Enhancing Sparse Event Detection in Healthcare Time-Series via Adaptive Gate of Context–Detail Interaction
Authors: Beomjun Bark
Yun Kwan Kim
Venue: ICLR 2026 Poster (2026)
Keywords: Event detection
Time series analysis
Healthcare
Primary area: learning on time series and dynamical systems
TL;DR: We propose an adaptive gating framework that improves sparse event detection in healthcare time-series by selectively fusing context and detail features.

Abstract: Accurate detection of clinically meaningful events in healthcare time-series data is crucial for reliable downstream analysis and decision support. However, most existing methods struggle to jointly localize event boundaries and classify event types; even detection transformer (DETR)-based approaches show limited performance when confronted with extremely sparse events typical of clinical recordings. To address these challenges, we propose a coarse-to-fine detection framework combining a global context explorer, a local detail inspector, and an adaptive gating module (AGM) that fuses multiple label perspectives. The AGM uses transformed labels—encoding event presence and temporal position—to improve learning on sparse events. This design acts as a switch that selectively activates detailed feature extraction only when an event is likely, thereby reducing noise and improving efficiency in sparse settings. We evaluate our framework on diverse healthcare datasets—including arrhythmia detection, emotion recognition, and human-activity monitoring—and demonstrate substantial performance gains over existing DETR-based models, with particularly strong improvements in sparse event detection. With precise and robust event detection, our framework enables interpretation and actionable insights in real-world clinical applications.

Published as a conference paper at ICLR 2026
ENHANCING SPARSE EVENT DETECTION IN HEALTHCARE TIME-SERIES VIA ADAPTIVE GATE OF CONTEXT-DETAL INTERACTION
Beomjun Bark
Yun Kwan Kim
Seers Technology pbj7698 (eaelmmmmll...... com
Seers Technology, Korea University ykwinθkorea.co ac.kr
ABSTRACT
Accurate detection of clinically meaningful events in healthcare time-series data is crucial for reliable downstream analysis and decision support. However, most existing methods struggle to jointly localize event boundaries and classify event types; even detection transformer (DETR)-based approaches show limited perfor- mance when confronted with extremely sparse events typical of clinical record- ings. To address these challenges, we propose a coarse-to to-fine detection frame- work combining global context explorer, local detail inspector, and an adap- tive gating module (AGM) that fuses multiple label perspectives. The AGM uses transformed labels encoding event presence and temporal position-to improve learning on sparse events. This design acts as a switch that selectively activates de- tailed feature extraction only when an event is likely, thereby reducing noise and improving efficiency in sparse settings. We evaluate our framework on diverse healthcare iiausstt-iicludiing arrhythmia detection, emotion recognition, and human buman--attvvtyy monitoring and demonstrate substantial performance gains over existing DETR-based models, with particularly strong improvements in sparse event detection. With precise and robust event detection, our framework enables interpretation and actionable insights in real-world clinical applications.
INTRODUCTION
Time-series data are generated across diverse physical, biological, and socio-economic systems, exhibiting characteristics that vary substantially depending on their source. Among these, biosig- nals-electrical or physical measurements of physiological activity-are particularly distinctive, combining inherent temporal periodicity with irregular, sparse, and clinically significant local events, and they are typically acquired at high sampling rates (Yuan et al., (023). For instance, arrhythmia episodes in electrocardiograms (ECG) or the onset and offset of specific physiological behaviors must be captured with high temporal precision, cal decision-making Detecting such events therefore requires not only distinguishing normal from abnormal segments but also predicting both the event type (class) and its temporal boundaries- pability essential for early diagnosis, continuous monitoring, and real-time alert systems (Figure.
accurate timing is critical for clini-
Previous studies have explored various approaches for time-series event detection. A common strat- egy is to leverage unsupervised or semi-supervised anomaly detection methods (Ergen & Kozat 2019 Braci Wagner 2020 Qiao al. 2024:) McIntosh Albu (024), which effectively identify abnormal segments but provide little information about what type of event is occurring. Another line of work frames detection as classification problem over fixed-length windows (e.g., 10 seconds).
These methods predict whether an event occurs within each segment (Bark et al. 2023 Mandala al. (2255] Saranya al. (2025), particularly in studies involving biosignal- based events or disease detection. However, such window-based formulations cannot capture exact onset and offset times, limiting their ability to perform boundary-awar aware event detection within fixed segments.
"Corresoondigg author.

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Published as a conference paper at ICLR 2026
(a) Anomaly Detection Time Series
(c) Multi-Event Detection Time Series
Abnormality
Class
Class
(b) Classification Time Series
Input
Event Detection
Event class
Framework
Start time
(Cles Boundary)
End time
Figure 1: (a) Anomaly detection identifies abnormal segments without event types. (b) Classification labels fixed-length time windows, without explicit boundary localization. (c) Multi-event detection predicts both event class and temporal boundaries within each segment, enabling accurate detection.
Overall, existing approaches face two key challenges: (1) learning the type of events under sparse conditions, and (2) accurately localizing event boundaries. In clinical applications, these challenges are particularly critical, as failure to identify the onset, offset, and type events can prevent timely interventions. Moreover, providing explicit temporal and class information allows clinicians to focus on specific suspicious segments rather than reviewing entire recordings, thus improving workflow efficiency, supporting evidence-based decision-make making, and generating actionable insights. Conse- quently, previous methods are limited in their ability to perform event detection in time-series data, highlighting the need for accurate, boundary-awar detection that facilitates both reliable interpreta- tion and clinical actionability.
To address these challenges, propose novel framework for time-series data composed of two key units: (i) an adaptive gating module that facilitates dynamic interaction between global and local features, and (ii) a coarse-to- fine global-local structure built upon the detection transformer (DETR) architecture (Carion et al., 2020; Sarlin et al. (019).. This design jointly predicts event types and their temporal boundaries, enabling effective learning from sparse events. During training, the model leverages multiple label perspectives-inellee cations-whie which allows robust detection even under extreme event sparsity. We validate our approach on healthcare time-series datasets, including arrhythmia detection, emotion recognition, and activity monitoring, demonstrating consistent performance across diverse multi-event detection tasks.
actual values, event existence, and occurrence lo-
The main contributions of this work are summarized as follows:
We propose a time-series multi-event detection framework based on DETR that simultane- ously estimates event types and temporal boundaries, enabling effective interaction between contextual and detailed features.
We introduce an adaptive gating module that enables effective detection of sparse events by transforming labels emphasize different aspects sspects-such event existence occurrence locations locations-allowing the model to learn complementary temporal cues.
We demonstrate the clinical applicability of our method through diverse experiments on healthcare datasets, showing its effectiveness for real-world multi-event detection tasks.
2 RELATED WORK
2.1
SIQUEENCE-TO-SQQUERR MODELS FOR EVENT DETECTION
Seq-to-seq models for time-series analysis have evolved rapidly, starting from LSTM (Graves) 2012). TCN (Bai et al. 2018), and Inception Time (Ismail Fawaz et al., 2020), and more recently advancing to Transformer- 2023) and time-series-specife foundation models (Garza et al. 2023 Das et al. 2024h. While these models are effective at capturing global patterns and performing point point-wise prediction or regression, they face structural limitations for event boundary detection within segments. Point-wine wiitt-wise outputs inherently insufficient for tasks that require identifying both event onset and offset. Moreover, these models well-suited for detecting sparse events, where occurrences rare and irregular.
architectures (Vaswani et al.] 2017 Nie et al. 2022 Liu et al.,
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Published as a conference paper at ICLR 2026
2.2 END-TO-END DETR MODELS FOR EVENT DETECTION
Several approaches have been proposed for event detection, among which DETR (Carion et al, 2020) has become one of the most widely adopted and influential architectures. DETR introduced the first end-to-end transformer-base
framework for object detection, replacing traditional anchor- based pipelines. Subsequent variants, such as Deformable DETR (Zhu et al. 2020), DINC" Zhang et al. 2022), and DEIM- (Huang et al., 2025). improved both convergence speed, accuracy, and training effciincy. However, these advances have largely been limited to the image domain. In time-series data, most timestamps correspond to normal states, with events occurring sparsely and exhibiting ambiguous temporal boundaries (Azib et al. 2023 Zamanzadeh Darban et al.) (024). These characteristics introduce a significant mismatch between conventional DETR architectures and the requirements of boundary-aware event detection in time-series data. Direct application of DETR often yields inaccurate predictions and inefficient training under extreme sparsity.
3 METHODOLOGY
Our framework processes univariate multivariate time-series data using end-to-end en- coder-decoder architecture. The components of the proposed framework and their operating prin- ciples, including the overall weight flow, described below. A schematic overview of the full framework is shown in Figure The input and output are defined as:
X XE R - Y = {(c,, on foff )
(I)
where B is the batch size, T is the segment length, and D is the number of variables (D = 1 for univariate, D > for multivariate time-series data). The output Y is set of N events, where o; denotes the event class, and 4h, a represent the onset and offset times of the i-th event, respectively.
