CogMoE: Signal-Quality–Guided Multimodal MoE for Cognitive Load Prediction
Authors: Aamir Bader Shah
Yu Wen
Renjie Hu
Jiefu Chen
Jose L Contreras-Vidal
Xuqing Wu
Xin Fu
Venue: ICLR 2026 Poster (2026)
Keywords: Cognitive-load
multi-modality
mixture-of-experts
Primary area: applications to neuroscience & cognitive science
TL;DR: We propose an adaptive mixture-of-expert framework for cognitive load prediction on multi-modal physiological data.

Abstract: The poor and variable quality of physiological signals fundamentally constrains reliable cognitive load (CL) prediction in real-world settings. In safety-critical tasks such as driving, degraded signal quality can severely compromise prediction accuracy, limiting the deployment of existing models outside controlled lab conditions. To address this challenge, we propose CogMoE, a signal-quality–guided Mixture-of-Experts (MoE) framework that dynamically adapts to heterogeneous and noisy inputs. CogMoE replaces conventional modality-based fusion with a quality-aware gating mechanism that integrates EEG, ECG, EDA, and gaze according to their estimated signal quality, shifting the basis of multimodal modeling from modality identity to signal quality. The framework operates in two stages: (1) quality-aware multimodal synchronization and recovery to mitigate artifacts, temporal misalignment, and missing data, and (2) signal-quality-specific expert modeling via a cross-modal MoE transformer that regulates information flow based on signal quality. To further improve stability, we introduce CORTEX Loss, which balances task accuracy, quality-aware representation refinement and expert utilization under noise. Experiments on CL-Drive and ADABase demonstrate that CogMoE outperforms strong baselines across all modality combinations and sequence lengths, consistently delivering improvements across diverse signal-quality conditions. Our code is publicly available at https://github.com/shahaamirbader/CogMoE.

Published as a conference paper at ICLR 2026
CoGMoE: SIGNAL-QUALIT--GUDEE MULTIMODAL MOE FOR COGNITIVE LOAD PREDICTION
Aamir Bader Shah" 1 Yu Wen 2 Renjie Hu', Jiefu Chen', Jose Contreras- .Xuqing
Wu', Xin Fu University of Houston
2tony Brook University
ABSTRACT
The poor and variable quality of physiological signals fundamentally constrains reliable cognitive load (CL) prediction real-world settings. safety-criticn tasks such as driving. degraded signal quality can severely compromise prediction accuracy, limiting the deployment of existing models outside controlled lab con- ditions. To address this challenge. we propose CogMoE, a nignal-quality-guiiee Eexture-of-Experee (MoE) framework that dynamically adapts to heterogeneous and noisy inputs. CogMoE replaces conventional modality-based fusion with a quality-aware gating mechanism that integrates EEG, ECG, EDA, and gaze according to their estimated signal quality, shifting the basis of multimodal modeling from modality identity to signal quality. The framework operates in stages: (1) quality-aware multimodal synchronization and recovery to mit- igate artifacts, temporal misalignment, and missing data, and (2) signal-quality- specific expert modeling via cross-modal MoE transformer that regulates in- formation flow based on signal quality. To further improve stability, we intro- duce CORTEX Loss, which balances task accuracy, quality- -wwar representa- tion refinement and expert utilization under noise. Experiments on CL-Drive and ADABase demonstrate that CogMoE outperforms strong baselines across all modality combinations and sequence lengths, consistently delivering improve- ments across diverse suanal-quality conditions. Our code is publicly available at attps:// http//ggttuus com wwwpaaaaaaaaaaaaammaam httaaaaaaannaaaee///oootaa
INTRODUCTION
Accurate prediction of cognitive load (CL) is critical in safety-critical domains such as driving, avi- ation, and healthcare, where elevated mental demand degrades decision-making and reaction time. Recent advances in multimodal sensing (EEG, ECG, EDA, and gaze) have made large-scale CL pre- diction feasible. However, the fundamental bottleneck is not the lack of sensors models, but the variable quality of physiological signals. In real-world conditions, these signals are often noisy, mis- aligned, or partially missing result of motion artifacts, electrode drift, sensor dropout, and other physiological noise sources (Giangrande et al., 2024 Anwer et al. 2024). Unlike controlled laboratory studies, practical deployments must cope with heterogeneous, unstable input streams, where a single corrupted modality can severely compromise prediction accuracy. More- over, unlike traditional multimodal setups where modalities provide complementary information, EEG, ECG, EDA, and gaze in CL. prediction largely reflect redundant views of the same under- lying cognitive process (Martinez Vásquez et al. (0023). Thus, signal quality, rather than sensor availability or model capacity, is the true limiting factor for accurate and reliable CL prediction.
Existing approaches to CL prediction have largely focused on improving classification accuracy through single-modality modeling naïve multimodal fusion (Angkan et al., 2024 Islam et al.) 2020). Traditional machine learning methods treat signals independently, while recent transformer- based models have demonstrated cross-modal integration. Yet, critical limitations remain. On the data side, many approaches assume clean inputs, neglecting the pre-processing and recovery needed to handle artifacts, temporal inconsistencies, and missing segments. On the model side,
"Equal contribution.
Coorespondencc: Xin Fu (ffu@@centraaluu..cdu))

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current frameworks typically assign experts by modality, lacking adaptive mechanisms to account for real-time variations in signal quality. As a result, their performance degrades severely in noisy or heterogeneous environments, with typical accuracy capped at 70-80% (Pulver et al. 2023 Angkan
et al. 2024 Azizi & BabaAli 2024).
Motivated by this observation, we introduce CogMoE, hignal-qualty-guuide (MoE) framework that reframes multimodal fusion around estimated signal quality rather than modality identity. CogMoE couples this quality-centric formulation with an end-to-end architec- ture: experts specialized for high-fidelity, noisy, and recovered signals, and a quality-aware gating mechanism that routes representations in real time based on estimated signal quality. Our framework integrates (1) Quality-Awaan multimodal Synchronization and Recovery, which aligns signals across Saaall-uually-Sppppaeeee
cexture-of-Expenee
Expert Modeling, which em- ploys a cross-modal transformer and Dynamic Pathway Gating (DPG) mechanism for quality-aware routing, and (3) CORTEX Loss, which balances task accuracy, quality-aware representation refine- ment and expert utilization to maintain consistent performance under noisy and incomplete signals. Together, these components form an end-to-end, adaptive, quality-aware design that directly ad- dresses the core bottleneck of CL prediction under heterogeneous sensor conditions. Extensive periments on CL-Drive and ADABase confirm that CogMoE achieves state-of-the -hrt-- performance, outperforming strong baselines by up 13 percentage points, while maintaining stability under varying signal quality. Our key contributions are as follows:
sampling rates and recovers missing data, (2)
a) We develop a Qualety-Aware Multimodal Synchronization and Recovery stage that aligns heterogeneous physiological signals and restores missing regions, providing a reliable foundation for subsequent quality-specific expert modeling.
b) We introduce a signal quality-guides MoE that shifts the routing criterion from modality identity to estimated signal quality, with three experts tailored to distinct quality regimes: a High Fidelity expert for clean signals, a Noise Resilient expert for noisy inputs, and Contextual Refinement expert for masked or recovered regions. A Dynamic Pathway Gating mechanism routes representations based on real-time signal quality estimates.
c) We propose the CORTEX Loss, a set of quality-aware objectives designed maintain task accuracy, support representation refinement, and prevent expert collapse. Its adaptive weighting schedule further stabilizes training under variable signal conditions, and abla- tions show that each component is required for reliable performance.
d) Extensive experiments on CL--Drive and ADABase, covering all modality combinations and multiple sequence lengths, demonstrate that CogMoE achieves up to 13% and 9.5% accuracy gains over strong basclines, with consistent improvements in FI across settings.
