:
ISNR
(13)
ISNR (Zrea)))) a²( (Zusa)
where (Zess) and (Zuea denote the mean and variance computed for elements in the test instance Zrest, respectively.
Phase 2: Diffusion Step Alignment Since a* may not reside in the pretrained schedule (āz (m
we project it onto the nearest feasible step f:
T = arg min aa - a*I.
(14)
Finally, the test instance Zuss is then corrupted to Z Z+ ZarZiss + / d,cC where Gaussian noise e strategically degrades anomalous components while preserving original data corre- lations. This controlled initialization enables conditional reverse process governed by (q.(15) while

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Table 1: Results on the five real-world datasets. For each metric, the higher the value, the better the performance. All values are the average percentages over five random seeds. The best F1 score is highlighted in bold and the second best is underlined.
SMD
PSM R
Ascrage
Method
F1
F1
P-]
14.87
78.10
24.98
13.46
92.37
23.50
14.30
23.94
17.91
32.62
89.57
68.05
99.74
58.61
73.83
37.65
Classcal
Pcon
16.55
45.61
34.20
13.32
92
23.28
13.62
19.95
16.18
12.79
84. 89
47.10
97.30
59.61
74.06
17.05
15:21
24.55
17.93
59.41
27.55
23.58
73.75
18.98
PCA
11.29
04.04
19.30
13.19
59.75
25.00
15.03
30.39
17.77
31.13
92.78
02
99.32
02.30
76.59
16.03
Representation
Deep XVDD
12.96
16.04
14.52
11:77
17.09
13.54
16.19
15.78
15.37
16.63
39, 30
30 30.46
51.29
61.95
70.51
10.88
DCDeteaar
2.35
2.90
12.05
15:05
10.54
100.00
19.07
12.79
100.00
22.68
4.15
100100
7.98
27:73
100,00
43.42
12.02
100.00
21.46
22.92
Recuntmation
PUANonaly
TimesNet
13.44
10.35
59.50
19.00
12.79
100110
22.08
13.05
19:911
16.19
10..5
100.00
42.75
60.00
K3.99
39.77
099
26.72
14.76
11.59
19:77
13.36
20.46
97.33
90.56
23.74
26.07
SARAD
10.74
91.94
19:24
12.79
100.00
22.68
17.06
41.26
24.10
41.55
58,50
48.60
96.20
66.92
78.92
38.71
DiITAD
10.53
100.00
19.06
12.80
99.67
22.68
10.60
22.58
14.32
27.87
98.88
43.48
12.04
100.00
21.65
24.24
Difluios Model
9.75
26.16
33.17
16.47
93.61
28,01
22.64
24.82
81.34
56.87
77.34
4381
ensuring training-inference SNR consistency and anomaly attenuation:
po (7 7r005) = (o
II
po 7 -1
Zk)
(15)
EXPERIMENTS
5.1 EXPERIMENTAL SETUP
Datasets We evaluate method five real-world datasets collected from industrial control and service monitoring scenarios: Mars Science Laboratory rover (MSL) (Hundman et al.) 2018). Soil Moisture Active Passive satellite (SMAP) (Hundman al. 2018). Server Machine Dataset (SMD) (Su
et al. 2019), Pooled Server Metrics (PSM) (Abdulaal et al.) 2021), and Secure Water Treatment (SWaT) (Mathur and Tippenhauer 2016). Details of these datasets are provided in Appendix 8.3.1
Evaluation Metrics For performance evaluation, report standard Precision (P), Recall (R), and F1 scores. To avoid potential overestimation introduced by point-adjustment methods (Sarfraz et al., 2024 Kim al.) (002)), we adopt a strict evaluation protocol based on exact match of predicted and ground-truth anomaly points.
Baselines We conduct extensive comparisons between our model and a range of state-of-thh-ar anomaly detection baselines. Classical machine learning methods such as Isolation Forest (IF) (Liu
et al. 2008). ECOD (Li et al.) 2022). CBLOF (He et al. 2003) and PCA (Schölkopf et al.. 2001 are
included to represent traditional approaches. We also evaluate reconstruction-
deep-learning
methods, including the CNN-based TimesNet (Wu et al.) 2022), the Transformer-based models such as Anomaly Transformer (AT) (Xu et al. (022)). ATF-UAD (Fan et al. 2023). D3R (Wang et al. 2023) and Sarad (Dai et al. 2024), the GaN-based FGANomaly (Du et al.] 2021 (21)).0 and the diffusion-based methods such as DiffAD (Xiao et al., 2023) and ImDiffusion (Chen et al. 2023) In addition, we consider representation learning methods, including Deep SVDD (Ruff et al. 2018 and DCDetector (Yang et al.) 2023). Notably, DiffAD and ImDiffusion leverage the strong generative capabilities of diffusion models to model temporal dependencies for anomaly detection. All baselines are implemented using their officially released code, with hyperparameters set according to the recommended configurations provided in the original papers.
5.2 RESULTS
As shown in Table I ICDiffAD achieves significant improvements across five benchmark datasets, demonstrating two critical advances. First, ICDifIAD outperforms previous diffusion-based methods by substantial margins, with +19.57% F1 improvement DiffAD and +34.06% over
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Table 2: Ablation study for component contribu- tions. The standard Fl scores are reported.
Table Ablation study for g(t). The standard F1 scores are reported.
IC
SNR
SIC
MSL
SMAP
SMD
PSM
SWaT
Avg
g(O)
MSL
SMAP
SMD
PSM
SWaT
23.01
31.88
22.81
21
16.16
44.57
76.69
76.05
40.27
36.52
Linear
33.17
28.01
23.68
56.87
77.34
32.60
26.21
22
54,80
77.19
42.74
Quadratic
34.78
27.70
27.42
55.97
77.33
33.17
28.01
23.68
56.87
77.34
43.8
Cosine
34.81
26.76
26.16
55.54
77.33
Input Time Series
Reconstructed Series Predicted Anomaly
Ground Truth
(a) stochastic generation by DiffAD
(b) input-consistent generation by ICDiffAD
Visualization of the results obtained by DiffAD Xiao et al. 2023 and ICDiffAD on MSL. The region of pale-pink shadow indicates the ground truth of anomaly events.