3.1 ARCHITECTURE OVERVIEW
We use a frozen foundation model (FM), denoted fFM, based on the Chronos-T5 tiny-bolt architec- ture (Ansari et al. 2024) pre-trained on large-scale time-series data. The FM serves lightweight backbone. Since Chronos-Tr T5 was originally designed for univariate time series, the framework em- ploys flexible input structure that expands contracts the FM according the input dimensional- ity, allowing it to process multivariate time-scries data. For a multivariate input, the representation constructed
D
JPM (X.s) E (xTx(x))
j=1
where + denotes channel-wise concatenation, B is the batch size, X., is the j-th input channel, T is the number of time steps in the FM output, and dFM is the embedding dimension of a single channel.
The embedding vector z extracted from the FM(Sex) is first mapped to an integrated representation h Bx through dings. The representation h then provides the foundational input to two feature extraction modules: the global context explorer (GCE) and the local detail inspector (LDI), which extract global and local temporal features, respectively. each with its own positional embeddings. Since GCE and LDI follow different processing paths, their outputs may reside in distinct feature spaces; an alignment layer is used to map all features into common space.
network (FFN) comprising linear layers and positional embed-
Next, the core component of the framework, the adaptive gating module (AGM), fuses the aligned features by modulating their contributions using a conditional gate scaler (CGS) and positional Gaussian injection (PGI), based on the global and local distributions of each sample. Specifically, the AGM computes gate values for each time step, controlling the relative contributions of GCE and L.DI features. Finally, the gated features are passed to the transformer decoder along with set of learnable object queries Q {q1, 92..... gN well as their class labels for each segment.
to predict both the start and end times of events as
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection DDIMM: DETR with Improved Matching for Fast Convergence
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Adaptive Gating Module: g
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Kinelaila 7
TCN- Attentino Kensel
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Start time
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Figure 2: Overview of the proposed framework. The input time-series data is first processed by frozen foundation model (FM), followed by a ferwaforwwrd network (FFN). Global and local tempo- ral features are then extracted via the global context explorer (GCE) and local detail inspector (LDI), respectively. The GCE and LDI outputs are fused through the adaptive gating module (AGM), which acts dynamic gate to modulate global and local information. The fused representation is subse- quently fed into the transformer decoder to predict event types and their temporal boundaries.
3.2 GLOBAL CONTEXT EXPLORER & LOCAL DETAIL INSPECTOR
The model operates on three complementary inputs: (i) the initial feature representation h, (ii) the global context extracted by the GCE, and (iii) the local detail extracted by the LDI. The GCE (GGCR E RBxxxd) and LDI (fi.DI E Bxrxd] are composed of a similar block structure com- prising a transformer encoder, TCN-based attention mechanism with dilation rates 21, 4, 8). and layer normalization, providing complementary perspectives for event representation in time series. To ensure consistency across these heterogeneous inputs, all three inputs are projected into a unified representation space through a linear alignment layer. Specifically, the initial features are aligned as
allggn Align(h),
while the outputs of GCE and LDI are also maintained the aligned space, which normalizes seale and distribution across inputs, unifies feature dimensions for consistent integration, and facilitates semantic alignment between global and local features, inspired by (Lin et al. 2017).
Building this aligned representation, the coarse-to-fine hierarchy is established not only through the different kernel sizes (7 for GCE and for LDI) but also via two additional factors. First, LDI receives both the aligned input and the globally refined GCE output:
FLDI = halign ① fGCE(halig))
(4)
creating richer, higher-dinnn dimees-dimmniionall space that enhances its ability to capture fine fine-grained local interactions. Second, the sequential ordering of the modules GCE preceding ADI-enable GCE to extract coarse, long-range dependencies, after which LDI focuses refining these representations with local detail.
Because all features remain in the aligned space, downstream components such as the AGM can consistently integrate global and local information. This hierarchical, aligned processing is critical for accurate, boundary-aware event detection and stable model training.
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Published as a conference paper at ICLR 2026
(a) Conditional Gate Scaler
(b) Positional Gaussian Injection
Graend Gaseian Vdel
Event
XAGM
O alc
RAGM
Time Scries FM (fFw)
Buse
COnD(RAGM)
Cosine Similarity
Figure 3: Overview the adaptive gating module (AGM). (a) Conditional gate scaler (CGS) mit- igates class imbalance by predicting weights we emphasize sparse event features while down- scaling frequent ones. (b) Positional Gaussian injection (PGI) encodes event locations by aligning Gaussian-distribucee labels with input features via cosine similarity.
3.3 ADAPTIVE GATING MODULE
The AGM is core component of our framework (Figure. B). consisting of two sub-modules: con- ditional gate scaler (CGS), which adaptively modulates the contributions each feature stream to better capture events in time series, and positional Gaussian injection (PGI), which injects positional priors to enhance temporal localization of events. The imput the AGM is the fused representation from GCE and LDI, formulated as
2 AGM = Cross (JGCE, JIDII, #AGM E Bxrxd
(5)
Within the AGM, FAGM sequentially passes through the CGS and PGI submodules. The resulting output, denoted as outputrar- is then compressed through a ID convolution layer followed by sigmoid activation to generate the gate tensor:
Sigmoid Conv
g E TR Bxrx1
This gate tensor dynamically modulates the relative contributions of LDI and GCE via element-wise weighting:
lgaaedd = O FLDI + (1 (1-g) fACE.
element-wise multiplication.
(7)
In summary, CGS and PGI work in sequence produce g. enabling the AGM adaptively balance global and local information while preserving boundary precision for sparse event detection.
3.3.1 CONDITIONAL GATE SCALER
The CGS is designed to enhance the learning of sparse events by modulating the relative importance of features. It reflects distributional differences between event and non-event segments, assigning higher weights to event-related representations. In particular, CGS controls how much the LDI can leverage the global context extracted by the GCE, and therefore only foCe(hatign) is used as input.
For each input segment, we reinterpret the labels from a binary perspective (no event: O, event: 1) and simultaneously assign weight (0, 1) mitigate imbalance. Specifically, let To and rı denote the ratios of the two classes (c.g.. 10:1). The GCE features are fed through an FFN to predict the binary label, supervised by weighted cross-entropy loss LBCF, with class weights computed as:
1/ro
1/ri
we
1/ro + 1/ri
W1
1/ro + 1/ri
ensuring < Wo, Wi < 1, with the rarer class receiving the larger weight. The final output of the FFN serves the conditional scaling weight w., directly modulating the AGM input.
Specifically, for each sample, the AGM input FAG is element-wise scaled by this conditional weight 7AGM w. @ ZAGM. where AAGM R AxTxd As result, abundant (non-event) features are downscaled while sparse (event) features are emphasized, enabling adaptive rebalancing during training.
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Published as a conference paper at ICLR 2026
3.3.2 POSITIONAL GAUSSIAN INJECTION
The PGI module encodes event locations within a time series by introducing Gaussian-shaped super- visory signals. For each ground-truth event, we generate normalized Gaussian label distribution (gaussian) centered at the event midpoint, with the start and end points explicitly set to zero to delineate boundaries:
N(l;c,o²²
maxueltsita | o() 14 <<t<h lo
Mgaussian (1)
0,
tE (ta, tel
where is and be denote the start and end indices of an event defined by y(t) # c f+tt is the event center, and is proportional to the event length. Here, (1; represents the value of ID Gaussian distribution with mean and variance o², The normalization by the maximum value ensures the label distribution peaks at within the event.
2
This Gaussian supervision is deliberately designed to: (i) explicitly indicate sparse event centers, (ii) enhance the distinguishability of consecutive events, and (iii) prevent boundary ambiguity that arises when events are split across fixed-length segments. In this way, PGI provides the model with smooth boundary cues related to event onsets and offsets within each segment. It thus complements original discrete label representation, offering richer supervisory signal for event localization.
To incorporate this signal, input features to the AGM after CGS (ĀAGM E RBxrxd) are passed through trainable convolutional layer (d -> drM), while Gaussian labels are projected via the frozen FM (fFM E IR Bx Bx encoder into the same representation space. A cosine similarity loss aligns the two:
Conv(rAcna)n:: : STM (Uganssnnsnn [Con((AACM)ooll) (TFFM(/gaassinn)belll
Less = BT
(10)
where is the batch size and T the time-step length. The final PGI output is obtained by concate- nating the AGM input after CGS with its convolutional layer along the feature dimension,
Outputpor AAGM + Conv(RAGM) e Bxrx(dd-drv)
(11)
3.4 TRAINING OBEJECTIVE
Our framework is trained with three loss terms: (1) a cosine similarity loss (Lews between the Gaussian labels generated by the PGI module and the AGM convolutional outputs, (2) binary cross-entropy loss (Lace) applied to the CGS predictions, and (3) a detection loss (LDeteation) the transformer decoder, computed via Hungarian matching. The overall objective is given by
Lioull aaooss + LBCE + Leteecton
0.2, 3 = 0.1, = 0.7.
(12)
Unlike the DETR, where Hungarian matching is based on 2D bounding box position and size, tem- poral event detection requires a tailored cost. We therefore define the matching cost
cost(i.j) = Aols costes (i.j) + Aca Li Li(cs, o3) + Aien L1
(13)
where Cj2 Cj denote event centers and 4,4, their lengths. We set Adts Aetr Aian = 1:
The detection loss (LDetection) combines localization and classification terms, formulated as Lpesection = 5.0 Lice + 2.0 Les, where Lise is an L2 loss over centers and lengths, and Les is weighted cross-entropy loss. To mitigate class imbalance, the classification weights assigned inversely proportional to class frequencies, emphasizing sparse event classes while down- nown-weighting abundant normal segments.