RELATED WORKS
2.1 COGNITIVE LOAD PREDICTION
CL prediction is essential for applications like human-computer interaction and healthcare. Early efforts focused on unimodal signals like EEG, which, despite its high temporal resolution, is highly susceptible to noise and lacks multimodal context (Pulver et al. 2023 Liu et al., (024b)] Yedukon- dalu et al. 2025). Similarly, ECG-based heart rate variability studies provide physiological insights
but fail to capture the multidimensional nature of CL (Sriranga et al. 2023 Wang et al. 20244 Shi
al. 2024). Emerging multimodal approaches integrate EEG and ECG enhance predictive accu-
racy (Azizi & BabaAli 2024. Arts & Van den Broek 2022) yet often overlook the complementary role of EDA and gaze data, which are crucial for comprehensive CL modeling. The recently re- leased CL-Drive dataset (Angkan et al. 2024) provides benchmark for CI. classification across EEG, ECG, EDA, and gaze, advancing research in this area. ADABase (Oppelt et al. 2022) pro- vides ECG, EDA, along with other signals in a simulated driving setting, and is used as benchmark for multimodal cognitive load prediction.
Unlike previous works, we propose quality-aware multimodal framework that first addresses the alignment of signals, then enhances data quality through recovery techniques, and finally integrates four physiological signals via a tross-attention transformer. This design directly targets the chal- lenges of noise, misalignment, and missing data, thereby enabling consistent and reliable CL pre- diction under heterogeneous signal quality.
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Traditional method
(b) Our method
Fusión
Inpux
Fusin
Midea
Semction
EDG
EDG
Expert
lone Frequ eney Sanchorizattin
Export
EDA
Export
GAZE
Fillring
GATE
Musterodel Hosooeyy
Single Modal
Direct
Quality- Multi-modal Sync Recovery
Modality Encodars
Signal- Qua--tyuiiitta Quecitt-Sppettllte Specitlc
Pre-procasaing
Model
Expart Modwiing
Figure 1: Comparison of (a) traditional method vs. (b) ours CogMoE.
2.2 MIXTURE OF EXPERTS NETWORKS
MoE (Mixture of Experts) architectures are gaining popularity for enhancing multimodal fusion by dynamically allocating computational resources specialized experts for specific tasks. Sparse gating mechanisms, such as those introduced in (Shazeer et al. 2017), allow MoE models to activate expert networks selectively, improving efficiency and scalability ity. Recent advances in MoE models have addressed multimodal challenges such as missing modalities and high computational costs. For example. FuseMoE (Han et al. 2024) combines sparsely gated MoE layers with a Laplace gating function to boost robustness in image-text tasks, while FlexMoE (Yun et al. (024) uses missing modality bank and specialized routing strategies to handle diverse modality combinations, showing effectiveness in healthcare datasets like ADNI and MIMIC-IV. Switch Transformer (Fedus) jet al., 2022) incorporates MoE layers within transformer blocks, reducing computational overhead by activating only subset of experts per input. More recently, DeepSeek- V2 (Liu et al.) 2024a) optimized MoE routing mechanisms to achieve state-of-the-ee efficiency in large language models (LLMs).
Despite MoE's success in multimodal tasks, existing designs generally route experts based on se- mantic content modality identity, implicitly assuming that signals are reliable. This assump- tion does not hold in cognitive load prediction, where EEG, ECG, EDA, and gaze signals are often noisy, partially missing, or misaligned. Our work introduces a quality-guided MoE, in which ex- perts are specialized for high-fidelity, noisy, and masked or recovered signals, and a dynamic gating mechanism that adaptively routes inputs according signal quality. This shift from modality- basedd to quality- based expert assignment represents a fundamental reconfiguration of MoE, enabling adap- tive modeling of physiological data under heterogeneous conditions.
3 METHODOLOGY
To achieve accurate CL prediction, we propose CogMoE, a signal quality-guided MoE framework designed to handle the noise, artifacts, and missing data that inherent in physiological signals. Unlike the traditional methods, as illustrated in Figure [ CogMoE operates in two complemen- tary stages: first, quality-aware multimodal synchronization and recovery stage acts pre- reconstruction step, aligning signals with different sampling rates and partially restoring corrupted missing inputs; then, a suuauu---aattt-spiiffte expert modeling stage employs a cross-modal MoE transformer with dynamically gated experts specialized for high-fidelity, noisy, and missing signals, ensuring that information flow is regulated according signal quality. Beyond these core com- ponents, CogMoE flexibly integrates physiological modalities, enabling effective prediction even when some are unavailable or degraded. Together, these stages allow CogMoE to deliver accu- rate and consistent CL predictions across diverse sensor conditions, enabling reliable multimodal inference under heterogeneous signal quality.
3.1 QUALITY-AWARY MULTIMODAL SYNCHRONIZATION AND RECOVERY
Physiological signals often sampled at different rates and suffer from misalignment missing segments. This motivates a two-step process, as shown Figure [ Free-Freyy Synchroniza- tion, which aligns signals across modalities, and multimodal Data Recovery, which restores incom-
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Time-frequency
Mutti-modal Recovery
Modality
Synchronization
Wivelet
fiegannc
Cross-modal Interpalation
EEG
EEG
Correlation
Analysis
EDA
time
timo
2503 Ho
Intra-modal Complation
Unbrown Kaana
Gaze wiplooodl
EDA
Gaze
Alignment
Output
E6G, 6DG, EDA,
Figure 2: Overview of Quality-Awar Multimodal Synchronization and Recovery.
plete inputs through cross-modal recovery by leveraging intact modalities to fill gaps and intra- modal recovery to ensure within-modality consistency, providing cleaner inputs for downstream modeling.
3.1.1 TIME-FREQUENCY SYNCHRONIZATION
Misalignment across modalities such as EEG, ECG, EDA, and gaze corrupts cross-modal relations and degrades feature extraction. Standard methods like Dynamic Time Warping (DTW) are highly sensitive to spikes and rapid changes, often producing ambiguous or unstable alignments (Su et al.) 2021 Doan et al. 2024). To address this, we adopt a synchronization strategy based on the Con- tinuous Wavelet Transform (CWT), which projects signals onto a near-continuous time-frequency grid. This representation captures both transient and slow components, enabling more robust and interpretable alignment across heterogeneous physiological signals (Arts & Van den Brock 2022).