Figure 3:
ImDiffusion, validating that SNR-guided input-consistent generation (via the SIC module) resolves the reconstruction instability of vanilla diffusion models for TSAD. The catastrophic failure of ImDiffusion (- (-14.49% F1 vs. DiffAD) further underscores the necessity of our SNR-guided implicit conditioning approach. Second, ICDiffAD surpasses SARAD, the prior reconstruction. based state- of-the-art, by +5.1% average F1, achieving 8.27% absolute improvement on the noisy PSM dataset despite minor gaps in SMD (-0.42%) and SWaT (-1.58%). This highlights the SNR Scheduler's effectiveness handling temporal heterogeneity. As shown in Appendix 8.4.1 ICDiffAD reduces false positives (-60.23%) versus DiffAD while maintaining competitive recall, achieving better precision-recat balance. Full metrics (AUC-ROC, AUC-PR, VUS-ROC, VUS-PR (Paparrizos et al.. 2022). Afilliation-base metrics (Huet et al., (022), point-adjustment metrics), standard deviations of Table I. and model complexity are detailed in Appendices 8.4.2 8.4.4. Figure compares the reconstruction and anomaly detection of DiffAD (Figure 3a and ICDifIAD (Figure 3b). ICDiffAD not only shows more faithful reconstruction, signals, resulting in better anomaly detection performance.
also successfully attenuates the anomalous
Despite current advancements in deep learning, classical methods exhibit strong performance in our experiments. In particular, the SWaT dataset, IForest and PCA significantly outperform deep learning methods. This is because most anomalies in SWaT can be easily detected through naive methods, despite complex temporal heterogeneity. However, experiments with SMD further prove our case when dealing with complex temporal heterogeneity and anomalies, as classical methods are being surpassed by deep learning methods such TimesNet and SARAD. ICDifTAD introduces two improvements for current diffusion models: the SNR Scheduler, which enhances the learning process on different noise scales, and the SIC module, which enables input-consistent generation for TSAD. The synergy between these two modules enables the improved diffusion for TSAD.
5.3 ABLATION STUDIES
Component Contributions Table quantifies the incremental benefits of our proposed components: SNR Scheduler and SNR Implicit Conditioning (SIC). The vanilla diffusion model (all components disabled) achieves average F1-score of 36.52%, with particularly poor performance on SMD (16. (16.16%) due to its complex temporal heterogeneity. Introducing Implicit Conditioning (IC) alone improves the average Fl-score by 3.75%, showing the need for input-consistent generation of Diffusion Models. Coupling IC with the SNR Scheduler yields an additional 2.47 average point gain, as training becomes more guided by SNR, rather than generation-orienteed Our SIC method estimates the optimal corruption factor and denoising step for diffusion models, achieving peak performance (43.89% average F1) through ISNR-guided anomaly attenuation and input-consistent generation.
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SHAPP
0256
0.00
3.00
325
0.72
325
1347
0.75
0.60
330
0.50
210
Figure 4: Ablation study of aS in terms of F1 score
Figure 5: Ablation study of a in terms of preci- sion and recall
Effect of g(() Table 3 compares different choices of g(t) of the SNR Scheduler and demonstrates that the proposed SNR Scheduler is robust to different g(t). This is because the SNR scheduler regulates noise injection according to the specific requirements of the anomaly detection, independent of g(),, which governs the overall trend of noise injection.
Effect of TSNRan The ablation study in Aemprricalr demonstrates the operational trade- offs governed by TSNR selection under fixed diffusion steps (T' 200). High TSNR levels (15-30 dB) correspond to minimal noise injection during training, inadequately conditioning the model for TSAD and yielding suboptimal Fl scores across all datasets. Conversely, aggressive noise corruption at low TSNR (=20 dB) guarantees that the model learns at different noise scale. This mechanism drives consistent F1 improvements as the TSNR decreases, with MSL, SMD, and PSM achieving gains of +10.3, +7.5, and +16.2, respectively, between 30 dB and -20 dB.
The dataset-specific response further empirically substantiates the SNR Scheduler's necessity, MSL and SMD exhibit gradual performance improvements (MSL: 0.2564 - 0.3589; SMD: 0. 1725 - 0.2437), reflecting their sensitivity to multiscale anomalies (low- amm--mml drifts and high-freug high-frequency spikes). In contrast, SMAP and SWaT display abrupt saturation. This is because their time series anomalies have much simpler patterns. Beyond TSNR <-20 dB, performance plateaus as the scheduler's adaptive noise curriculum inherently balances coarse and fine fine-grained generation.
Effect of The Gaussian filter bandwidth governs the spectral decomposition fidelity of the SIC module, directly influencing the trade-off between normative pattern preservation and anomalous deviation suppression. S precision (P) and recall (R) across four TSAD benchmarks spanning heterogeneous temporal entropy (H)): PSM (H 6.44), MSL (H 4.06). SMD (H 3.83), and SMAP (H 3.05).
For Complex Temporal Regimes (e.g., PSM), ICDifIAD achieves maximal performance (P = 0.45, R 0.76) at minimal = 1. At a aggressive high-pass filtering isolates high-frequency into the residual N, minimizing ISNR. This induces a small corruption factor, enforcing the reverse diffusion process to start from lightly corrupted signal and retaining most of the complex temporal information. Consequently, input-consistent reconstruction dominates. Further increasing will degrade the performance on PSM, because stochastic reconstruction is suboptimal complex temporal regimes.
For Simpler Temporal Patterns (MSL, SMAP, and SMD), increasing a enhances precision (MSL: P = 0.21 + 0.25; SMAP: P = 0.21 + 0.67; SMD: P = 0.18 - 0.52) but reduces recall (MSL: R = 0.53 + 0.46; SMAP: = 0.89 - 0.34; SMD: R = 0.91 > 0.41). Larger a elevates ISNR, increasing the denoising factor and resulting in the retention of the coarse trends. While this suppresses high-freque frebufrr noise (improving precision), attenuates reconstruction errors for subtle anomalies embedded in low-frequency drifts (reducing recall).

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6 CONCLUSION
We propose ICDifAAD, a diffusion-basee time series anomaly detection method that innovatively introduces an SNR-guided noise scheduler and an implicit conditioning mechanism address the challenge of stochastic reconstruction in diffusion models for TSAD. This approach achieves balance between generative flexibility and reconstruction fidelity. ICDiffAD achieves improvements by +19.57% average FI score over previous diffusion-basee methods and +5.1 % average FI score over reconstruction- based methods, demonstrating its ability learn robust temporal patterns and detect anomalies through input-consistent generation.
7
ACKNOWLEDGEMENTS
This work was supported in part by the National Key R&D Program of China (No.2023YFB4302200) and the Special Foundations for the Development of Strategic Emerging Industries of Shen- zhen(No. KJZD2002200030944400000
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8 APPENDIX
8.1 PROOF OF CORRUPTION FACTOR ESTIMATION
In this section, we provide the proof of Eq. (1 presented in this paper.