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Published as a conference paper at ICLR 2026
4 EXPERIMENT
4.1 PERFORMANCE COMPARISON ON HEALTHCAAE DATASETS
Datasets We evaluate our model on four public databases covering arrhythmia detection, emotion recognition, and activity monitoring. Specifically; MIT-BIH (Moody & Mark 2001) and SHDB- AF (Tsutsui et al. 2025) for arrhythmia detection, WESAD (Schmidt et al. 2018) for emotion recognition, and OPP (Roggen et al. (010)) for activity monitoring. For arrhythmia detection, consider both specific arrhythmias (MIT-BLL BIT Class 3, SHDB-AF Class 3, MIT-BII Class 15)and broader categories (MIT-BIH Class 6, SHDB-AF Class 5). All datasets split into training, vali- dation, and test sets (8:1:1). Sampling frequency and channel count vary: MIT-BIH and SHDB-AF 256 Hz with and channels; WESAD is 200 Hz with 8 channels; OPP is H0 with 36 chan- nels. Time-series signals are first resampled, then interpolated to fill missing values, and finally segmented into fixed-length windows. Stratified sampling applied address class imbalance. Pre-procees Pre-processing details and class definitions are provided in Appendix
Baseline We compare our approach against six DETR-based baselines. We employ the same Chronos-T5 T5 model as a pre-trained, fixed feature extractor (backbone encoder) to adapt them to ID time-series inputs. The extracted embedding vectors are fed into the Transformer encoder of the respective DETR-style architectures, using ID) structure. Temporal coordinates (event center and length) are used to align with the time-series data. All models, including DETR (Carion al. 2020), Multi-scale DETR, reformable-DETR (Zhu et al., 2020), DAB-DETR (Liu al. 2022a), DN-DETR et al. 2022), and Deformable-DiNN (Zhang et al. (022), were trained for 100 epochs with batch size of 64, early stopping after five epochs without improvement, and learning rate of 3 (0.9, 0.999)).
x 10 using the AdamW optimizer (weight decay x 10 20= = Implementation details are provided in Appendix C
Metrics We evaluate performance using three complementary metrics: macro point-wise F1 (PW- F1), macro affiliation FI (AFFF1), and mean average precision (mAP). PW-F1 measures frame-level accuracy by converting predictions and ground-truth events into time-indexed label sequences. AF- Fl assesses event-level correctness by checking whether each predicted segment overlaps with ground-truth segment of the same class. Finally, mAP summarizes precision-recall behavior based on confidence scores, treating each event as a temporal segment analogous DETR-style detec- tion. Together, these metrics capture timing accuracy (PW-F1), segment alignment (AF-FI), and confidence-bart based detection quality (mAP). Detailed metrie definitions are Appendix
Table I: Overall performance comparison across multiple datasets. Bold indicates the best perfor- mance, while underlined values indicate the second-bes best performance. Full quantitative results in Appendix and qualitative examples of detected events are shown in Appendix
MIT-BIH
SHDB-AF
WESAD
OPP
Model
Metric
15
Class
Class
PW-FI
77.22
64.41
41.32
93.24
60.19
59.53
61.30
DETR
AF-FI
82.72
58.3
43.35
93:47
60.83
62.29
61.89
MAP
51.45
53.33
45.23
95.78
77.77
53.14
50.85
PW-F1
75.60
73.36
65.52
91.99
63.49
63.88
58.05
Multi-scale DETR
AF-FI
76.00
62.39
49.34
92.13
64.67
65.65
58.38
mAP
66.03
55.60
50.97
97.76
76.85
68.24
48.28
PW-FI
85.39
68.
$8.96
90.86
$3.33
62.69
61.05
Deformable
AF-FI
60.81
45.67
91.10
60.92
mAP
63.17
$2.59
52.16
97/72
74.18
66.59
$1.86
PW-FI
83.13
71.05
$3.50
93.11
51.52
$5.59
60.35
DAB-DETR
AF-I
60.77
46.03
58.58
60.88
mAP
65.57
54.19
46.85
98.96
86.16
72.75
50.30
PW-FI
77.82
66.82
62.17
91.59
71.92
66.44
57.00
DN-DETR
AF-FI
77.71
58.18
47.37
86.26
70.13
66.32
56.05
mAP
66.28
51.42
48.80
96.69
80.06
55.12
49.29
PW-FI
74.11
63.49
69.
Deformale-DINN
AF-FI
83.22
60.00
48.59
88.82
65.98
70.73
55. 80
mAP
72.05
52.20
46.92
96.00
83.65
69. 69.96
50.68
PW.FI
90.63
83.37
74.86
96.23
83.41
73.59
64.98
Ours
AF-FI
60.87
$2.85
96.09
74.29
62.81
mAP
77.66
57.54
44.55
97.38
85.83
65.19
60.07

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Published as a conference paper at ICLR 2026
Results T shows that model consistently outperforms all baselines both PW-F1 and AF-FI while maintaining competitive mAP. On MIT-BIH Class 3, our model achieves a PW-F1 of 90.63, exceeding other methods by +4.22-15.03 percentage points (%p). On SHDB-AF Class 5, it reaches an AF-FI of 83.78, surpassing baselines by +1 63.65-31.64 %p. Although PW-F1 and AF-F1 often conflicting, our model attains strong performance both metrics while sustaining high mAP, indicating that it is not tuned to single event type but captures both the temporal precision and continuity of diverse events.
Table 2: Summary of notable results for specific dataset events. Each row reports the performance for a specific event within each dataset and provides the corresponding ratio. Bold indicates the best performance, while underlined values indicate the second best performance. Full results for all classes are provided in Appendix
Dutuset (Event)
Radio (%)
Metric
DETR
Multi-scale DETR
Deformable DETR
DAB-DETR
DN-DETR
DINO
|| Ouns
MIT-BIH Class (AFI.)
0.91
PW-F1 AF-FI
64.02
77.39
58
60.51
81,90
77.17
77.39
72.30
71.04
66.65
79.13
78.01
84
84.58
MIT-BIH Class 15
(T)
1.32
PW-FI
AF-FI
33.33
0.60
44.44
38.31
30.00
34.19
28.06
32.26
58.97
$2.94
60.94
58.33
78.57
80.17
SHDB-AF Class (PAT &NOD)
43.84
AF-FI
29.75
38.65
22.88
12,50
37.69
24.54
67.26
WESAD Class 8 (Task I)
0.95
PW-F1
30.11
31.36
10.41
37.46
41,56
49.59
60.00
AF-FI
12.57
41.51
OPP Class 5 (Stand)
42.36
PW-FI
46.75
40.02
36.45
44.28
35.35
35.73
55.61
AF-F1
45.91
40.61
32,97
44.53
27.37
29.18
48.83
MIT-BIH 6
PW-F1
24.53
(Ventricular arrhythmia)
0.59
AF-FI
28.27
36.36
30.3
25.27
21.75
25.60
19.52
Highlights Table 2 highlights the distinctive strengths of our model on selected classes. Even for extremely sparse events, our method outperforms strong baselines, achieving combined PW- Fl and AF-FI improvements over Deformable-DINO ranging from 6.6 to 20.2 %p. Performance on non-sparse events (e.g., OPP Class 5 "Stand") is also competitive. For challenging cases such "Ventricular arrhythmia" in MIT-BIH Class 6, model attains high PW-FI but lower AF-F1. This indicates that while the precise boundaries of individual events may not always be perfectly captured, the model reliably detects event occurrence-a property particularly valuable in healthcare applications.
Table 3: Summary of model performance by event length. Event classes are positioned as short or long based their class spesssi mean event length (CMEL, in seconds) relative to the global mean event length (GMEL, in seconds), considering a fixed window length. This table provides a detailed comparison of performance metries for cases with short long events across multiple datasets, based on the GMEL. Bold indicates the best performance, while underlined values indicate the best performance. Full results for all classes are provided in Appendix F
Dosuset (Event)
CMEL (GMEL)
Metric
DETR
Multi-scle DETR
Deformable DETR
DAB-DETR
DN- DN-DETR
DINO
Ours
MIT-RIH Class (AFIB)
8.52
AF-F1
88.04
91.49
95.08
87.51
90.00
$1.44
87.31
94.81
87.91
96.67
MIT-RIH Class (SVTA)
(..19)
PW-FI
AF-FI
84.53
81.21
96.71
85.71
85.42
94.72
96.36
$8.42
94.24
85.22
87.11
96.43
$8.45
96.