Specifically, we first apply the CWT to each modality's 's signal Sm. converting the time-series data into 2D time-frequency representation Wm. Using complex Morlet wavelets, this representa- tion captures both high-frequency events (e.g., spikes) and low www.uereuccy trends (e.g., slow ECG or EDA variations), thereby preserving transient well smooth components critical for aligmment. To align signals across modalities, we cast the problem as 2D cross-correlation in time-frequenyy space. Compared time-domain correlation, the time-frequency representation leverages spectral information, making alignments more stable under noise and non-stationary dynamics. The opti- mal alignment is given by the location of the maximum correlation; multiple maxima exist, the alignment with the smallest temporal and frequency shifts is selected:
At". A arg min F.f"
arg max Wo)(e, P.
(1)
where At" and Aʃ" denote the temporal and frequency shifts, respectively. These shifts then ap- plied to synchronize modalities. In practice, CWT first applied channel-wise, and the aligned rep- resentations are aggregated into a modality-level tensor before entering modality-speeific encoders, where lightweight channel attention or linear projections preserve intra-channel distinctions while producing compact embeddings. This process mitigates the ambiguities inherent in peak-mat methods, yielding more reliable synchronization for downstream modeling.
3.1.2 MULTIMODAL DATA RECOVERY
Sensor noise and dropout frequently create missing segments in multimodal physiological signals, which undermines reliable feature extraction and downstream modeling. To address this, we pro- pose two-step recovery strategy. First, gross-modal recovery uses intact modalities infer whether and when key events occur. Second, intra-modal recovery restores modality continuity, en- forcing smooth transitions and periodic trends. Combined, these steps reconstruct missing regions consistently across modalities while preserving each signal's 's internal structure. Concretely, we first generate a binary mask Mn, (t. for each modality by examining the aligned time- mee-fruuunccy rep- resentations of the other modalities. For each location (t. f), compute the aggregated energy Hm, (t. f) within a local neighborhood by summing contributions from all other modalities, and mark the region missing if Hn, (t, f) exceeds predefined threshold.
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Encoder
noisy
clean
missing
Modality Specific Encoders
Linea
LayerNorm
Attemion
Dynamio Pathway
Gating
Quality Assessment
Dynamic Weights
On
Dropout
Z.ee
Cross Attention
Weight Computation
X
2u(2,0)
Cognhtve Load Prediction
(HFE)) Fideiity
HIEE
NRE
CRE
Su(2.0)
KMM
(3001) Noiso Realltem
Normalization
Papert
Classification
XGaza
(088)
Figure 3: Overview of Signal-Qualit-SSeeciee Expert Modeling
After generating the mask identify missing regions, apply cross-modal interpolation fill in these gaps. For each missing point in modality m. we use the aligned values from other modalities m', weighted by their local frequenyy similarity, interpolate the missing value:
E O'm (") (V, (') (t,
where am (t, (t, is the weighting factor that reflects the local neighborhood correlation between modalities, and Bm (t, f) is normalization factor that balances contributions across modalities by compensating for magnitude differences.
After cross-modal interpolation, we apply intra-modal completion to further refine the time- frequency representation within each modality. This step ensures that the interpolated values are consistent with the underlying signal structure while minimizing noise and preserving the energy distribution of the signal. Specifically, we apply low-rank matrix completion the masked region Mm (t, f), formulating the reconstruction constrained nuclear norm minimization problem:
Whinnal arg min |Wm |l+ s.t. Wm((.f)MM.. W m(..)).....
Here, ||W,,,,, ll. denotes the nuclear norm, i... the sum of the singular values of W.. which promotes low-rank solutions. This optimization reconstructs the missing values while preserving the observed values and enforeing global low-rank structure. As result, the recovered signal maintains smooth temporal transitions, periodicity, and physiologically meaningful patterns. By combining cross- and intra-modal enhancements, the recovered signals preserve both global structure and local fidelity, providing reliable inputs for subsequent expert modeling.
3.2
EXPERT MODELING
While synchronization and recovery alleviate issues of misalignment and partially missing data, they cannot fully eliminate the challenges of physiological signals. Residual noise remains, severely cor- rupted segments may not be recoverable, and complex interdependencies across modalities require more than pre- pre-processing alone. To address these limitations, we introduce Signal Signal-Quality Quality-Sppccifi Expert Modeling (Figure 3). which adaptively handles heterogeneous signal quality during repre- sentation learning.
This stage begins with (1) modality-specific encoders tailored to each signal's properties, producing embeddings that capture modality-level characteristics. These embeddings are then integrated into unified representation Zm to capture inter-modal dependencies. Next, Z... passes through (2) (3) Dynamic Pathway Gating (DPG), which routes features to specialized experts within the (4) MoE network according signal quality. Finally, employ Cognitive Routing and Temporal EXpertise (CORTEX) Loss to optimize expert utilization, enhance prediction accuracy, and suppress noise, ensuring reliable performance under varying sensor conditions.
cross-attention layers and
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3.2.1 SAAALL-QALLIY--UIDEER MIXTURE OF EXPERTS
Conventional MoE architectures assign experts by modality or semantic content, but this assump- tion is less relevant for physiological data. Once aligned, EEG, ECG, EDA, and gaze often reflect overlapping aspects of the same cognitive state. The key variability instead lies in signal quality, since some channels are clean while others are noisy or partially missing. This motivates our signal- quality-guided MoE, which introduces three specialized experts and Dynamic Pathway Gating (DPG) mechanism that routes fused features to the most suitable expert in real time. Further design details are provided in Appendix A.7
The design of each expert is follows: High Fidelity Expert (HFE) is designed for clean sig- nals with a high signal-to-noise ratio (SNR) > 15 dB, employing a lightweight FFN with two fully connected layers and ReLU activations to balance efficiency and performance. Noise Resilient Ex- pert (NRE) targets noisy signals, adopting an extended FFN with residual connections and noise- aware normalization, where the residual paths capture fine-grained features while the normalization mitigates variability from motion artifacts or environmental interference. Finally, Contextual Re- finement Expert (CRE) focuses masked or preliminarily recovered inputs, refining them the embedding level via cross-attention to leverage cross-modal dependencies and contextual patterns, thereby correcting residual artifacts and adapting refined representations the prediction task.
After designing the experts, we introduce the Dynamic Pathway Gating (DPG) mechanism, which routes the fused feature vector z to the most suitable expert according signal quality. Unlike traditional gating based on static rules (Yun et al., [224]] Fedus et al., 2022). DPG derives a quality score 9m for each modality m, by combining three factors: (I) SNR, estimated as the variance ratio between signal and noise, where the noise variance is approximated using a self-referencing strategy with Gaussian corruption and masking; (2) the proportion of non-missing values
Pmmissing.m ) and (3) temporal consistency, measured by average autocorrelation within the first Lm lag window. The final score computed as:
9m = SNR x (1 Pmissing.m. X m
(4)
The modality quality scores }qm are normalized to form quality vector q, which is concatenated with the fused feature z to guide expert selection. The DPG mechanism then computes routing weights yh (z, q) via a softmax over expert-specific linear projections:
exp W g. [2; gl)
(z 9) = Σj exp W as [z; q))
(5)
where each W g a learnable parameter for expert k and iz ql denotes the concatenation of the fused feature and quality vector. The final expert-driven representation is then computed
i =
9h (z, a fz
(6)
with fz(z) denoting the output of expert k. The resulting é is passed through a classification head to yield the final prediction y. By modulating expert contributions based on signal quality, DPG en- sures that the most relevant expert dominates the fused representation, enabling adaptive integration under noisy degraded inputs. To further enhance DPG, incorporate regularization term into the loss function that promotes balanced expert utilization, preventing over-reliance on any single expert. This ensures that all experts contribute meaningfully the prediction process, enhancing the model's stability across varied signal conditions. The regularization term is defined as:
R gale
Σ
gk 24, 9:)
1 K
N
where N is the batch size and K the number of experts. This smooth squared-erre penalty dis- courages over-reliance on any single expert while retaining flexibility. Unlike KI. divergence, which

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can be unstable under sparse routing, the coefficient of variation, which is non non-differentiable, the squared-error form provides stable gradients and remains invariant to scale.