According to Eq. 2). the noise corruption Z, after steps in the diffusion process is given by
Z vā,Zo + VI-
(16)
For 7, the term 0z Zo represents the useful signal, while v ãe corresponds to the noise component. The signal-to-noise ratio SNR, is therefore given by
P F Zo)
SNR,
(17)
P ( - E.
where IP denotes the power operator, which is specifically defined
P(Z) = (2)
(18)
where 2() and (Z) denote the mean and variance computed for elements in the Z, respectively. Thus,
12 (Vā, Zo) + 0² (V Zo) SNR, 112 (VI Cs (2) + o (v1 a:
(19)
As ~ (00, I), it follows that
SNR, ā. (n² + oo)
(20)
1-aa
Therefore, we estimate the corruption factor in the denoising process based the ISNR as
ISNR
a ISNR + (2(z)) + (Zo)
(21)
8.2 DATA PROCESSING PSEUDOCODE
Step 1: load original training data
X Linput np. load ("path/to/dated)
Step 2: Normalize training data (Standard Process)
norm normalize (X (X_ingut) minmax scaler on every channel
Step 3: Extend foatures (repeat first C channels)
x_extra X_norml;, :C] _exteeneed concatenate (o_m Lextra, axis=1)
Step 4: slice into sliding windows
for in range (0, 1en (X (X_extended) win_size 1, win_size): winaew extendedd i sizesizel
Step 5: Format window
2 unsqueeze (window)
shape: (1, win_size, num num_features)
step Output yield
8.3 DETAILED EXPERIMENTAL SETTINGS
8.3.1 DATASETS
We evaluate anomaly detection performance using five real-world datasets spanning diverse applica- tion domains, including industrial control and service monitoring. The anomalies in these datasets range from IT service outages external cyber-physical attacks targeting control systems.
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Table 4: Statistics of the main datasets
Datasets
Domain
Entities
Dimension
Train #
Test #
Anomaly Ratio
MSL
Spacecraft
27
55
58,317
73,729
10.50%
SMAP
Spacecraft
55
25
135,183
427,617
12.80%
SMD
Server Machine
38
38
708,405
708,420
4.20%
PSM
Server Machine
25
132,481
87,841
27.80%
SWaT
Water Treatment
51
51
496,800
449,919
12.10%
Server Machine Dataset(SMD) (Su et al. 2019): The SMD dataset was collected from
large-scale internet company and contains five weeks of data from 28 server machines, each equipped with 38 sensors. The first five days consist solely of normal data, while the last five days intermittently include anomalies.
Pooled Server Metrics(PSM) (Abdulaal et al. 2021): The PSM dataset was collected from internal nodes of multiple application servers at eBay. It contains 13 weeks of training data and 8 weeks of test data. Anomalies are present in both sets, but labels are provided only for the test set. These labels were manually created by engineers and domain experts, and may include both scheduled and unscheduled anomalies.
Mars Science Laboratory(MSLL and Soil Moisture Active Passive(SMAP) (Hundman et al., 2018): The MSL and SMAP datasets are publicly available datasets collected by NASA. They consist of telemetry anomaly data derived from Incident Surprise Anomaly (ISA) reports in spacecraft monitoring systems. The MSL dataset contains 55 dimensions, while SMAP has dimensions. The training sets for both datasets contain unlabeled anomalies.
Secure Water WWa))))nt(SWaT) (Mathur and Tippenhauer 2016 The SWaT dataset was collected from scaled-down water treatment testbed instrumented with 51 sensors 11 days. The first 7 days contain only normal data. In the last 4 days, 41 anomalies were injected using various attack strategies.
Table summarizes the statistics of these datasets. Train# and Test# denote the number of training and testing samples, respectively. The anomaly rate is defined as the ratio of the number of anomalous points to the total number of test samples.
8.3.2 BASELINE
All baselines are evaluated based on our own runs using the same hardware environment. We trained all baseline models using their official open-source implementations and followed the hyperparameter settings recommended in their respective papers. The publicly accessible URLs of the baselines used listed follows:
(Liu et al.
2008 https:/ggthubb.oommyyyhooo2bppp
ECOD Li al.
2022): http:///gghhbbboommyyyyooo///yy
CBLOF He et al. 2003): https//ggiiubbcoommyyyyhoooooodd
PCA (Schölkopf et al.
2001) httpss//gghhhubbooom//yyoooooood
DeepSVDD (Ruff al.
2018
:
https://gihhna.cccmmm//hhaooossp
DCDetector Yang et al.
2023
ATF-UAD [Fan et al.
2023): http::/gggibbbboom/wwwssssss///a
AT (Xu al.
2022):
FGANomaly (Du et al.
TimesNet (Wu et al.
2022
D3R (Wang al.
2023):
SARAD DDai et al.
2024)
http://hhthbbboommcccciaaaaaaaap
DiffAD (iao et al.
2023 http://ggiabbboom/CCuunnnnnnniDD
ImDiffusion Chen al.
2023)
http://gghhaakoomm////o0s///ff/
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8.3.3 IMPLEMENTATION DETAILS
We implemented ICDiffAD in Python using the PyTorch library and the Hydra framework. All experiments were conducted single NVIDIA 3090 GPU (24 GB). We used the Adam optimizer with an initial learning rate of le-4. In our experiments, for all datasets, the sliding window size was fixed at 1024, the number of diffusion timesteps
set to 200, the SNR scheduler parameter TSN RAB was set to -10, and the function g(t) was defined as a linear function. For corruption factor estimation in the SIC mechanism,
performed TPE sampling over Gaussian kernel sizes 8 E {3. 1024) and Gaussian kernel standard deviations a E {1, 100) on each dataset, and we reported
the results corresponding to the highest Fl score.
8.4 ADDITIONAL EXPERIMENT RESULTS
8.4.1 CONFUSION MATRIX COMPARING DIFFAD AND ICDIFFAD
Contale MOTE
Contann MASTO
Contaboo
CM
PP00000
(a) MSL for DiffAD
(b) SMAP for DiffAD
(c) SMD for DiffAD
(d) PSM for DiffAD
(e) SWaT for DiffAD
(f) MSL. for SARAD
gg) SMAP for SARAD
(h) SMD for SARAD
ii PSM for SARAD
i SWaT for SARAD
(k) MSL for ICDifIAD
() SMAP for ICDiITAD
(m) SMD for ICDiiTAD
(n) PSM for ICDifIAD
(o) SWaT for ICDifIAD
Figure 6: Confusion Matrix Comparing DiffAD, SARAD, and ICDifFAD
Figure shows the confusion matrices of DiffAD (Diffusion Models), SARAD (Reconstruction Models), and ICDiffAD (Ours) for different datasets. Compared with DiffAD, ICDiffAD's SIC mechanism has reduced the average False Positive (Predicted Anomaly but Normal) by 60.2% while maintaining high detection accuracy. The reconstruction-baut based method struggles in noisy datasets such as PSM, causing the model to have 2 times more False Negatives (Predicted Normal but Anomaly) than ICDifFAD. On the MSI. and SMAP datasets, ICDiffAD also demonstrates excellent detection performance with low false positive rates, highlighting its strong robustness in satellite telemetry scenarios. However, our method still faces challenges when dealing with datasets like SMD, which exhibit high temporal heterogeneity. The presence of rapid distributional shifts and diverse temporal patterns in SMD makes it difficult for the model to maintain stable performance across all time periods. Addressing these challenges remains important avenue for future research in diffusion-basse TSAD. Overall, ICDiffAD significantly reduces false alarms, which on the one hand validates the effectiveness of the SIC mechanism in addressing uncertainty reconstruction in
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iiffusion-based TSAD, and the other hand, highlights the great potential of diffusion models waiting to further explored.