MIT-BIH Class 15
2.95
PW-FI
6,63
44.64
69.51
21.55
63.31
$1.91
70.26
51.15
70.60
$0.05
MIT-BIH Class (VFL)
PW-F1
3.36
31.54
31.53
22.68
16.55
32.16
69.03
(..11)
AF-F1
15.37
17:43
13.27
22.93
9.38
11.69
10.27
SHDB-AL AF Class (AT)
9.61 (9.90)
PW-FI
42.69
$9.29
$8.20
38.28
32,81
65.82
$7.64
55.68
75.20
WESAD Class 8 (Stross)
(..4(
PW-F1
AF-F1
77.45
75.93
81,41
84.94
85.79
85.11
83.63
63.70
77.00 77.02
73.76
85.33
85.28
N6.57
86.58
OPP Class
6.80
PW-F1
83:46
79.13
76.22
84.78
79.69
81.31
85.27
82,58
N7.Me
86.67
Highlights Table emphasizes the model's performance with respect to relative event length. To account for class-dependent duration characteristics and to provide an objective, sparsity- independent evaluation, we adopt CMEL and GMEL as length- that the model generally achieves high performance regardless of event length. This trend is partic- ularly evident in the MIT-BIH Class 15 dataset, where the model outperforms baselines for both the SVTA whosse event duration exceeds the GMEL-and the P class, which is shorter than the
metrics. The results show
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GMEL. A notable exception is the VFL class, which contains extremely short events. Although the model exhibits outstanding PW-F1 performance (exceeding the previous best by 36%p), its AF-FI performance is lower than that of the comparison models. This suggests that while our model can accurately identify fine-grained points within very short events, its ability to detect an entire event instance unified and independent segment still leaves room for improvement.
4.2 ANALYSIS OF INTERACTION BETWEEN GCE AND LDI
To investigate how our model dynamically integrates multi-scale information, we analyzed interactions between the GCE and LDI under event and non-event conditions. High- dimensional outputs were aggregated via mean and normalized for visualization (Figure. 4
Motivation
Observation Figure 4 iillustrates the dynamic behavior the AGM. In stable conditions without events, the AGM output closely follows the coarse-grained GCE values, indicating reduced reliance on computationally expensive fine-gres aieeggriied representations such as LDI and primarily leveraging stable global information for efficiency. In contrast, when events occur, fluctuations in both LDI and GCE become pronounced, and the model switches to a GCE-LDI interaction strategy to accurately capture subtle changes and utilize rich LDI representations for improved predictive performance.
Insights These results highlight how the AGM leverages context-detail interactions to adaptively select feature scales: emphasizing efficiency by relying on coarse coarse-grained context in stable periods, while exploiting fine-grained detail to maximize sensitivity during critical events, This adaptive strategy particularly effective for sparse event detection in healthcare time-series data.
Interaction between LDI, GCE, and Gate (No Event)
Interaction between LDI, GCE, and Gate (lvvntt)
1.0
Nomulicd valie
0.8
Gate
0.5
Time sop
stop
Figure 4: Adaptive gating module selectively prioritizes feature scales, The normalized outputs of LDI, GCE, and AGM are shown over time. non-event periods (left), the gate closely follows the low-variability GCE, emphasizing stable global information. During events (right), the gate in- creases the contribution of bariibvarabilitty LDI, capturing subtle changes and improving robustness.
4.3 ABLATION STUDY
Quantitative Analysis We evaluate the contribution of the AGM and its two submodules the PGI and the CGS (GS-thhough joint quantitative and convergence analysis. Under three ablation settings (removing AGM entirely, removing CGS, or removing PGI), the full model consistently achieves the best performance across datasets and metrics (Table 4). Removing the AGM entirely leads to substantial performance drops in both PW-FI and AF-FI, highlighting the critical role of adaptive gating in integrating multi-scale temporal cues. The PGI-only and CGS-only variants maintain moderate performance, yet they exhibit clear degradation compared to the full model, in- dicating that each submodule contributes unique, complementary information. Overall, these results quantitatively confirm that the AGM effectively integrates complementary label perspectives and significantly enhances event detection performance.
Interpretive Analysis To further understand their roles during training, we analyzed epoch-wise trends (Figure. 5). The CGS-only model converges rapidly in early epochs by emphasizing event presence, whereas the PGI-only model, although slower to start, steadily refines predictions through positional information and eventually surpasses the CGS-only variant after about 15 epochs, achiev- ing higher final performance. This complementary behavior- CCG for early learning and PGI for line-grained refinement, with PGI ultimately yielding stronger results results-explaine the superior perfor- mance of the full model. Convergence curves of DETR-based baselines provided in Appendix

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Table 4: Ablation study of the AGM and its components. "w/o" denotes "without." Bold indicates the best performance, while underlined values indicate the second-best performance.
Model
Metric
MIT-BIH
SHDB-AF
WESAD
OPP
Class
Class 6
Class 15
Class
Class
Class 8
Class 5
PW-F1
76.46
60.51
51.36
w/o AGM
AF-FI
55.21
51.25
39.32
91.90
61.20
63.39
46.27
mAP
64.31
52.69
43.44
96.55
72.69
62.30
50.12
PW-FI
84.41
81.22
72.76
95.77
70.88
61.84
52.46
w/o COS, with PGJ
AF-FI
87.07
$9.98
45.65
95.52
79.78
63.43
45.12
mAP
55.41
45.50
96.52
$2.25
PW-FI
84.68
80.02
74.72
95.13
73.17
61.67
$4.78
w/o PGI, with CGS
AF-FI
82.93
58.23
44.81
95.02
73.41
63.71
46.67
MAP
71.69
54.89
45.68
96.53
70.68
63.55
53.31
PW-FI
90.63
83.37
74.86
96.23
83,41
73.59
64.98
Outs (Baseline)
AF-FI
85.96
60.87
52.85
96.09
83.78
74.29
62.81
mAP
77.66
57.54
44.55
97.38
85.83
65.19
60.07
Point-Wise Wise El score (PW-FI)
Affiliation F1 score (AF-F1)
meam Average Precision (inAP)
0.6
0.5
0.6
Volue
Value
0.5
Value
Busciin
0.2
Baciss
Basdinc
PGM)CC
PGM wio CGS, wia PGI
02-
AGM
weo CGS, with PGJ
wil CGS, wiah PGJ
with 0BS
with 0GS
0.1
with CGS
0.0
Epoch
Epoch
Epoch
Figure 5: Epsch-wise performance of the model on three metrics (PW-F1, AF-F1, mAP), illustrat- ing the contributions of CGS and PGI. Early in training (epochs 0-10), the CGS-only model ("w/o PGI, with CGS", green dashed) converges faster, emphasizing event presence. After 15 epochs, the PGI-only model ("w/o CGS, with PGI", orange dashed) surpasses CGS, achieving higher final performance by refining predictions based on positional information. This demonstrates the com- plementary roles of CGS and PGI in accelerating early learning and improving final outcomes.
CONCLUSION AND FUTURE WORK
We presented framework for detecting event types and boundaries in sparse time-series data by combining a global context explorer, local detail inspector, and adaptive gating module (AGM). Within the AGM, the positional Gaussian injection and conditional gate scaler provide complemen- tary cues, improving sparse-event detection. Our approach outperforms DETR-based baselines on healtheare datasets, enabling more accurate interval identification of clinically relevant events. In real-world scenarios, the proposed framework can be applied to continuous monitoring tasks by de- tecting events within fixed-length segments and then merging them to reconstruct full-length events, facilitating practical and scalable deployment (Appendix E).
However, the main limitation of this work lies in its evaluation, which was confined to healthcare datasets. As a result, the generalizability across different domains remains to be validated. Future work will address this by extending the evaluation to diverse sparse-event contexts. In addition, we employed a coarse-to-fine fo-ee encoder and DETR-style decoder to effectively detect sparse events, this design may introduce challenges for real-time service deployment. To address this, we aim to explore lighter backbones and model compression strategies. We also plan to incorporate additional types of contextual information- such as patient demographics or clinical history further en- hance the adaptability and interpretability of the gating module. Taken together, believe that by addressing the aforementioned limitations, our framework could be widely applicable across a variety of domains, including healthcare, finance, industrial monitoring, and beyond.
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REPRODUCIBILITY STATEMENT
The model architecture is described in the main text through equations and figures. Details of the datasets, evaluation metries, and model parameters used for implementation are provided in the Appendix. The complete source code and scripts to reproduce our framework are available at: https://githus: Stt.///awo..ao///ooo/////o------ Event -Detection
ACKNOWLEDGMENTS
This work was supported by the Technology Innovation Program (RS-2025-02306270, Develop- ment of digital healthcare device for Al diagnosis and monitoring of swallowing disorders based on EGG-SEMG-ACC complex biological signals) funded By the Ministry of Trade, Industry & Re- sources(MOTIR, Korea).
LLM USAGE
For preparation of this paper, we used a large language model (LLM) solely to refine and improve the clarity of written sentences. The LLM did not contribute to the research ideas, experimental design, data analysis, or implementation the methods described this paper.
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A DATASET
We evaluate our framework on four datasets spanning three representative tasks: (1) arrhythmia detection, (2) emotion recognition from biosignals, and (3) human activity monitoring. Each dataset is split into training, validation, and test sets with a ratio of 8:1:1, and the number of samples summarized in Table Given the severe class imbalance across datasets, we apply stratified sampling to ensure valid evaluation.
All datasets undergo a unified prepprccsssng pipeline comprising signal resampling and class as- signment. Resampling is necessary because the backbone model used in this study accepts inputs of fixed length. Labels are adjusted during resampling to preserve class information and maintain alignment with the signals. During data preparation, any missing values in the time-series signals were addressed through linear interpolation. If a particular signal multivariate time-series was entirely incomplete, the corresponding data sample would be removed from the analysis. Detailed pre-pros pre-processing steps and class definitions for each dataset are provided in the following subsections.