3.2.2 COGNITIVE ROUTING AND TEMPORAL EXPERTISE (CORTEX) LOSS
A single loss objective can only drive task prediction and fails to ensure expert specialization or consistency under noisy and incomplete signals. To overcome these limitations, we introduce the CORTEX Loss, which jointly optimizes expert specialization and quality-aware routing by integrat- ing multiple terms aligned with the design goals of CogMoB: (1) accurate cognitive load prediction, (2) balanced expert utilization to prevent collapse into single pathway, and (3) quality-aware signal representation consistency under transient noise or residual artifacts. Rather than combining unre- lated terms, CORTEX provides a unified objective in which each component contributes to the over- all quality-aware modeling strategy of CogMoE. Guided by empirical observations ( (Appendix (..en CORTEX adaptively weights these components during training, ensuring that the learned experts remain complementary and that quality-aware routing is effectively reinforced. The loss consists of the following components:
CORREEX -rask + Cnoise + Xerefinemet
Here, the Task Loss (Cusk) applies standard cross-entropy to the MoE outputs, anchoring optimiza- tion accurate CL prediction. To support quality-aware expert specialization, auxiliary MSF:based losses. The Noise Suppression Loss (Lmoise) guides the NRE to suppress noise while preserving discriminative features by matching denoised outputs to relatively clean reference representations. This design explicitly enforces representation consistency under signal degradation, preventing noise-induced representation drift that could otherwise destabilize expert routing. These clean references are obtained through a self-referencing strategy during training. We simulate de- graded signal conditions by injecting synthetic Gaussian noise and random modality masking into the input signals, while treating the corresponding uncorrupted, preprocessed representations prior to augmentation as clean references. Complementarilyy the Refinement Loss (Crefinement) guides the CRE by promoting improved representations for recovered or low-qualit modalities, thereby enhancing robustness under missing or unreliable inputs. Finally, the gating regularizer Rgas (See- tion 3.2.1 prevents expert collapse by encouraging balanced expert utilization. Together, these terms form a unified objective that ensures quality- aware routing and complementary expert behaviors un- der variable signal conditions.
incorporate two
To make CORTEX Loss responsive to both training dynamics and signal conditions, we assign adaptive weights (7, A, () to its components. Early in training, stronger emphasis is placed on auxiliary terms (noise suppression, refinement, and expert regularization) to stabilize learning, while their influence gradually decays to allow the task objective dominate. Concretely, we apply decay schedule to the regularization weight 3:
Bini
8 = min (mmax: 1 at
where is the training epoch. This schedule encourages the utilization of diverse experts in the early stages while preventing peee-ennaization later to maintain flexibility. The coefficients 7 and À are set via hyperparameter search to balance task accuracy with noise suppression and refinement.
4 RESULTS AND DISCUSSION
4.1 DATASET AND IMPLEMENTATION
We compare CogMoE with a range of baselines, including conventional methods (RF, XGB, MLP
VGG, ResNet) and the recent multimodal model BIOT (Guo et al. 2003 Breiman 2001
Chen
& Guestrin 2016, Simonyan & Zisserman, 2014 He et al. 2016,] Azizi & BabaAli 2024.. The
conventional models provide reference points for unimodal or naive fusion strategies, while BIOT
represents the strongest existing multimodal approach for CL prediction. Since multimodal methods
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Table 1: Accuracy and Fl scores for different modality combinations across models on CL--Drive.
Modalities
VGG
ResNet
VGG
ResNet
CogMolE
BCG
16.15 (57
92.31 (90.95)
EDM
$7.95 (86.50)
EEG
77.41 (33.34)
77.58 (73.72)
(72.66)
75,56 (75.21)
69.35 (65.26)
63.83 (63
61.95 (59.75)
77.75 (80.56)
50,94 (89, (89.60)
Gaze
94.35 (93.10)
DOG, EDA
82.37 (90.85)
DOG, BEG
(16.2))))27)
82.95 (81.25)
(22.3)
77.57 (75.80)
74.27 (71.88)
67.75 (66,97)
64.49 (02.14)
83.54 (55. 96)
93.54 (92.20)
BOG, Gare
92.27 (91.05)
RJM, KEG
(9.48777644)
60.06 (77
76.31 (14,00)
78,99 (76.94)
71.24 (68 46)
66 (16. (1)
60:90 (57.45)
94.05 (92-75)
FIM. Gase
50.62 (8S.20)
REG, Gare
79.89(76.3))
80.75 (78. (7806)
(73.4)))
78.78 (76 74)
72.85 (69) 15)
67.62 (66:05)
66.68 (64.85
91.71 (90.40)
DOG, EDA, EEG
81.26 (28, 80)
$2.51 (80)54)
76 (74.54)
78.78 (77:22)
75 (72.71)
70.12 (69 20)
64.41 (02 82)
92.71 (91.45)
DOG, KDA, Gare
90.65 (77,83)
62. 12 (80
77.1 (75,4))
80:17 (78 39)
73.61 (70.67)
71.26
68:71 (06. 373
95.37 (94.16)
BOG. REGG Gare
(77.9.)77)4))
83.02 (81 22)
75.85 (14.55)
78.80 (5))
74.05 (71.83)
71.45 (70.50)
70:04 (67) 69)
92.37 (91.15)
FIM, KEG, Gare
89.37 (88.09)
BOG, RIA, REG, Gase
81.71 (79.23)
83.67 (82.05)
69 (76.1166.9 19)
80.66 (79.17)
76 99 (74.28)
73.87 (73.00)
69.96 (67.04)
94.52 (93.25)
Table 2: Accuracy and Fl scores for different modality combinations across models on ADABase.
Model
Single
Dual
Comb
ECG
KDA
EOG, EDA
ECG
EDA
ECG, EDA
ECG
EDA
ECC, EDA
XGB
72.50 (69. 12)
83.12 (79 22)
71.20 (68.95
(77.11)
74.10 .5)
83.02 (79.47)
CogMoE:
80.52 (79.10)
88.47 (91.23)
91.05 (85.12)
89.23 (87.05)
(92.15)
(88.44)
(88.54)
(90.66)
(89.37)
in this domain are still scarce, BIOT serves as the primary reference point for comparison. For all baselines, we report accuracy and F1, following standard practice in cognitive-load prediction.