8.4.2 ADDITIONAL. DETECTION RESULTS
Table AUC-ROC and AUC-PR scores across five real- datasets, where higher values indicate better performance. All values average percentages from five random seeds. The best results shown in bold, and the second-best results are underlined.
Dapaa
Aserace
Clossed
CR..
BCOD
59.41
61.15
14.41
18:33
57. 595
39.33 23
10.30
15:34
62.69
06.05
10.24
12:30
54.15
70.39
39.73
51.77
87.75
86: 12
76.49
75:21
01.53
08:87
30.15
14.85
69.57
17:44
01
64.70
47.04
81.00
00:55
T3
Representatinn
41:30
51:77
30.51
52.23
4:7
17.10
38.35
02:47
17.35
54.31
17.55
AT
40.04
9.90
$1.30
14.19
49.97
4.55
45.79
26.21
42.25
10.23
47.67
13,02
Recostraction
PtANNemaly
TimeNia
48.15
10:31
65.05
11:35
09:65
0.69
06.32
83.21
86-94
73.60
52.65
62.94
31.85
SARAD
96.91
63.16
33.25
Difteioc Model
DIAA
Ours
75.78
26,05
37.51
55.30
13.30
01:31
74.83
1435
1.10
71.41
41.25
27337
83.15
20.40
72.51
71.00
13.07
1510
Table Standard deviations of anomaly detection performance Table AUC-ROC and AUC-PR scores are reported. All values are percentages.
Standard deviations of
Tatowel
MSL.
SMAF
SMD
FIM
SWAY
Method
AUC RDC
AUL
AUC ROC
AUL
AUC ROC
AUC
AUC
AUC
AUC ROC
AUC
0.37
0191
Classical
6.00
0.00
0.00
0.00
0.00
Representation
Deep DVDD SVDD
DCDetecee
0.19
200
0.14
0.6
0.87
L0K
025
0.43
0.78
2044
0.13
104
5.01
0.32
3.05
0.15
0.04
0331
0.21
0.58
AIT-UAD
1.09
2.01
8.69
4.20
1.21
0:25
1.30
3:10
15:30
0.36
AT
1.32
0.97
1.37
1.26
0.44
0.43
1.95
1.01
5.30
1.95
Recostruction
EGANomally
TimesNet
0.74
1.65
0.17
0.18
0.24
5.97
0.93
0.82
0.44
1.13
2.44
2.84
0.72
Diffusioo Model
DIEAD
0.83
0.42
0.17
0.08
0.34
0.74
0.45
0.2
0.00
Ours
0.48
0.68
3.98
2.48
0.84
1.29
0.66
0.27
0.30
0.000
In addition to using precision, recall, and Fl-score as evaluation metrics Sections 5.2 and 5.3 we
further employ comprehensive set of measures including lbreshold-independeer A C-ROC and AUC-PR, completely parameter-free VUS-ROC and VUS-PR, the sequence-level evaluation metric Affiliation, and pojut--ddustmmnt metrics for event-level evaluation, enable thorough assessment.
reppots the AUC-ROC and AUC-PR results of the evaluated methods, while Table 6 presents the corresponding standard deviations. Table 7 summarizes the VUS-ROC and VUS-PR scores, with their standard deviations provided Table 8 Table displays the Affiliation metric values, and Table 10 lists their respective standard deviations. Table reports the point-adjustment metrics.
Experimental results demonstrate that ICDiffAD significantly outperforms prior diffusion-based approaches, achieving improvements of 23.45% in AUC-RÓC, 18.65% in AUC-PR, 16.37% in VUS-ROC, 12.53% in VUS-PR and 8.93% in Affiliation-FF over DiffAD (ImDiffusion, which relies on multi-step reconstruction and voting, is not compatible with these metrics). These findings further validate the effectiveness of our method in addressing the reconstruction instability inherent in vanilla diffusion models for TSAD.
Although ICDiffAD does not consistently achieve the best performance across all datasets, diffusion- based methods with input-consistent reconstruction exhibit strong overall performance and achieve high average scores. This underscores the considerable potential of diffusion models in TSAD, warranting further exploration.
8.4.3 STANDARD DEVIATIONS OF DETECTION PERFORMANCE
Table 12 reports the standard deviations of anomaly detetion performance as reported in Table in
Section 5.2
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Table 7: VUS-ROC and VUS-PR scores across five real-world datasets, where higher values indicate better performance. All values are average percentages from five random seeds. The best results are shown in bold, and the second-best results are underlined.
Methad
1I
10111
61.07
17.30
31.0
1:.5
1750
$31.52
83.77
10-15
Claskill
CBLDP
BOOD
61.56
63:94
22:32
19:74
43.27
51.12
11.91
13.01
68.28
64.55
8,31
9.07
64.90
00.25
46.25
35.62
32327
78.55
63.42
12.00
01.62
66.70
28,00
20:
PCA
18.99
44.85
1005
09.43
51.43
Reprraentation
Deep
50.96
14.84
ATFUAD
13.04
11:39
1.97
50.3
1035
03:11
17.5
AT
$1.28
14:45
$2.02
15.99
49.97
5.36
36.82
26.47
46.45
12.79
47.26
15.01
Recotmaction
IGANomaly
Time-Nar
$7.51
15:90
17.29
08.61
40.01
13.05
17.44
70.37
0.19
50.94
00065
45.00
76.11
66.61
63:85
51.31
78
SARAD
87.52
30.25
Difiaiion Medki
DEAD
Ours
75.84
31.14
55.23
15:25
15.55
73.78
14.47
7.35
56
41.60
26,25
02.35
42.53
06.35
30.35
20,06
16255
Table 8: Standard deviations of anomaly detection performance in Table and VUS-PR scores are reported. All values are percentages.