We further employ pre-trained foundation model (Chronos-TSs (Ansari al. 2024 as the back- bone, Chronos-TT T5 has not been trained on any the datasets used in this study, thereby preserving data independence.
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Table 5: Summary of datasets employed evaluate the proposed framework across arrhythmia detection, emotion recognition, and human activity monitoring tasks. Key statistics include dataset size, number of channels, sampling frequency, and the global mean event length (GMEL).
Dataset
MIT-BIH
SHDB-AF
WESAD
OPP
Dataset size Variable number
86,208
466,128
86,733
28,753
2
8
36
Sampling frequency GMEL
256 Hz
256 Hz
200
30
5.19 sec
9.90
9.41 sec
3.93 sec
A.1 MIT-BIH: MIT-BIH ARRHYTHMIA DATABASE
We utilize the MIT-BIH Arrhythmia Database (oody Mark 2001). widely used benchmark for arrhythmia detection, which contains annotations for 14 types of arrhythmic rhythms. For pre- processing, ECG signals were denoised using a 1-30 Hz band-pass filter and resampled to 256 for computational efficiency (Jeon et al., 2020). The signals were segmented into 10-second win- dows with I-second stride to increase data diversity. the 10-second length was chosen because is sufficiently long to yield promising results in identifying rhythm types and classifying arrhyth- mias in previous studies (Liu et al. 2022b). Since ECG signals can exhibit multiple simultancous arrhythmias, evaluate framework under several scenarios: (1) 3-class: AFIB and AFL detec- tion from normal rhythm, (2) 6-class: grouping the 14 rhythms into six categories- -Normal, Atrial Seaddyardia/Pace///uneeee
rhythm (B/P/J rhythm), Conduc- tion disorders, and Others, and (3) 15-class: using all rhythms individually. Class distributions and category definitions for the 6-class scenario are provided in Table 6
arrhythmia, Ventricular arrhythmia,
Table 6: Class distribution and category mapping in the MIT-BIH Arrhythmia Dataset. Each class represents specific rhythm type: normal sinus rhythm (Normal), atrial fibrillation (AFIB), atrial flutter (AFL), supraventricular tachyarrhythmia (SVTA), atrial bigeminy (AB), idioventricu- lar rhythm (IVR), ventricular flutter (VFL), ventricular tachycardia (VT), paced rhythm (P), sinus bradycardia (SBR), nodal rhythm (NOD), ventricular bigeminy (B), pre-excitation (PREX), second- degree atrioventrieular block (BII), and ventricular trigeminy (T). The "Category" row indicates the grouping used for the 6-class evaluation scenario. The "Ratio" " row shows the percentage of each class in the dataset, and the "CMEL" row reports the class-specifie
event length.
Class
Normal
AFIB
SVTA
IVR
NOD
PREX
BII
T
Category
0
1
2
3
3
4
4
4
5
Ratio (%)
73.07
9.18
0.91
0.24
0.1
0.17
0.17
0.25
7.63
2.08
0.32
2.90
0.86
0.81
1.32
CMEL (sec)
6.70
2.33
7.75
5.07
4.54
0.88
2.95
2.38
5,50
4.43
5.96
A.2 SHDB-AFF JAPANESE HOLTER ECG DATABASE FOR ATRIAL FIBRILLATION
We also evaluated our framework on the SHDB-AF dataset (Tsutsui et al., 2025). two-channel Holter ECG dataset annotated with various arrhythmia rhythms. Similar to MIT-BIH, we considered two settings: (I) 3-class task for detecting major arrhythmias (AFIB and AFL), and (2) a 5-class task corresponding to the four arrhythmia events in the dataset. For pre-processing. ECG signals were denoised using a band-pass filter (1-30 Hz) and resampled to 256 Hz. Unlike MIT-BIH, moving window approach was not applied, the dataset already provides sufficient coverage; signals were segmented into 10-second segments, as in the previous dataset. The class distribution and proportions summarized in Table
A.3 WESAD: WEARABLE STRESS AND AFFECT DETECTION
The WESAD dataset (Schmidt al. 2018 is publicly available database for stress and emotion
recognition using wearable devices. It includes six modalities: 3-axis accelerometer, ECG, EMG, EDA, temperature, and respiration. Originally recorded at 700 Hz, we resampled the signals to 200 Hz to match our backbone model. EMG was excluded due to insufficient resolution after resampling.
To increase data diversity, applied 10-second window (Islam & Washington 2023) with a 1-
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Table 7: Class distribution in the SHDB-AF dataset. Normal denotes normal rhythm, AFIB atrial fibrillation, AFL atrial flutter, AT atrial tachycardia, and PAT & NOD represent other supraventric- ular tachycardias and intranodal tachyeardias.
Class
Normal
AFIB
AFL
AT
PAT & NOD
Ratio (%) CMEL (sec)
78.57
19.56
1.54
0.31
0.03
9.91
9.88
9.61
9.45
second stride. The dataset consists of 8 subject states, and their distribution is summarized in Table Since states and 8 lack clear labels, we grouped them Task 2, and 3 for this study.
Table 8: Class Distribution in WESAD Dataset
Class
Transient
Bascline
Stress
Amusement
Meditation
Task
Task 2
Task 3
Ratio (%)
45.44
20.29
11.48 9.45
6.42 9.23
13.60
0.95
0.91
0.91
CMEL (sec)
9.61
9.62
7.61
8.06
7.53
A.4 OPP: OPPORTUNITY ACTIVITY RECOGNITION
The OPPORTUNITY (OPP) dataset (Roggen et 2010) is publicly available dataset designed for human activity recognition research. It contains multimodal recordings from wearable and environ- mental sensors, including inertial measurement units (IMU), accelerometers, and position sensors, collected in various daily-life scenarios. For our study, we selected 36 representative accelerometer signals from the dataset for analysis. Signals were used their original sampling frequency (30Hz) without resampling. To enhance data diversity, we applied sliding window approach with win- dow length 10 scconds and a stride of second. Although human activity recognition (HAR) datasets typically use short windows (Jaén- (Jaén-Vargas et al. 2022). during experiments we found that such short windows tend to perform more like classification rather than event detection. There- fore, adopted a relatively long window of 10 seconds (Mekruksavanich & Jitpattanakul 2022 Duan et al., 2023), as used in some HAR studies. Table lists the selected accelerometer signals along with their sensor locations. The OPP dataset provides annotations at multiple hierarchical levels. this study, we focused on the highest-level labels, corresponding to five distinct activity classes. These are summarized in To
Table 9: Selected accelerometer signals from the OPP dataset. Superscripts "AP and denote sensors at different positions on the same limb.
Sensor Location
Signals (X, Y, Z)
Right Knee (RKN^)
accX, accY, acc7. accX, accY, acc7
Hip (HIP)
Left Upper Arm (LUA")
accX, acc aceZ
Right Upper Arm (RUA.)
accX, accY, accZ
Left Hand (LH)
accX, accY, aceZ
Back (BACK)
accX, accY, accZ
Right Knee (RKN.)
accX, accY, aceZ
Right Wrist (RWR)
accX. accY, accZ
Right Upper Arm (RUA^)
accX, accY, accZ
Left Upper Arm (LUA.)
accX, accY, accZ
Left Wrist (LWR)
accX, acc Y, aceZ
Right Hand (RH)
accX, acc aceZ
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Table 10: Class Distribution in OPP Dataset
Class
Base
Stand
Walk
Sit
Lie
Ratio (%)
14.71
42.36 4.03
24.16 2.78
15.93
2.84
CMEL (sec)
7.29
6.80
B METRIC
In this study, we employed three evaluation metrics: point-wise F1 score (PW-F1), affiliation score (AF-FI), and mean average precision (mAP).
The point-wise Fl score (PW-FI) was computed by transforming the predicted events, defined by their start point, end point, and class label, into one-dimensional time-series sequence and comparing it with the ground-truth sequence. Let the sequence length be T. For each time point t , T', the predicted label D. and the ground-truth label y/ were compared point by point to calculate the number of true positives (TP), false positives (FP), and false negatives (FN) for each class. Then, the Fi-score was computed for each class, and the final macro point-wise Fl score was obtained by averaging across all classes equally. This metric evaluates the overall prediction accuracy across entire time series.
T.P.. PW-F1 2 T. FP. FN
(14)
PW-F1 1 C Σ PW-FI,
(15)
The affiliation F1 score (AF-F1) evaluates model performance at the event level. Unlike point- wise metrics, which measure accuracy at each time step. AF-FI quantifies how well predicted event segments align with ground-trute toouud segments, serving
complementary metric the point-wise F1 score. An event is defined by a start time, an end time, and a class label. A predicted event is considered true positive (TP) if belongs to the same class ground-truth event and its temporal span overlaps with the ground-truth segment. A false negative (FN) occurs when a ground-truth event is not predicted, and false positive (FP) occurs when a predicted event does overlap with any ground-truti event. The AF-FI score is computed per class as
T.TP.
AF-FI. 2 T FP. FN..