For datasets, we focus on multimodal physiological recordings with open access and ground-truth CL labels, which are essential for evaluating generalizability and flexibility across modality combi- nations. We therefore select CL-Drive (Angkan et al. 2024) and ADABase (Oppelt et al.) 2022), as they provide synchronized multimodal recordings at sufficient resolution with CL annotations. Other datasets, such as CLARE (Bhatti et al., 2024), are closed-source, while ID3RSNet (Feng ctt al., 2025) focuses on EEG-only signals and thus falls outside the scope of our study. CL-Drive contains ECG, EDA, EEG, and gaze signals from 21 participants and is benchmarked classification task due to class imbalance in its multi-level annotations. ADABase provides mul- timodal recordings from 30 participants in simulated driving scenarios; for comparability, we only ECG and EDA, matching the CL-Drive configuration. This binary setup follows prior work and reflects practical requirements in safety-critical applications.
binary
To guard against overfitting, we follow each dataset's original evaluation protocol: CL-Drrve with 10-fold segment- wise cross-validation and ADABase with 10-fold subject-wise cross-validation. Satee-Onn-Subjjee
(LOSO) evaluation is less suitable in this multimodal setting, as the number of required splits grows combinatorially with modality combinations, leading to prohibitive cost and fragmented evaluation. More detailed experimental settings, full hyperparameter search ranges, and extended analyses are provided in Appendices A.I
A.2 and A.3 respectively.
4.2 RESULTS ANALYSIS
Modality Combinations Tables and 2 report accuracy and Fl scores for all modality combina- tions on CL-Drive and ADABase. To our knowledge, this is the first systematic evaluation from single- to four-modal fusion on CL-Drive, while prior works only reported partial results. Across all configurations, CogMoE consistently achieves higher accuracy and Fl scores than baseline models, confirming its effectiveness for CL prediction.
In CL-Drive, the EEG+EDA combination yields higher accuracy (94.05%) than EEG+gaze (91.71%) highlighting their complementary roles in CL assessment. The best overall accuracy (95.37%) is achieved with three modalities, slightly higher than the four-modal setup (94.52%) We attribute this drop to EBG's redundancy with other signals and its higher susceptibility to arti- facts, which can complicate synchronization at finer sampling rates. This observation underscores the need for flexible integration, as CogMoE is able to adaptively benefit from different modality subsets rather than relying on all signals. On ADABase, the highest accuracy (92.54%) is achieved combinations, again showing the effectiveness of selective fusion. Compared with tree-based models (XGB, RF) and deep networks (VGG, ResNet), CogMoE consistently out-
with
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Sequence Length (wec)
Scquence Length 20/sed
Sequence Langth 30/eed]
Sequence longth 40/scc)
100
Accuracy drop
90
Accuracy Score
S
60
Caphio
cod Antte
Figure 4: Sequence length impact across different models for four-modal combinations in CL-Drive.
(a)
(b)
Accuracy (56
F1 Score (86)
95
Score(S()
85
80
75
Raw Input
CWT+ Aligoment
Durs
W/o MoE
W/o CORTEX
Ours
Loss
Figure 5: Ablation results on (a) the pre-processing stage and (b) the expert modeling stage.
performs across modality combinations, with accuracy improving as more complementary signals integrated. Statistical validation using the corrected resampled t-test (Nadeau & Bengio 1999) confirms that CogMoE's s gains in both accuracy and Fl are significant (p < 0.01) on both datasets.
Varying Sequence Length Figure 4 evaluates CogMoE against other models on the four-modal combination across sequence lengths 10s, 20s, 30s, and 40s on CL--Drive. CogMoE consistently outperforms all competitors, maintaining stable performance across different temporal contexts. For a 10s sequence, it achieves the highest accuracy of 94.52% surpassing baselines and significantly outperforming traditional methods. Although accuracy decreases slightly with longer sequences, CogMoE shows less than 5% drop across sequence lengths, compared to an average 12.05% drop for other models. Modality details are provided Appendix AA..2
Expert Utilization and Quality-Sensithe Routing We analyze expert assignments on clean and perturbed test sets validate the effectiveness of the MoE design. Averaged across conditions, 35% of samples are routed to HFE, 33% to NRE, and 32% to CRE, indicating balanced utilization without expert collapse. As expected, noisy inputs are routed more frequently to NRE, masked channels to CRE, and clean samples to HFE. These trends confirm that the experts specialize intended and that routing adapts to signal quality. Additional utilization details are provided in Appendix A.3.3
4.3 ABLATION STUDY
To quantify the contributions of individual components in CogMoE, we conduct a series of ablation studies. First, assess the pre-processing stage by comparing three variants: raw input (no pre- processing), CWT alignment only, and full synchronization with recovery. As shown in Figure ( (a). performance improves steadily across these stages, with full pre-processing yielding gains of 10.3% in accuracy and 11. 1.47% in Fl over raw input. Second, we examine the effect of the MoE architecture and the CORTEX loss through three configurations: (1) a simple FFN without MoE, (2) the MoE model trained without CORTEX loss, and (3) the full model with both MoE and CORTEX loss. As shown in Figure 5 (b), moving from the FFN to the MoB yields clear gains, and adding CORTEX loss brings further improvement, with the complete model achieving over 1 11% higher accuracy and Fl than the FFN baseline. Finally, to further isolate the MoE contribution, we compare CogMoE against dense transformer variant (CogBasic) where experts replaced with single FFN. As reported in Table CogMoE surpasses CogBasic by 6-8% on both accuracy and F1, confirming the value of our quality-aware expert design. Extended ablations provided in Appendix A.4

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5 LIMITATIONS AND FUTURE WORK
While CogMoE achieves strong performance, several limitations remain. First, it currently relies supervised labels; exploring unsupervised or self-supervised objectives could reduce dependence on annotated data and improve scalability. Second, although current experiments the design of CogMoE is centered on signal quality rather than domain or application, making it gen- erally applicable. We will extend evaluations as new multimodal datasets become available. Finally, future work could make the gating mechanism adaptive not only to signal quality but also to tem- poral scale, allowing window lengths adjust dynamically with sensor feedback and context. This would enable CogMoE to better handle varying task demands and improve practicality. Together, these highlights CogMoE's potential as quality-awar framework with broad applicability.
driving datasets,
CONCLUSION
In this paper, we presented CogMoF, a lill--uualltttdedeee MoE framework that reframes mul- timodal CL. prediction around the quality of physiological signals rather than modality identity. CogMoE provides an end-to-end, quality-aware pipeline: a synchronization and recovery stage that stabilizes heterogeneous inputs, followed by cross-modal transformer with quality-specific ex- pert routing that activates experts specialized for high-fidelity, noisy, and recovered segments. To further reinforce quality-aware awwre modeling, introduced the CORTEX Loss, unified objective that balances task accuracy, noise suppression, and stable expert utilization under variable signal conditions. Experiments CL--Drive and ADABase demonstrate that CogMoF achieves up to 13 percentage-point gains over strong baselines and remains robust across all modality combinations and multiple sequence lengths. Together, these results highlight CogMoE tent solution for CL prediction in real-world settings where signal quality fluctuates, and suggest broader design consideration: multimodal systems benefit from aligning their modeling strategies with the key characteristics of the input signals.
practical and consis-
ACKNOWLEDGMENTS
This research is partially supported by NSF grants CCF-2130688, CNS-2107057, and CCF. 2504152.