Standard deviations of
Mechod
VUS-ROC
VUS-PR
VUS-ROC
VUS-PR
VUS-ROC
VUS-PR
VUS-ROC
VUS-PR
VUS-ROC
VUS-PR
0:78
0.32
0.70
0.33
0,40
0.11
1.13
1.10
1.56
2.58
Classical
CBLOF
ECOD
0,00
1.30
0.00
[..1
0,00
0.28
0,00
0.19
0.00
1.32
0,00
0.39
0,00
01.4
0..7
0,00
0,00
0.99
0.95
0,00
PCA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Representation
ATD-
3.26
1.92
0.34
2.72
2.19
0.12
1.74
11.92
6:27
AI
1.21
1.35
1.52
1,23
0.39
0.48
3.22
0186
3.61
0.43
Recontuction
FGANomaly
TimesNet
0.59
1.14
0.23
0.18
5.35
1.17
0.22
0.95
0.50
0.73
0.37
0.38
0.86
2.50
0.93
1.83
0.34
1.20
0.43
3.37
D3R
1.77
12773
1,82
11.44
2.46
0.37
1.43
1.77
0.38
1.68
SARAD
1.60
0.60
1.43
0.24
1.01
0.76
1.09
0.69
0.60
1.03
Diffision Model
EXTAAD
8.4.4 MODEL COMPLEXITY
We conduct comprehensive comparison of neural network- -ased models on the SMD dataset in terms of training time, inference time, model size, and computational cost, to evaluate the practicality of ICDiffAD in real-world deployment scenarios. The results are presented in Table 13 All experiments related to time overhead were conducted on a single computing node equipped with an AMD EPYC 7443 CPU (24 cores, 96 threads), NVIDIA 3090 GPU (24GB), and 256 GB of RAM.
As observed, due to the iterative nature solving each diffusion step. diffusion-based models inherently require longer training and inference times. However, given that both the training and testing sets of SMD contain approximately 16 days of data, the additional overhead introduced by the diffusion process remains acceptable for practical deployment and maintenance. Moreover, thanks to the use of an SNR-based scheduler for optimized noise scheduling and the input-consistent reconstruction guided by SIC mechanism of input sequences, ICDiffAD achieves faster training and inference speeds compared to existing diffusion- based approaches.
8.4.5 ABLATION STUDY FOR DENOISING STEP
We conducted comprehensive experiments on five real-world datasets by testing fixed denoising steps during the reverse diffusion process. As shown in Table [4 the optimal denoising steps vary across different datasets, and all fixed-step configurations underperform compared to our SIC method. These results confirm that our proposed SIC approach effectively estimates the optimal corruption factor and denoising steps for various datasets.
8.5 BROADER IMPACTS
The societal implications of ICDifIAD extend beyond technical advancements time series analysis. From a positive perspective, the method's ability to detect subtle anomalies in noisy, heterogeneous data could enhance predictive maintenance in critical infrastructure (e.g., power grids, transportation systems), potentially preventing catastrophic failures and reducing economic losses. In healthcare, improved anomaly detection in physiological time series (c.g., EEG, ECG) may enable earlier diagnosis of pathological conditions, though rigorous clinical validation would be required before
17

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Published as a conference paper at ICLR 2026
Table 9: Affiliation-basee metrics across five real-world datasets, where higher values indicate better performance. All values are average percentages from five random seeds. The best results are shown bold, and the second-best best results are underlined.
Averaze
Ioust
33.81
15,45
$9,335
63.31
Clasica
CBLOF
ECUED
58.94
00.60
99:60
$1.72
55.43
71.45
44.96
42.41
10360
19300
59.56
62.05
63.51
69
62.33
16.32
60.08
02.92
60.00
91:35
10380
06.35
75.00
73.21
60.64
52.00
100.00
150.000
68
15.30
68,30
65.72
PCA
59.27
64.53
01.79
51.22
99.71
72.79
72.58
55.42
02.85
06.24
100.00
79.09
$3.90
09.99
70.05
69:44
Repeciestaion
DeopSyD0 SY
+0.33
05.21
00:11
31.01
18.23
12.30
06.82
10.00
71.41
15.32
04.11
19.30
55.13
07.55
70.33
04.15
AT
51.04
06:49
96.09
54.08
$7.00
09:45
54.36
53.63
09.39
67.62
Recommtaction
EGANomaly
Timoohin
68:74
56.36
95.27
04:13
71.64
75.43
44.62
53.44
19350
95.62
61:71
06.51
56.11
53.46
08.
$3.25
63.11
77.15
55.31
16:12
96.55
98.34
73.06
70.50
50.53
14
1001000
81.52
00.00
11.13
68.55
11.90
91.16
50.82
61.33
10300
Diftasion
99.63
$1.92
78.66
Table 10: Standard deviations of anomaly detection performance in Table Affiliation- based metrics are reported. All values are percentages.
Standard deviations of
Petrect
STSI
SMAP
XSDD
PSM
SWat
Medin
Al-FF
AIE-
Al:FI
A.I.R
A:1-E1
AII-F
All-R
A.0.21
ATI-I
All-
ATI-F]
ATI-F
AIT-3
ATI-FI
IFea
0.67
3.21
0.58
0:77
2.85
01:4
1.05
1.78
0.44
1.91
4.63
1.01
0.11
0.007
0.055
Clasical
CRLOF
EODD
0.00
0.96
0.00
3,64
0.00
0.88
0.32
0.00
0.00
1.57
0.00
0.40
0.00
0.33
0.00
1:35
0.00
0.39
0.32
0.00
0.00
1:33
0.00
0.62
0.000
0.08
0.02
0.00
0.00
0.01
PCA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Representaton
Deep SVDD
DCDetctorr
0.00
1.0g
0.00
2,67
0.00
E.33
0.00
0.18
291
0.00
0.00
0.34
0.00
1311
0.00
0.77
0.00
0.82
0.00
3:37
0.00
3.92
0.00
3:27
0.00
1.00
0.00
0.37
6:33
0.00
ATF UND
1.02
0,33
0,41
133
3.36
1.07
1.15
3.25
1335
1.37
3.28
2327
7.32
3.23
6,30
AT
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.50
0.50
0.00
0.00
0.00
0.00
0.00
Recontraction
FGANomaly
TimesNett
0.21
0.30
5.46
4.33
0.21
0.38
0.15
0.87
0.85
4.39
0.25
1.31
0.49
1.52
6.61
2.69
0.55
0.86
0.58
2,61
3.38
7.36
0.20
1.92
0.000
1.39
0.00
0.88
0.00
0:45
D3R
1.31
3.67
0.99
0.24
4.28
0..3
0.29
1.32
2.14
0.48
2.58
0.10
1.49
4.58
1:27
SARAD
0.81
10.30
0.41
0.28
1.99
0.:3
0.88
4.34
1.28
1.99
0.67
1:52
1.00
1.21
0.42
Diliision Model
DREDD
Onrs
0.02
2.44
3,39
1.21
0100
0.64
0.12
0.06
0.51
1.38
0.23
0011
0.22
1.32
0.78
2.28
0.11
0.33
0.19
1200
0.01
1.34
0100
0.08
0.00
0.00
0300
0.00
0500
0.00
deployment. Methodologically, our SNR-centric framework advances the integration of diffusion models into discriminative tasks, offering insights into balancing stochastic generation with input- consistent reconstruction in generative AI applications such as medical imaging and climate modeling.