(16)
and averaged across classes using macro scheme:
C AF-FI = Σ AF-FI
(17)
c=1
This metric provides meaningful assessment for time-series event detection by evaluating the accuracy of predicted events in terms of both their class and temporal boundaries, accounting for cases where single event may be split into multiple events may be merged into one.
The mean average precision (mAP) measures model performance by first computing the area under the precision-recall (PR) curve for each class, referred to average precision (AP), and then aver- aging the APs across all classes. Originally widely used in object detection and DETR frameworks, mAP is employed here an auxiliary metric for evaluating event detection in time-series data. Here, each event is treated as temporal segment, and the PR curve is derived from the confidence scores of predicted events. As such, mAP captures not only the correctness of event predictions but also the quality of their ranking based on prediction confidence, offering a comprehensive assessment of overall model performance.
AP. = [ Po(R) dR
(18)
C
MAP C
Σ
AP.
(19)
0=
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Published as a conference paper at ICLR 2026
C IMPLEMENTATION DETAILS
C.I MODEL ARCHITECTURE
Our framework builds upon Chronos's tiny-bolt architecture (Ansari et al. 2024) and comprises three primary components: (1) a feature extractor, (2) an AGM, and (3) query-based decoder for event detection. The feature extractor consists of the GCE and the LDI. GCE captures long-range temporal dependencies, while LDI extracts local temporal patterns. The AGM contains two sub- modules: the CGS, which adjusts feature importance based on event presence, and the PGI, which encodes positional information of events. An overall forward-pass algorithm flow is provided in Al- gorithm 1 and detailed descriptions of each module, including their dimensions, are summarized in Table The model has approximately 11.0M-13 13.3M parameters depending on the number of input channels, making relatively compact. Considering the sparsity of events in time-series data, we set the number of queries to 10 during training. The GCE and LDI use kernel sizes of and 3, respec- tively, and the feature dimension was experimentally set to 40 for optimal performance. Full archi- tectural details, including layer dimensions, kernel sizes, and other hyperparameters, are provided in our GitHub repository (https: /tt:::/gtthuh com/ hbum ] CDI TS Event Deteet
Algorithm Overall Model Forward Flow
1: procedure FORWARDPAsS(Time Series Input X E BxxxxD)
2:
b Chronos Embedding and Initial Projection
3: Features, <- (hronossEmbed((x...d for d in 1..D
4:
+ FFN_Block FFN_Block (aaneaCaCateaatteeeaaeeee
5: halign AlignmentLayer(h)
6:
GCE: Global Context Explorer (fGCE)
7:
GCE GCE Block(haain))
8:
GC'aatig + AligamemLLayer(GCEE)
9:
LDI: Local Detail Inspector (fIDI)
10:
JLDI < Concatenate (halign. GCBalign)
11:
LDI + LDI_Block (LLD))
12:
LDJalign + AligmeenLaaer((LD0)
AGM: Adaptive Gating Module
14:
JAGМ +- CrossAAttention[I... [L.Dal GC Falig))
We +- CGS. BloCBBlackGGCaanng
16:
JAGM We AAG
17:
Outputper &- Concatenate((AAGm. Conv(ZAGM))
+ Sigmoid Conv Conv(outputpoci
19:
Meated LDD g) GCFalig
20:
>> DETR-Style Detection Head
Decoder TransformerDecoderr Target Query Queryembed Memory hggtte))
22:
Predboxes B BBoxPredictor Decoder.ut)
Pred ClassPredictor (ecoder.uu)
24:
return Predhoxess Prediogits)
25:
26:
end procedure
C.2 TRAINING PROCEDURE AND HYPERPARAMETERS
The model was trained for 100 epochs with batch size of and a learning rate of 1 x 10-4, Early stopping was applied, halting training if no improvement occured for five consecutive evaluations. The dataset was split into training, validation, and test sets in an 8:1:1 ratio using stratified sampling to preserve class distributions. We used the AdamW optimizer with weight decay of 5 10-2 c=1x 10 and 3 = (0.9, 0.999). The overall loss combined three components: Less- LECEs and Licscction with relative weights 2:1:7. For the Hungarian matching in detection, the class, center, and length costs were weighted 1:5:1, respectively. The Chronos model dimension was increased from 2048 to 4096. Random seeds were fixed to 42 ensure reproducibility across all experiments.
17

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Published as a conference paper at ICLR 2026
Table 11: Overview of the proposed model architecture.
Module
Pupose
Iepurt Cuiput
Descrition
ChrcondEmbed
Per channel Reature cocoding
[8,2:2] +> [M. v. AI deal
Pte Pre-crined emibedking for each chanael
FFN_Block
Comprias the enhhokling dimensian
|P, P d.ul - (P
Lineee projection with sommaliratinn and posticoul antholding Transfomerr encuder with TCNoriniioon and posttional emboolinng
OCF_Blokk
Capture global temporal fronures
1R.r.d 1,,
L.D.Block
Capturn local temporal frotures
18,,,34 -+ r,4
Transfomerr encuder with TCN-oneiiion and positional emboolingg
XGM (CGS PCD
Adaptive intrrctton
15.7.4 - [0...4]
Adjusts features based on event presence and positional encoding
Decnder
Query based event prediction
B..N.4 -+ [0.N.d]
Trursfommer decoder with learnable queries
Predictor
Event iecalcatoooclasiicatiinn
B.N.o. JM. classes)
Linear layers to predict event boundaries and labels
C.3 HARDWARE AND SOFTWARE SETUP
experiments were conducted on machine running Ubuntu 22.04, equipped with two Intel Xeon Scalable 6526Y processors (16 cores, 32 threads) and 256 GB DDR5 ECC RAM. Three NVIDIA Quadro RTX A5000 GPUs (24 GB GDDR6) were used for training and evaluation, Experiments executed within Docker container using Python 3.10 and PyTorch 2.3.0 with CUDA 11.8.
D COMPARISON OF CONVERGENCE SPEED WITH BASELINE MODELS
Although the primary goal of our model improved sparse event detection in time-series data, also demonstrates faster convergence compared to baseline models. To illustrate this, we compare the convergence behaviors up to 40 epochs (Figure (6, as most models terminate around this point due to early stopping. Specifically, we present the results for the mAP metric representative example.
Convergence Speed Comparison Across Models
Defamabl-DERR
DN-DETR DENO
Durt
0.5
mAV
Upochs
Figure 6: Convergence comparison of model against baseline models, including DETR, Multi- scale DETR, Deformable DETR, DAB-DETR, DN-DETR, and DINO. The plot shows mAp over training epochs. Our model consistently achieves higher mAP and reaches peak performance in fewer epochs, indicating both improved effectiveness and training efficiency.
E SAMPLE PREDICTIONS
We present representative examples of our framework's event detection results across multiple time- series datasets, For clarity, the signals are normalized within each class range, and selected cases are shown to illustrate overall trends (from Figure to Figure 13). Each figure compares the model's
18

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Published as a conference paper at ICLR 2026
predictions (Blue) with the ground truth (Red). The top waveform shows the original signal, while the two colored traces below indicate the occurrence of specific events.
Sample
Grend
Thmme
Figure 7: Sample Predictions in MIT-BIH Class 3.
Sample
Time
Figure 8: Sample Predictions in MIT-BIH Class 6.
Sample Predictions MTT BBB Class
Tudicko
Time
Figure 9: Sample Predictions in MIT-BIH Class 15.
Sample Prcdictions SHIB--FF Class
Padluke
Time
Figure 10: Sample Predictions in SHDB-AF Class 3.
19

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Published as a conference paper at ICLR 2026
Sample Predictions SIID AP
Pidiike Ored Iaa
Clovalal arpitale
Figure 11: Sample Predictions SHDB-AF Class 5.
Sample Prediction in WESAD Class
Detake Roed Ind
Clavaaded
Figure 12: Sample Predictions in WESAD Class 8.
Sample Prediction OPP Class
Tidiake
Clooadid arphakk
Time
Figure 13: Sample Predictions in OPP Class 5,
F FULL EXPERIMENTAL RESULTS
We report the full experimental results, including per-class PW-F1 and AF-FI scores, for all datasets from Table 12 to Table 18 A comprehensive summary and key results are presented in Section 4. I.