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A APPENDIX
A.1 DETAILED EXPERIMENTS SETTING
To evaluate our model, we used two datasets: CL-Drive and ADABase. Relevant details from both datasets are as follows:
We employed the CL-Drive dataset, which comprises synchronized EEG, ECG, EDA, and eye-tracking recordings from 21 drivers who each completed nine distinct three-minute scenarios in high-fidelltyy simulator. The dataset provides cognitive load labels that were originally collected by the authors via participants' self-assessments on 9-point PAAS scale at ten-second intervals; these labels were binarized into "low" (1-4) and "high" (5-9) categories for our clas- sification tasks. A preliminary signal-quality assessment indicated that approximately 45% of the ten-second segments were artifact-free, 35% contained transient noise (c.g., motion artifacts), and 20% exhibited partial or complete channel dropout. To maintain consistency with existing bench- marks and to guard against overfitting, all evaluations were conducted using the dataset's origi- nal stratified ten-fold cross-validation protocol, with each participant's segments evenly distributed across folds.
CL-Drive Dataset:
ADA Dataset: For ADABase, we focused exclusively on its k-drive component; simulator-based driving paradigm designed to emulate semi-autonomous (SAE Levels 1-3) tasks collected from 30 participants with synchronized multimodal biosignals (ECG, EDA, EMG, PPG, respiration, skin temperature), behavioral metrics (eye-tracking events, facial action units, reaction times), and subjective measures (NASA-TLX ratings, salivary cortisol pre/post). For comparability with CL-Drive, we restricted our inputs to the ECG and EDA channels and assign labels according the following binary scheme: single-task low load combines baseline ("observation-only" and 1-drive trials, single-task high load combines test ("secondary-task") and 1-drive trials, dual-task low load comprises the baseline trials under 2- and 3-drive conditions, dual-task high load comprises the test trials under 2- and 3-drive conditions, and the combined-tate split contrasts all baseline trials (1-3 drive) against all test trials (1-3 drive). This mapping similar to the one provided in the original paper. ADABase employs a subject-wise k-fold split: each iteration holds out 20% of par- ticipants for testing, with no overlap between train and test sets, repeated across 10 random folds to ensure thorough, cross-subject evaluation.
Training and Evaluation: All experiments were implemented in PyTorch, utilizing the Adam op- timizer with hyperparameters obtained through Optuna-based automated optimization. Model se- lection was based on the highest validation accuracy. Input features were preprocessed following the procedure described in Section 3 with default sequence length of 10 seconds. This allowed the model to learn stable representations that generalize well to practical conditions. Additionally, gradient clipping was used stabilize training, preventing vanishing exploding gradients when processing long sequences. For evaluation, we chose k-fold stratified cross- validaaaiotio over LOSO. This choice was motivated by the need for evaluation protocols that yield stable and representative estimates across folds, particularly critical requirement in multimodal physiological settings where signal quality varies considerably. To further assess reliability, we also tested model performance under different sequence lengths, as detailed in Section 4
Fairness in Comparison To ensure fairness despite the baselines' closed-source implementations, we re- re-implemented and executed each method on CL-Drive and ADABase according to the training protocols specified in their original publications. Our reproduced results deviated by at most 1..5% from the published accuracies and Fl scores (see Table We also experimented with applying our aumtentgment pipeline to the baselines; however, this uniformly degraded their performance, as we show in Appendix A.44. reflecting their lack of tolerance to synthetic perturbations. Therefore, we retained their cited -on-auumeeted) values in our work for fair comparison.
A.2 HYPERPARAMETER SEARCH SPACE
We used Optuna Akiba et al. 2019 for automated hyperparameter optimization. The search ranges
were: Learning rate: le-3 to le44 Batch size: 16 to 64 Dropout: 0.1 to 0.5 - Attention heads:
to 8 Transformer layers: 2 to 4- Hidden dimensions: 128 to 512 Expert dropout rate: 0.1 to 0.3
These ranges follow established transformer best practices and were validated in preliminary trials to ensure stable convergence.
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Table 3: Reported reproduced performance and percentage change CL--Drive (four modality
combination) and ADABase (k-drive).
Model
Modality
Reported
Reproduced Acc. (%t
% Change Acc. (%)
(5)
Fl (9)
(%)
(8)
RF
81.71
79.23
80.52
78.03
-1.46
-1.51
CL-Drive
XGRoost
83.67
82.05
84.92
83.29
+1.49
+1.51
VGG (feat)
[ECG, EEG
80.60
79.17
79.44
78.13
-1.44
-1.31
(feat) VGG (rew)
Gaze]
72.98
73.87
73,00
72.53
71.89
-1.81
-1.52
ReaNet (raw)
69.69
67.04
70.91
68.52
+1.75
+2.21
XGBoost
ECG-Single
72.50
69.12
73.80
70.45
+1.30
+1.33
ADABase
XGBoost
EDA-Single
79.22
81.76
77.92
XGBoost
BCG BcGl
71.20
68.95
73.01
69.70
+1.81
+0.75
XGBoost
77.11
XGBoost
ECG-Comb
74.10
71.56
75.
72.67
+1.22
+1.11
XGBood
EDA Comb
83.02
79.47
84.56
80.22
+1.54
+0.75
DOG, CDA COG GITa
GOG KEO
K0N
XO5
0000/ 2mo))
Dight
Figure 6: Model performance comparison of CogMoE with other models across multiple modality combinations on the CL-Drive dataset.
A.3 EXTENDED RESULTS AND ANALYSIS
A.3.1
MODALITY-SPECIRE PERFORMANCE
To further analyze the model's performance, we evaluated CogMoE against other models across multiple modality combinations, as illustrated in Figure The radar chart provides comprehensive comparison, revealing key performance trends across different methods and modality configurations.
Performance Variability Across Modalities: Examining each modality in isolation, gaze data achieves the highest single-signal accuracy at 94.35%, followed by ECG (92.11%) EEG (90.94%) ) and EDA (87.95%). When pairing two modalities, certain combinations. such as EDA EEG, benefit from complementary physiological markers and reach 94.05%, while others (e.g.. ECG EDA at 82.37%) suffer from overlapping information differing noise characteristics. Notably, the tri-modal set ECG + EDA + Gaze attains the best overall performance of 95.37%, suggesting that adding gaze to core cardiac and skin-conductance signals provides the most synergistic blend of temporal and autonomic features. However, integrating all four modalities introduces marginally more complexity and potential noise, particularly from EEG artifacts, resulting in a slight drop to 94.52%. The radar chart in Figure that models incorporating Gaze and BBG tend to achieve higher performance than those excluding them, suggesting their strong contribution to cognitive load estimation. Hence, judicious selection of modalities, based on their individual quality and the nature of their cross cross-modee modal complementarity, can yield better generalization than naively aggregating every available signal.