18

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Published as conference paper at ICLR 2026
Table 11: Point-adjustment metrics across five real-world datasets, where higher values indicate better performance. All values are average percentages from five random seeds. The best results are shown in bold, and the second-best results underlined.
Assrage
Classical
CHLOF
59
PCA
62.18
62.19
86.
70.54
Reperseatation
UCDertettr
SVDD
M1.2
93.09
59/09
3083
98.92
56.02
K3.99
91.10
15.67
17.18
000.3
97.90
93.11
50042
$2.39
R0397
95.06
ATF UND
20.15
56,29
93.15
A1
62.09
65.15
99.59
94.15
59.40
96.49
80.40
95.45
02.35
95.91
99.50
97.88
91.55
96.73
94.07
94-92
Recostraction
TineeNet
$3.92
67.58
50.20
56.42
38.91
85.15
95.34
92.52
00.27
58.29
91.78
71.52
85.63
58.46
89.22
63.14
87.41
85.81
95.34
98.19
97.53
96.76
96.43
07.47
86,
88.74
92.98
97.52
91.74
89.85
90.67
86.34
D3R
88.19
$7.69
87.64
86.37
$9.29
67.71
87.34
49
91.91
93.84
99.21
96.45
83.00
83.00
$5.04
89.35
SARAU
91.28
53.99
92.42
90.28
96.15
90.16
500
$7.19
95.77
95.57
95.27
95. 03
92. 22.46
95.53
96.99
M2.40
BREAD
22.77
32.24
34.11
76.32
77.35
19.35
19327
18..0
12.75
17.00
91..92
07.30
93.04
15..00
07.05
10.30
Difficcion Model
InDiffieon
59 90
96
87.82
87.71
06. 18
91 75
76
95
95. 14
94.11
91.53
07.33
89 80
84.85
87.10
01.34
Our
05.29
94.33
94.80
98.11
09
97.40
95.41
05.90
98.85
94.00
95.61
08.31
97.19
99.01
98.19
96.89
Table 12: Standard deviations of anomaly detection performance in Table 1 Standard deviations of threshold-dependent Precision, Recall, scores are reported. All values are percentages.
Dataset
MSL
SMAF
SMD
PSM
SWaT
Method
P
F1
F1
Ft
P
F1
1.17
2.79
Clusical
CBLOF
ECOD
0.00
1.38
9.22
0.00
0.00
1.13
0.44
4,16
0.00
0.00
0.95
0.00
0.45
0.00
1.50
0.58
0.00
0.00
0.44
0.00
4.09
0.00
1.06
0.03
0.00
0.02
0.00
0.00
0.01
PCA
0.00
0.00
0.00
0.90
0.00
0.00
0.00
0.00
Representation
Doup SVDD
2366
7.72
6.16
0.21
2.72
1.55
3.49
1,00
DXDDetctoo
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
ATF-UAD
1.54
21.85
0.46
1.58
0.01
1.07
1,27
7.19
1.67
2.58
9.71
2.36
13.51
9.85
12.31
AT
Recontruction
RiANamaly
TimesNet
0.30
6.46
0.38
0.16
0.85
0.26
0.49
14.00)
2.69
0.44
0.98
0.00
0.20
2.51
0.00
1.7%
0.00
0.00
D3R
1.13
7.76
1.36
0.25
2.78
0.12
0.31
1.58
4.99
0.57
3.46
0.11
2.58
8.51
3.27
SARAD
DILDD
0X02
0.02
0.04
0.38
0.56
0.32
0.00
0.01
0.00
0.00
0.00
Diffisioon Model
DiDiisusioon
2.85
0.45
0.82
0.38
0.15
0.22
1.32
0.67
0.82
6.43
0.47
0.87
NA
NA
NA
Ours
3,00
0.74
0.62
0.21
2,15
3,83
0.20
2.45
0.09
0.00
0.00
Table 13: Model complexity and overheads. The total training time (in seconds), the total inference time (in seconds), the total number of parameters (in MB) and the GFLoPS(Giga Floating Point Operations Per Second) are reported.
Method Deep SVDD
Training Time (s) Inference Time (s) Total Params (MB)
GFLOPS
43.73
0.25
0.02
0.01
DCDetector
1522.39
337.61
0,66
198.10
ATF-UAD
6922.81
239.81
0.14
24.10
AT
19.88
4.22
4.83
123.47
FGANomaly
1678.43 43
13.82
0,09
0.01
TimesNet
144.02
12.20
4.70
319.22
D3R
8960.97
1520.63
52.24
28.62
SARAD
808.20
71.00
9.55
92.45
DiffAD
3299.53
2468.00
38.83
310.29
ImDiffusion
62003.90 .0
3043.42
0.31
51.84
Ours
1282.85
166.34
30.82
716.96
Table 14: Ablation study for denoising step.
The standard Fl scores are reported.