Table 12: Full Results: MIT-BIH 3 classes
Class
Ratio(%)
Metric
DETR
Multi scale DETR
Deformable DETR
DEB-DETR
DN-DETR
DINO
Ours
PW-F1
98.86
99.44
99.34
99.46
98.84
99.48
99.72
Base
89.91
AF-FI
PW-F1
90.42
93.08
88.99
94.81
96.67
AFIB
9.18
AF-FI
88.04
91.49
87.51
90.00
84.44
87.31
87.61
PW-F1
64.02
58.11
77.17
72.30
66.65
78.01
84,58
AFL
0.91
AF-FI
77.39
60.51
81.90
77.39
71.04
79.13
84.30
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Published as a conference paper at ICLR 2026
Table 13: Full Results: MIT-BIH 6 classes
Class
(atio(S)
Merric
DETR
Multi-scale DETR
Deformable DETR
DAB-DETR
DN-DETR
DINO
Ours
Normal
PW-FI
93.81
95.99
95,45
94.77
95.08
96.18
97.46
Atrial arbythmmia
10.43
PW-FI
74.78
86.82
91.08
76.90
91.57
78.29
87.78
89.99
77.32
91.93
95.23
Ventricular anhythmia
0.59
PW-FI
AF-FI
28.27
20.26
36.36
44.59
29.88
30.83
44.51
25.27
21.75
24.55
25.60
54.71
79.04
19.52
B/PJ rhyth
10.03
PW-FI
AF-F1
95.26
82.05
95.76
79.49
95.18
79.52
95.73
82.35
78.46
94.95
79.02
96.10
96.51
82.63
Conduction disorders
4.57
AF-F1
59.05
62.29
59.28
70.22
64.62
70.99
$7.91
61.04
68.91
PW-FI
44.60
62.45
56.55
59.03
$3,80
$3.31
61.23
AF-FI
47.30
58.85
57.52
53.30
56.88
57.02
54.75
Table 14: Full Results: MIT-BIH 15 classes
Class
Ratio(%)
Metric
DETR
Multi Multi-scale scale DETR
Deformable DETR
DAB-DETE
DN-DETR
DINO
Ours
Normal
73.07
PW-F1
87.89
91.95
93.17
94.50
AF-F1
PW-FI
78.43
91.38
83,29
84.54
86.66
89.34
91.60
AFIB
AF-FI
63.01
73.26
68.71
70.84
70.32
71.38
74.24
PW-F1
31,94
64.75
AFL
0.91
AF-FI
12.52
33.67
22.66
29.36
29.20
23.47
18.53
PW-F1
81,21
96.71
94.72
96.36
94.24
96.43
96.14
SVTA
0.24
AF-FI
85.42
88.45
PW-F1
91.82
81.67
85.02
74.59
97.08
95.61
97.01
AB
0.10
AF-F1
60.99
52.44
54,39
53.49
61.06
58.52
62.21
PW-FI
56.91
66.43
61.84
48.49
65.02
61.62
68.85
IVR
0.17
AF-FI
48.76
49.26
57.81
PW-F1
3.36
31,54
31.53
22.68
16.55
32,16
69.03
VFL
0.17
AF-F1
15.37
17.43
13.27
22.93
9.38
11.69
10.27
PW-F1
17.20
50.29
43.95
38.
32.80
41.18
46.29
VT
0.25
AF-1
46.23
PW-F1
6.63
69.51
71.55
63.31
70.26
70.60
80.05
AF-F1
37.60
44.64
50.83
51.91
50.25
51.15
55.79
PW-F1
83.52
93.48
93.66
95.65
93.42
94.44
89.46
SBR
2,08
AF-FI
70.71
74.87
73.30
75.56
75.39
67.96
PW-F1
27.49
44.10
31.31
21.93
40.99
33.63
40.98
AF-F1
24.05
30.97
31.11
15.95
33.72
26.52
34.42
PW-F1
2.14
43.97
30.83
30.95
29.45
34.88
48.68
2.90
AF-F1
2.09
13.13
14.50
57.14
11.31
14.59
16.59
PW-F1
64.86
80.80
70.54
76.71
65.37
62.03
81.12
PREX
AF-FI
59.57
66.67
48.78
49.23
41.67
50.00
65.00
PW-F1
61.13
47.82
93.72
BII
0.81
AF-FI
58.82
61.02
53.97
32.26
$3.97
63.49
63.83
PW-F1
0.60
38.31
34.19
28.06
58.97
60.94
80.1
1.32
AF-F1
33.33
44.44
30.00
32.26
52.94
58.33
78.57
Table 15: Full Results: SHDB-AF 3 classes
Class
Ratio(%)
Metrie
DETR
Multi scale DETR
Deformable DETR
DAB RAB-DETR
DN-DETT
DINO
Ours
PW-F1
98.83
99.23
99.03
98.96
98.77
99.06
99.48
Base
78.91
AF-FI
PW-F1
95.09
96.02
95.26
95.68
95.08
95.40
97.68
AFIB
19.56
AF-FI
95.75
96.42
95.81
96.15
84.95
94.11
97.84
PW-F1
91.39
87.95
86.45
90.54
88.10
83.74
94.78
AFL
1.54
AF-FI
91.19
87.85
86.38
90.31
87.57
83.54
94.33
21

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Published as a conference paper at ICLR 2026
Table 16: Full Results: SHDB-AFS 5 classes
Class
Ratio %6
Metric
DETR
Multi-scale DETR
Deformable DETR
DAB-DETR
DN-DETR
DINO
Ouri
Normal
78.57
PW-FI
97.31
92.53
97.36
96.59
97,90
98.51
99.29
AF-FI
PW-F1
89.74
83.55
86.02
85.21
92.01
94.49
97.33
AFIB
19.56
AF-FI
90.96
85.08
87.62
86.74
91.97
94.80
97.40
PW-F1
79.22
74.22
66.49
85.98
88.07
94.68
AFL
1.54
AF-FI
79.04
75.66
66.64
75.49
84,62
86.96
94,29
PW-F1
42.69
$8.20
38.28
32.81
65.82
55.68
75.20
AT
0.31
AF-FI
43.59
59.29
38.81
33.85
66.23
57.64
76.16
PW-F1 F1
29.12
37.80
22.51
12.50
43.84
25.85
66.41
PAT&NOD
0.03
37.69
Table 17: Full Results: WESAD 8 classes
Class
Ratio(%)
Metric
DETR
Multi-scale DETR
Deformable DETR
DAB-DETR
DN-DETR
DINO
Ours
PW-FI AF.FI
71.00
81.41
80.13
82.53
75.71
80.44
85.63
Transient
45.44
Baseline
20.29
PW Fl
72,95
83.15
82,89
83.91
77.29
78.30
81.59
80.11
84,25
PW-FI
75.93
84.41
85.44
83.63
77.02
85.28
86.58
AF-FI
77.45
84.94
85.79
83.70
73.76
85.33
86.57
70.81
57.49
Amusement
6.42
AF-FI
55,39
68.15
71.34
77.15
57.78
68.42
63,40
Meditation
13.60
PW-FI
89.42
90.54
90.41
90.96
89.06
89.09
88.44
AF.FI
89.79
90.63
91.17
91.19
88.90
89.87
89.04
PW FI
30.11
31.36
10.41
37.46
41.56
49.59
60.00
Task
0.95
31.69
12.57
44.40
41.51
50.97
PW-FI
39.84
50.21
50.64
13.26
58.17
48.6 61
56.42
AF-FI
44.15
56.38
55.66
16.80
62.28
$1.50
63.92
48.21
63.49
77.00
Task
0.91
AF-FI
58.55
43.83
56.72
11.31
62.75
67.45
74.78
Table 18: Full Results: OPP 5 classes
Class
Ratio(%)
Metric
DETR
Multi-scale DETR
Deformable DETR
DAB-DETR
DN-DETR
DINO
Ours
PW-F1
41.49
41.45
42,06
41.87
41.61
40.25
44.15
Base
14.71
AF-FI
PW-F1
46.75
40.02
36,45
44.28
35,35
35.73
55.61
AF-FI
45.91
40.61
32.97
44.53
27.37
29.18
48.83
PW-F1
26.98
28.87
34.44
28.14
24.73
20.74
28.