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Table 4: Accuracy and F1 Scores for varying sequence lengths.
Sequence Length = 10
Sequence Length = 20
Sequence Length = Sequence Length 40
92.11% %: (90.98%)
90.61% (89. 48%)
89.11% 98%%
87.11% (85 (8%)
EDA
87.95% (86.50%
86.45 (85.00%)
84.95% (83.50%)
82.95% (81.50%)
EEG
90.94% (89. 60%)
89.44% (88. 10%)
87.94% (86.60%)
85.94% (84.60%)
94.35% (93.10%)
92.85% (91.60%)
91.35% (90.10%)
89.35% (88.10%)
ECG, EDA
82.37% (80.85%)
81.17% (79.65%)
79.17% (77.65%)
77.37% (75.85%)
ECG, EEG
93.54% (92.20%
92.04% (90. (00))0%)
90.54% (89.20%)
88.54% (87.20/%)
BOG, Gaze
92.27% (91 (91005%)
93.87% (92.60%)
(90. 10%%)
90.37% (89.10%)
EDA, EEG
94.05% (92.75%)
92.55% (91.25%)
90.55% (89.25%)
89.05% (87.75%)
EDA, Gaze
89.62% (88,30%)
88. 12% (86.80%)
86 86.62% (5%3
84.62% (83 (3330%%
EEG, Gaze
91.71% (90.40%)
90.21% (88.90%)
88.71% (87.40%)
86.71% (85.40%)
ECG, EDA, EEG
92.71% (91, .45%)
91.21% (89, (89.95%)
89.71% (88.45%)
87.71% (86.45%)
ECG, EDA, Gaze
95.37% (94. 10%)
93.87% (92.60%)
92.37% (91.10%)
90.37% (89.10/%)
BOG, KEG, Gaze
92.37% (91.15%)
90.87% 65%%
89.37% (88. 15%))
87.37% (86. 15%%)
EDA, EEG, Gaze
89.37% (88.00%)
87.87% (86.50%)
86.37% (85.00%)
84.37% (83.00%)
ECG, EDA, EEG, Gaze
94,52% (93.25%)
93.02% (91.75%)
91,52% (90.25%%
89.52% (88.25%)
Performance Variability Across Models: Traditional machine-learning methods such as RF and XGB often struggle to leverage complementary information across heterogeneous signals, since their tree-based or boosting frameworks rely on static, handcrafted feature combinations and can suffer from the curse of dimensionality when modalities are concatenated. As a result, they exhibit a more pronounced performance drop in multi-modal settings and fail to capture complex cross-modal interactions. Deep learning models like VGG and ResNet mitigate some of these issues by learn- ing hierarchical representations, yet they still employ uniform fusion layers that treat all features equally and may overfit redundant information. In contrast, CogMoH's mixture-of-expprts architec- ture dynamically routes each input through specialized experts, enabling adaptive feature selection and more effective cross-modal integration, which underpins its superior performance, especially in high-complexity configurations.
Overall, the radar chart highlights CogMoE's superiority in multimodal cognitive load prediction, reinforcing the importance of both cross-modas attention and expert-driven selection in optimizing information integration across physiological signals.
A.3.2 IMPACT OF SEQUENCE LENGTH ON CoGMoE
Table 4 summarizes CogMoE's accuracy and Fl score for each modality combination evaluated on 10s, 20s, 30s, and 40s windows. Three main patterns emerge:
Peak Performance at Shorter Windows: Across all modality sets, the highest scores occur at 10s.
For example, the tri-modal combination (ECG+EDA+Gaze attains 95.37% accuracy and 94. 94% Fl at 10s, then gradually declines to 90.37% 11%///190% 10% at 40s on average. Even single-signal models (c.g., Gaze) follow this trend, confirming that shorter temporal contexts preserve the most discriminative information.
Gradual Degradation with Window Length: As window size increases, performance uniformly tapers but remains strong. Most combinations lose 1-4% accuracy by 30s and stay within 5% margin at 40s. This controlled decline indicates that CogMoE's cross-attention and dynamic gating can absorb added temporal variability without catastrophic failure.
Dependet Stability: Some modality sets are inherently more resilient to longer con- texts. Multi-modal configurations that blend complementary signals (c.g., ECG+EEG+Gaze) show smaller relative drops compared to less synergistic pairs (c.g., ECG+EDA). This suggests that the mixture-of-expent mechanism can leverage richer joint representations to mitigate noise and sampling-rate mismatches over extended sequences.
In summary, CogMoE delivers consistently high performance across range of window lengths, with only modest degradation at 40s. The interplay between window size and modality complementarity highlights the value of adaptive feature routing in handling temporal complexity.
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A.3.3 EXPERT UTILIZATION ANALYSIS
To assess how effectively our DPG mechanism leverages the HFE, NRE, and CRE, we analyzed the distribution of samples routed to each expert, in addition to measuring classification accuracy. We performed the analysis on two versions the dataset: (I) the cleaned CL-Drive data, and (2) synthetically perturbed version, where we introduced Gaussian noise and randomly masked one or more modalities to simulate real-world challenges, such as sensor dropout, motion artifacts (c.g., in ECG), and partial gaze interruptions.
Our findings revealed that, on average. 35% of the samples were routed to HFE, 33% to NRE, and 32% to CRE across all test conditions. For the perturbed data, the gating mechanism was adapted by increasing the usage of NRE when SNRs dropped below a learned threshold, especially in the presence of synthetic motion artifacts in the ECG signals. Additionally, the gating mechanism in- creasingly favored CRE for inputs containing masked or preliminarily recovered regions, where residual artifacts were present, enabling representation refinement maintain classification perfor- mance. HFE remained dominant for cleaner segments with high SNR and minimal artifacts.
deeper breakdown of expert utilization by modality further validated the effectiveness of the gating function. For example, ECG and EEG signals, which are more susceptible to motion artifacts, showed increased routing to NRE in perturbed scenarios. Conversely, gaze and EDA signals, which often suffer from complete dropouts rather than noise corruption, exhibited higher dependence on CRE for representation refinement. This adaptive behavior underscores the importance of dynamic expert selection in handling signal inconsistencies.
A.3.4 COMPARISON OF ALTERNATIVE ALIGNMENT METHODS
To validate the choice of CWT over alternative aligmment tine-frequency representations, conducted controlled comparisons with commonly used approaches, including DTW (Dynamic Time Warping), STFT (Short-Time Fourier Transform), and WPT (Wavelet Packet Transform). These baselines cover progamcc-rogramming aligmment, fixed-window spectral analysis, and dis- crete multiresolution wavelet transforms. As discussed in Sec. 3.I.I DTW often yields unstable or ambiguous alignments under rapid signal changes, STFT suffers from fixed-window resolution constraints, WPT loses continuous-seut information, and learned alignment methods require paired supervision across modalities, which is unavailable for physiological data. Table [ reports quan- titative results under each method's optimally tuned configuration. All results are reported on the four-modality setting of the CL-Drive dataset. Despite tuning, all alternatives remain noticeably inferior to CWT, supporting its use our synchronization stage.
Table 5: Comparison of alternative alignment methods under optimal hyperparameters.