Denoising Step
SMAP MSL
SMD
PSM
SWaT
90%
24.33
23.94
19.88
48.91
76.51
70%
29.12
25.01
21.32
52.37
77.08
50%
32.60
26.27
22.84
54.80
77.19
30%
31.19
27.33
22.71
54.83
76.87
10%
28.71
25.98
20.33
49.61
76.33
19


Reviews:
[{"review_id": "Q3pHmk8Zri", "reviewer": "Reviewer_2Pjm", "summary": "This paper targets the challenge of diffusion-model TSAD, i.e., stochastic reconstructions that inflate false positives. It proposes ICDiffAD, built on two components: 1) SNR Scheduler re-parameterizes the noise schedule using a target SNR and a monotone corruption function, deriving $\\alpha_t$ directly from the SNR goal. 2) SNR Implicit Conditioning decomposes each test window via a zero-phase Gaussian low-pass filter $G_{\\sigma}$ to compute an ISNR, then converts ISNR into an optimal corruption factor, and snaps it to the nearest pretrained diffusion step. On five benchmarks (MSL, SMAP, SMD, PSM, SWaT), the proposed ICDiffAD improves average F1 versus prior diffusion models and stronger baselines. The ablation experiments show gains from both SNR Scheduler and SIC.", "questions": "1) This paper appears to contain notation inconsistencies in SNR expression. Eq. (10) defines SNR(t) using  $\\bar{\\alpha}_T$ (terminal product) while the text underneath says “$\\bar{\\alpha}_t$ represents cumulative signal retention up to step $t$”, which can confuse readers about the intended instantaneous SNR.\n\n2) This paper performs per-dataset TPE searches over filter size and reports the best F1 scores, implying supervised selection using test labels, which can overstate generalization. Clarifying a validation split or unsupervised criterion would help.\n\n3) The discussion and analysis of diffusion-based TSAD methods is not comprehensive enough, and a comprehensive review of relevant papers in this field is needed.\n\n4) Many applications care about event-level detection/latency. Adding event-level metrics would strengthen claims.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1) This paper crisply argues that stochastic reconstructions in diffusion models conflict with determinism requirement of TSAD, with intuitive illustrations and discussion. The motivation is interesting and clear.\n\n2) The proposed method is simple yet effective, and its training control is physically interpretable.\n\n2) The experiments and ablations are comprehensive, and the proposed method improves average F1 and reduces false positives in comparisons.", "weaknesses": "1) This paper appears to contain notation inconsistencies in SNR expression. Eq. (10) defines SNR(t) using  $\\bar{\\alpha}_T$ (terminal product) while the text underneath says “$\\bar{\\alpha}_t$ represents cumulative signal retention up to step $t$”, which can confuse readers about the intended instantaneous SNR.\n\n2) This paper performs per-dataset TPE searches over filter size and reports the best F1 scores, implying supervised selection using test labels, which can overstate generalization. Clarifying a validation split or unsupervised criterion would help.\n\n3) The discussion and analysis of diffusion-based TSAD methods is not comprehensive enough, and a comprehensive review of relevant papers in this field is needed.\n\n4) Many applications care about event-level detection/latency. Adding event-level metrics would strengthen claims.", "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": "3Oz4gphAQh", "reviewer": "Reviewer_J9Mc", "summary": "This paper proposes ICDiffAD, a diffusion-based framework for time-series anomaly detection that introduces a synergized adaptive noise scheduling mechanism. Experiments on several benchmark datasets demonstrate promising performance compared to prior diffusion-based anomaly detection methods.", "questions": "See the weaknesses section", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The idea of customizing a synergized adaptive noise scheduling mechanism within a diffusion model is innovative and potentially valuable for anomaly detection.\n\n2. The introduction of a semi-deterministic generative process is a meaningful adaptation. It helps reduce randomness during reconstruction and could indeed be beneficial for stable anomaly detection tasks.", "weaknesses": "1. Questionable learning effectiveness of deterministic diffusion. The paper modifies both the noise-adding and denoising processes from stochastic to deterministic forms. However, the theoretical rationale and empirical evidence supporting that such deterministic transitions preserve the learning capability of diffusion models are insufficient.\n\n2. Unclear formulation of the SNR Scheduler. The proposed scheduler controls the noise coefficient α based on a target noise level M_T, yet the paper does not explain how M_T is derived, estimated, or learned.\n\n3. Unaddressed risk of anomalous conditioning. The paper criticizes prior deterministic reconstruction methods for potentially conditioning on anomalous inputs but does not clearly explain how the proposed method avoids the same risk when performing semi-deterministic reconstruction.\n\n 4. Ambiguous dimensionality description. During Data Preprocessing, the channel-wise concatenation operation changes the dimension from K to \\hat{K}, but Equation (6) does not define \\hat{K} or specify how concatenation is implemented among the original dimension K. This lack of clarity makes Equation (6) difficult to follow.\n\n5. Questionable assumption. The proposed SNR Implicit Conditioning assumes that normal sequences exhibit lower frequencies while anomalous sequences exhibit higher frequencies. This assumption is not universally valid. Some anomalies manifest through amplitude or trend deviations rather than frequency differences. Moreover, for frequency-dominant anomalies, existing frequency-domain methods could already achieve superior performance, diminishing the uniqueness of the proposed mechanism.\n\n6. The proof in Section 7.1 lacks clarity. The logical connection among Equations (19), (20), and (21) is difficult to follow, and the derivation steps between them are not well explained.", "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": "g4hfKXk11J", "reviewer": "Reviewer_SPCE", "summary": "The paper proposes a diffusion-based method for time series anomaly detection called ICDiffAD. It has two main parts. First, an signal-to-noise ratio (SNR) guided scheduler that replaces standard $\\beta$ schedules by targeting a terminal SNR and deriving per-step $\\alpha_t$ from a function $g(t)$. This makes the corruption level interpretable and consistent across runs. Second, SNR Implicit Conditioning (SIC) that estimates an input SNR by splitting each test window into a low-pass signal and a residual, then maps this estimate to a target signal-retention $\\alpha^\\*$ and chooses the closest diffusion step $\\hat T$. The reverse process then starts from $x_{\\hat T}$ and reconstructs to $x_0$. Anomaly scores are the reconstruction residuals on each window. The model uses a UNet-style backbone on 2D reshaped windows and keeps one set of hyperparameters across datasets. Experiments on MSL, SMAP, SMD, PSM, and SWaT report higher point-level F1 than prior diffusion baselines and small gains over the strongest reconstruction baseline. Ablations indicate that both the SNR scheduler and SIC contribute to the improvements.", "questions": "- What do the superscripts in $x^{\\text{tar}}$ and $x^{\\text{con}}$ stand for? Please define the terms “tar” and “con” explicitly when first introduced to avoid ambiguity.\n\n- Why is GFLOPS highest for your model while wall-clock is lowest among diffusion baselines?\n\n- How does the proposed method compare to baseline in a point-adjusted evaluation protocol?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The proposed SNR Implicit Conditioning (SIC) adaptively estimates an optimal corruption level and denoising step for each test instance. This mechanism effectively selects the optimal diffusion time $\\hat T$ per input, balancing reconstruction fidelity and determinism.\n\n- The reported improvements are consistent across multiple benchmarks. Ablation studies (TSNR sweeps, $g(t)$ robustness, SIC contributions) and qualitative visualizations substantiate the claims, showing that performance gains come from the model design.\n\n- The authors employ point-level metrics instead of range-based ones, avoiding inflated results and better reflecting the intrinsic difficulty of time-series anomaly localization.