Walk
24.16
AF-FI
39.21
37.57
45.34
38.99
35.78
30.62
36.98
PW-F1
88.00
87.07
88.54
82.29
86.60
88.14
89.51
15.93
AF-FI
76.88
76.22
78.34
77.45
76.01
78.13
77.58
PW-F1
83.46
76.22
84.78
79.60 99.69
81.31
82.58
86.67
Lic
2.84
AF-FI
85.58
79.13
87.03
82.54
85.03
85.27
87.86
22


Reviews:
[{"review_id": "8ZdaVVe42u", "reviewer": "Reviewer_xGKy", "summary": "This paper presents a novel approach for enhancing sparse event detection in healthcare time-series data through the use of an adaptive gate mechanism. The proposed method combines global context exploration, local detail inspection, and an adaptive gate module to improve the precision of event localization in medical datasets, such as ECG recordings. The authors have conducted experiments on various healthcare datasets and demonstrate substantial improvements over existing methods in terms of sparse event detection performance.", "questions": "How does the proposed method handle cases with missing or incomplete data, particularly in sparse time-series recordings?\nCould the adaptive gate mechanism be further enhanced by incorporating additional types of context information (e.g., patient demographics, clinical history)?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "The introduction of the adaptive gate mechanism (AGM) is a significant contribution to sparse event detection. By leveraging both global and local perspectives, the method addresses key challenges in event localization. The explanation of the proposed framework and the AGM module is clear and well-structured. The authors provide sufficient details on the model's design and operational principles. The experiments on diverse healthcare datasets, including ECG signals, are comprehensive and effectively demonstrate the superiority of the proposed method over existing approaches. The results are promising and support the validity of the approach.", "weaknesses": "How does the proposed method handle cases with missing or incomplete data, particularly in sparse time-series recordings?\nCould the adaptive gate mechanism be further enhanced by incorporating additional types of context information (e.g., patient demographics, clinical history)?", "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": "v7F8B7svSI", "reviewer": "Reviewer_hGZQ", "summary": "This paper proposes a coarse-to-fine detection framework for enhancing sparse event detection in healthcare time-series data. The framework combines a Global Context Explorer (GCE) and a Local Detail Inspector (LDI) with an Adaptive Gating Module (AGM). The AGM uses Positional Gaussian Injection (PGI) for refined temporal localization and a Conditional Gate Scaler (CGS) for adaptive rebalancing of sparse event features. The model leverages multiple label perspectives during training and employs a DETR-based architecture for joint event type and boundary prediction. Evaluated on diverse healthcare datasets (arrhythmia, emotion, activity monitoring), the proposed method demonstrates substantial performance gains, particularly for sparse events, over existing DETR-based baselines.", "questions": "1.  **Event Handling at Window Boundaries:** Given the 10-second windowing approach for continuous time-series, how are ground-truth events that are longer than 10 seconds or that span across window boundaries specifically annotated and handled during training and evaluation? Does this approach risk truncating events or causing ambiguities in boundary detection, especially for a framework aiming for \"precise event boundary detection\"?\n\n2.  **Precise Generation of Gate Tensor `g`:** The paper states that \"the AGM produces a gate tensor `g`... that controls the relative contributions of LDI and GCE\" . However, the exact mechanism or specific layers (e.g., what output features, linear layers, and activation functions) through which this `g` tensor is computed from the features (e.g., after PGI and CGS) is not explicitly described. Please clarify the precise generation process of `g`.\n\n3.  **TCN Dilations for GCE and LDI & Alignment Layer Details:** To substantiate the \"coarse-to-fine\" distinction between GCE and LDI, please specify the dilation rates used in their respective TCN-Attention mechanisms. This is crucial for understanding how kernel sizes 7 and 3 translate into genuinely \"global\" and \"local\" effective receptive fields. \n\n4.  **Clarification of Notation and Consistency:** Please provide clear and explicit definitions for all variables used in equations (e.g., `N` in Eq. 1, `B` in Eq. 2) early in the methodology section. Ensure consistency in input definitions, specifically by including the batch dimension `B` in the definition of input `X` (Eq. 1). Also, clarify the meaning of the subscript `b` in $w_b(t)$ (Eq. 6).", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1.  **Targeting a Critical Problem (Sparse Event Detection in Healthcare Time-Series):** The paper addresses a highly relevant and challenging problem in healthcare—detecting rare, clinically meaningful events with precise boundaries in complex time-series data. This problem is clearly articulated as a limitation of existing methods.\n2.  **DETR-based End-to-End Framework:** Adopting and adapting the DETR architecture for time-series event detection, combined with a coarse-to-fine feature extraction, offers an elegant end-to-end solution. The tailored Hungarian matching cost for temporal events is a thoughtful adaptation.\n3.  **Comprehensive Evaluation:** The framework is rigorously evaluated on four diverse healthcare datasets across three different tasks (arrhythmia, emotion, activity recognition) and various class evaluation scenarios, demonstrating consistent performance gains over multiple strong DETR baselines. The use of PW-F1, AF-F1, and mAP provides a holistic view of performance.", "weaknesses": "1.  **Ambiguity in Time-Series Windowing and Event Truncation:** The use of fixed 10-second windows with a 1-second stride for long-duration healthcare time-series (e.g., Holter ECG) raises concerns. The paper lacks a clear explanation of how events that span across window boundaries are handled, potentially leading to event truncation or inaccurate boundary detection, which contradicts the goal of \"precise event boundary detection.\" This is a critical aspect for reproducibility and clinical applicability.\n\n\n2.  **Underspecified \"Adaptive Gating Module\" (AGM) Mechanism:** The core component, the AGM, specifically the precise mechanism for generating the `g` gate tensor (e.g., what layers/activation functions are applied to which input features to produce `g` as a R $\\in$ B×τ×1  tensor), is not explicitly detailed. This lack of clarity hinders the understanding of a key novelty and impacts reproducibility.\n\n\n3.  **Lack of Clarity in GCE/LDI \"Coarse-to-Fine\" Distinction and TCN Details:** The distinction between \"global\" (GCE) and \"local\" (LDI) features is primarily attributed to TCN kernel sizes (7 vs. 3). However, **without specifying the dilation rates** used in their TCN-Attention mechanisms, it's unclear if this difference is substantial enough to justify the \"coarse-to-fine\" claim, especially given the inherently short window length. Furthermore, the detailed operation and structure of the \"FFN-based alignment layer\" are insufficiently explained.\n\n4.  **Information Dispersal and Lack of Self-Containedness:** Several crucial details, such as full metric definitions, complete quantitative results tables (beyond highlights), detailed dataset class distributions for specific class evaluation scenarios (e.g., \"Class 3\", \"Class 6\", \"Class 15\" for MIT-BIH), and specific hyperparameters/adaptation methods for baselines, are mostly relegated to the Appendix. This significantly impedes the reader's ability to grasp the main content and evaluate the results. Key figures like Table 1, Table 2, and Figure 2 (which is overly complex and lacks sufficient labeling for intermediate representations like `z` and `h`) are not self-contained, forcing constant reference to the appendix and hindering streamlined understanding.\n\n5.  **Inconsistent and Undefined Notation:** Several fundamental variables used in equations, such as the batch size `B` (Eq. 2), the total number of events `N` (Eq. 1), and the subscript `b` in $w_b(t)$ (Eq. 6), are used without explicit definition early in the methodology. Furthermore, the input definition for `X` (Eq. 1) does not include the batch dimension `B`, leading to an inconsistency with later equations that use `B`.\n\n6.  **Limited Discussion on Clinical Interpretability/Actionability:** While the abstract and introduction mention \"actionable insights\" and \"reliable interpretation\" in real-world clinical applications, the paper's analysis is predominantly quantitative. A deeper discussion or qualitative analysis on *how* the improved event detection truly translates into concrete clinical benefits (e.g., enabling earlier alerts, more precise diagnosis, or better treatment planning) would strengthen its real-world impact claim.", "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": "qg7NbKZMna", "reviewer": "Reviewer_HDHf", "summary": "This paper introduces a lightweight deep learning model for time-series event detection with a novel evaluation metric called Affiliation F1 score (AF-F1). The proposed model builds upon Chronos's tiny-bolt architecture and consists of three primary components: a feature extractor (comprising GCE for capturing long-range temporal dependencies and LDI for extracting local temporal patterns), an Adaptive Interaction Module (AGM) containing CGS (adjusts feature importance based on event presence) and PGI (encodes positional information), and a query-based decoder for event prediction. The AF-F1 metric evaluates model performance at the event level by measuring how well predicted event segments align with ground-truth segments, considering both class labels and temporal boundaries. The model achieves a compact size of 11.0M-13.3M parameters and demonstrates strong performance on event detection tasks.", "questions": "No more questions", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The introduction of AF-F1 as an event-level metric is a significant contribution, addressing a critical limitation of point-wise metrics by considering both temporal alignment and class accuracy, which is particularly valuable for event detection tasks.\n2. The separation of components for different temporal scales (GCE for long-range dependencies, LDI for local patterns) demonstrates thoughtful design that effectively captures diverse temporal features.\n3. The use of both AF-F1 and mAP as complementary metrics provides a more complete assessment of model performance, capturing not only event correctness but also the quality of prediction ranking based on confidence scores.", "weaknesses": "1. The evaluation appears to be limited to a single dataset (implied but not explicitly stated), which may not adequately demonstrate generalizability across different domains or event types.\n2. There's no detailed analysis of model performance across different event types (e.g., short vs. long events, frequent vs. rare events), which could reveal important performance characteristics and limitations.", "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": "KNWBY1Mp0K", "reviewer": "Reviewer_FGhW", "summary": "The paper proposes a DETR-inspired framework for sparse event detection in healthcare time-series. It introduces a dual-branch encoder—a Global Context Explorer and a Local Detail Inspector—fused through an Adaptive Gating Module (AGM) that dynamically balances global and local information. To handle extreme event sparsity, the authors design Positional Gaussian Injection for soft temporal supervision and a Conditional Gate Scaler to mitigate class imbalance. Experiments on several physiological datasets show consistent improvements over DETR-based baselines, achieving more precise event localization and better data efficiency.", "questions": "NA", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper’s novelty is moderate but meaningful. While the framework builds on DETR and existing ideas such as global–local fusion, adaptive gating, and soft temporal labels, these components are integrated with notable care. The design is elegant and well aligned with the characteristics of healthcare time-series—sparse, imbalanced, and context-dependent. The adaptive gate offers interpretable behavior, and the Gaussian label smoothing plus conditional weighting show clear practical insight. Overall, the work is not conceptually radical, but it represents a thoughtful and well-executed adaptation that brings tangible progress to sparse event detection in medical data. \n\nThe presentation is clear and well-structured, making the technical ideas easy to follow. Overall, this is a thoughtful and well-executed adaptation that combines solid engineering with practical relevance.", "weaknesses": "The main weaknesses are twofold. \n\nFirst, the method’s generalization remains limited. It still requires dataset-specific fine-tuning, and the learned gating behavior may not transfer well across domains with different event sparsity or temporal patterns. \n\nSecond, the architecture is relatively heavy, combining dual-branch encoders, gating, and DETR-style decoding, which raises computational cost and makes real-time healthcare deployment challenging.", "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)