\n\n- The model uses a fixed set of core hyperparameters across datasets (e.g., $T = 200$, window size $= 1024$, fixed $\\mathrm{TSNR_{dB}}$), which enhances reproducibility and ensures fair comparisons across benchmarks.", "weaknesses": "- The statement that input-agnostic schedulers “indiscriminately degrade all frequency components” and “over-corrupt transients” is asserted without citation or evidence. This statement is also unclear.\n\n- In Sections 4.2 and 4.3, the variable $Z$ appears with both $l$ and $t$ subscripts, but it is unclear which one corresponds to $Z_0$ in expressions such as $\\mathbb{E}\\[\\||Z_0\\||_F^2\\]$. Please clarify the intended meaning of each $Z$ symbol.\n\n- The text suggests that $\\alpha_t$ and $M_t$ depend on each other, which reads as circular. Please state explicitly that a target terminal TSNR is first chosen, a specific example would clarify this. Also fix notation in the $\\mathrm{SNR}(t)$ equation (10) to use $\\bar{\\alpha}_t$ rather than $\\bar{\\alpha}_T$.\n\n- The paper repeatedly claims to achieve *deterministic reconstruction*, but the proposed procedure still **samples noise** when initializing the reverse process at $\\hat T$ via $x_{\\hat T} = \\sqrt{\\bar\\alpha_{\\hat T}}x_0 + \\sqrt{1-\\bar\\alpha_{\\hat T}}\\varepsilon.$ This is **not** determinism in the diffusion sense (which would correspond to DDIM with $\\eta=0$ or a closed-form sampling path). The goal of the paper is to get **input-consistent reconstruction rather than strict determinism**, so the terminology is confusing/misleading.\n\n- The paper reports only strict point-level metrics as the primary evaluation, but does not include point-adjusted or event-level scores used by many prior TSAD works. While strict evaluation is commendable and should remain the main protocol, adding point-adjusted or event-based metrics (even in the appendix) would allow fair comparison with earlier baselines that report only lenient metrics. Without both views, it is difficult to fully contextualize the reported gains.\n\n- Efficiency is mixed up with speed rather than real compute cost. Table 12 shows higher GFLOPS than DiffAD and ImDiffusion but still lower runtime, which suggests the model is actually doing more work per input but runs faster thanks to better GPU utilization or code efficiency. It is therefore unclear whether the method is truly efficient or simply well-optimized.\n\n- It is unclear whether the proposed *SNR Implicit Conditioning* actually achieves the intended effect. The paper does not report the distribution of selected start steps $\\hat{T}$ (e.g., mean and standard deviation per dataset), so it is difficult to verify that the ISNR-based selection is meaningful. A simple baseline would be to perform reconstruction starting from a fixed reduced step (e.g. $T/2$) as a hyperparameter, instead of always starting from full corruption. Without such comparison, it is hard to tell whether the gains come from the conditioning mechanism itself or from simply using fewer denoising steps.", "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": "GOLFtVRObV", "reviewer": "Reviewer_uRQA", "summary": "This paper aims to address the reconstruction inconsistency problem in diffusion-based time series anomaly detection, which arises from the noise-adding and denoising processes. Specifically, the authors argue that when a sample is diffused all the way to pure noise, the strong generative capability of diffusion models can produce highly diverse reconstructions, thereby degrading anomaly detection performance. The paper focuses on optimizing the noise scheduler during both training and testing. In the training stage, the authors replace conventional noise scheduling strategies (e.g., cosine schedule) with one based on the signal-to-noise ratio (SNR). During inference, they further adapt the noise strength per sample, again guided by the SNR. Experimental results across multiple datasets and various baselines demonstrate that the proposed method performs among the top tier. In particular, compared with other diffusion-based approaches, the proposed technique shows improvement in efficiency.", "questions": "1. See the Weaknesses part.\n2. Are the reconstruction results in Fig. 2 produced by the proposed method? If so, the quality of the reconstructed samples appears suboptimal—could the authors clarify this?\n3. In Fig. 3, the performance of DiffAD seems extremely poor, almost equivalent to random output. Could the authors explain the cause of this behavior or verify whether there might be an issue in the experimental configuration?\n4. How is the inference time reported in Table 12 calculated? Does it correspond to a single forward pass of the neural network, or to the entire denoising process? The reported time seems unusually large.\n5. Given that ImDiffusion has a relatively small number of parameters (also see Table 12), is the comparison fair? Have the authors normalized or controlled for model capacity when reporting efficiency results?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "The paper has two notable merits, summarized as follows:\n\n1.  The paper introduces a novel perspective by optimizing anomaly detection from the viewpoint of noise scheduling. In my opinion, this is an interesting and original angle, and to some extent represents a new direction for improving both the accuracy and efficiency of diffusion models for conditional time series generation.\n2.  The experimental evaluation is reasonably comprehensive. In particular, the ablation studies are well designed and clearly demonstrate the effectiveness of each component in the proposed framework.", "weaknesses": "Similarly, from my perspective, the current version of this paper also exhibits several non-negligible weaknesses, summarized as follows:\n\n1. **Motivation.**\n   First, I acknowledge that generative models can produce diverse outputs when starting from different noise priors. I also notice that the paper (see Fig. 1) implicitly assumes that the denoised samples obtained from diffusion models correspond to “normal” (i.e., smooth) samples. This assumption itself is reasonable. However, later in the paper (lines 278–279), the authors state their own premise—*“the premise that time series anomalies manifest as high-frequency deviations from normative patterns.”*\n   Given this assumption, one could argue that existing diffusion-based methods are already capable of separating such high-frequency anomalies from normal patterns. Furthermore, regarding Fig. 1, I am skeptical about the second-row examples: such patterns could naturally arise from any sliding-window training process, and thus may not genuinely indicate anomalous behavior.\n\n2. **Writing and presentation.**\n   The narrative flow of the paper is not particularly convincing. For instance, the introduction of the signal-to-noise ratio (SNR) is abrupt and lacks conceptual grounding, despite being the core of the entire framework. Merely pointing out the potential limitations of previous scheduling methods is insufficient; the authors should elaborate on how SNR-based scheduling specifically benefits time-series data.\n   In addition, Fig. 2 provides only a coarse overview. A more detailed modular diagram or code block would be valuable for understanding the system design.\n\n3. **Methodology.**\n   The claim made in the *Introduction*—that *“this new scheduler induces the model to learn normal patterns across diverse, SNR-calibrated corruption levels, enhancing robustness to noise perturbations and temporal heterogeneity”*—is intriguing but not well supported. I am not convinced that a more linear ($\\alpha$)-curve (as shown in Fig. 2) would directly yield these benefits.\n   Moreover, the first module of the method seems fully general and could be applied to computer vision or other domains, with no specific connection to time-series data. In contrast, the second module, *Diffusion Step Alignment*, appears to disrupt the previously optimized noise-level alignment, which raises additional concerns.\n\n4. **Experiments.**\n   When reading the *Experiments* section, I was expecting to see visualizations of real-world data similar to those in Fig. 1, which would strongly support the paper’s central claims—but none are provided. The main experiments focus solely on point anomaly detection, with weak evaluation on localization tasks.\n\n5. **Reproducibility.**\n   Although the authors state that they plan to release the code in the future, the current submission does not provide sufficient implementation details to ensure reproducibility.", "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)