Method
Optimal Parameters
Acc (%)
F1 (%)
A Ace (%)
DTW
Best window 10% (search 5-20%)
88.1
87.4
-6.4
STFT
Window = 256, hop 128 (128-512/ 25-50%
89.3
88.6
-5.2
WPT
Daubechies-4, depth = (db2-<lb,, depth 2-4)
91.2
90.5
-3.3
CWT (Ours)
Complex Morlet, scales 1-64
94.5
93.3
A.4
ADDITIONAL ABLATION STUDIES
A.4.1 COMPUTATIONAL EFFICIENCY
To evaluate computational efficiency, we compared model size, total parameters, FLOPs, and infer- ence latency for the four-modality (CL-Drive) configuration under identical hardware and software settings. Simple classifiers (Decision Tree, SVM, Naïve Bayes) were omitted due to their substan- tially lower accuracy. As summarized in Table 6. CogMoE achieves a favorable trade-off between performance and efficiency despite relying on a transformer backbone. Its mixture-of-experts design activates only a subset of experts per sample, reducing both FLOPs and inference time relative to monolithic VGG and ResNet architectures. Consequently, CogMoE maintains moderate model footprint (19.9 MB, 2.27 M parameters) and requires only 12.5 M FLOPs and 113.9 ms per infer- ence, making it comparable to lightweight feature-hased models yet far faster than raw-input CNNs.
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Table 6: Computational efficiency comparison the four-modality (CI.-Drive) combination.
Model
Model Size (MB)
Total Parameters (K)
FLOPs (M)
Inference Time (ms)
RF
5.00
3,95
71.84
XGBoost
1,94
49.71
VGG (feat)
27.41
2.385.7
14.71
127.10
(feat)
VGG (raw)
55.02
4,795.1
20.62
134.56
ResNet (raw)
77.72
6,759.9
68.61
204.02
CogMolF (Ours)
19.92
2,271.7
12.54
113.99
For completeness, we further decompose the 113.9 ms end-to-end latency reported in Table The total cost includes both the synchronization and recovery stage and the MoE forward pass.
Table 7: Breakdown of CogMoE end-to-end latency (four-modality CL-Drive setting).
Component
Latency (ms)
CWT-based synchronization
17-19
Recovery (SVT--based refinement)
9-11
MoE forward pass
80-85
Total
113-115
CWT synchronization is implemented using FFT convolution and is computed once per window instead of per timestep, so its cost does not scale with sequence length. Recovery uses truncated singular-value thresholding with only few iterations. Combined with the MoE forward pass, the full pipeline operates within approximately 114 ms, which lies comfortably inside the 100-300 ms reaction budget commonly required in human-in in-the-loop and safety-critical systems.
A.4.2 IMPACT OF MOE ARCHITECTURE AND ROUTING DESIGN
To enable a more direct comparison with transformer-based multimodal architectures, we further conduct an ablation by replacing the MoE component in CogMoE with standard feedforward (FFN) layer, yielding dense model we refer to as CogBasic. This removes the dynamic expert gating mechanism while keeping the overall transformer backbone intact. Table summarizes sults, showing that CogBasic performs 6-8% worse than CogMoE on average across accuracy and Fl-score, underscoring the impact of our MoE design.
Table 8: CogBasic (FFN-only) vs CogMoE across modality combinations CL-Drive. vs. CogBasic (Acc / F1)
Modalities
CogBasic
CogMoE
ECG
86.8 (85.3)
92.1 (91.0)
+5.3/+5.7
EDA
79.5 (81.3)
88.0 (86.5)
+8.5 +5.2
EEG
84.5 (82.4)
90.9 (89.6)
+6.4/+7.2
Gaze
87.0 (85.7)
94.4 (93.1)
+7.4/47.4
ECG, EDA
76.0 (74.3)
82.4 (80.9)
+6.4/46.6
ECG, EEG
86.0 (85.0)
93.5 (92.2)
+7.5/+7.2
ECG, Gaze
85.0 (84.1)
92.3 (91.1)
+7.3/7..0
EDA, EEG
86.0 (84.2)
94.1 (92.8)
+8.1/+8.6
EDA, Gaze
80.5 (79.2)
89.6 (88.3)
+9.1/+9.1
EEG, Gaze
84.0 (83.2)
91.7 (90.4)
+7.7/47.2
ECG, EDA, BEG
85.2 (84.2)
92.7 (91.5)
+7.5/+7.3
ECG, EDA, Gaze
87.1 (86.4)
95.4 (94.1)
+8.3/47.7
ECG, EEG, Gaze
82.0 (80.6)
92.4 (92.0)
+10.4/+11.4
EDA, EEG, Gaze
80.5 (79.8)
89,4 (88.0)
+8.9/+8.2
ECG, EDA, FBG, Gaze
86.1 (84.5)
94.5 (93.3)
+8.4/ +8.8
To further compare quality-guided routing with traditional modality expert assignment, we implemented modality-based MoF baseline (ModMoF) in which each expert is statically assigned
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to modality. This setup follows prior multimodal MoE pipelines that modality identity as the routing criterion. For faimess, ModMoE uses the same transformer backbone and the same cross-entropy loss as the dense baseline, without any quality objectives.
Table 9 provides the controlled comparison. ModMoE underperforms CogMoE by 3-5% in both accuracy and F1 and exhibits severe expert collapse toward single dominant expert (EEG). In trast, CogMoE maintains balanced utilization across HFE, NRE, and CRE, confirming that quality- guided routing yields more stable expert specialization and higher predictive accuracy. These find- ings further support our motivation that the dominant axis of variation in multimodal physiological data signal quality rather than modality identity.
Table 9: Comparison of modality-basee based ModMoE and signal-quality -uided CogMoE.
Model
Routing Rule
Acc (%)
F1 (%)
Expert Utilization (%)
ModMoE
Modality-based
89.8
88.9
EEG 62.3%,
ECG 18.5%, EDA 11.2%, Gaze 8.0% NRE 33.1%,
CogMoE
Signal-qualty-guiide
94.5
93.3
HFE 35.4%,
CRE 31.5%
A.4.3 DATA AUGMENTATION
To further validate our design, we conduct extended ablations on data augmentation. Data augmen- tation refers to generating additional training samples by applying controlled perturbations, such as Gaussian noise injection, random temporal shifts, and channel dropout to existing signals, thereby exposing the model to realistic distortions and missing information. We compare key baselines and CogMoE on the CL-Drive dataset under four-modality inputs, both without augmentation and with these synthetic perturbations.
In Table T0 we report accuracy and F1 scores before and after augmentation, along with relative changes. Conventional models (RF, XGB, VGG, ResNet) exhibit consistent performance drops of an average of 3% in both metrics. We attribute this degradation to the rigid architectures of traditional models, whose fixed feature representations and decision boundaries lack resilience to noise injections and channel dropouts. In contrast, CogMoE gains +3.04% in accuracy and +2.84% in F1, due to its gignal-quality-awee gating and expert specialization, which allow it to leverage the augmented variability for stronger generalization under noisy conditions.
Table 10:
Data augmentation ablation: performance without and with augmentation, and percentage change on the modaaity combination (CL-Drive).
Model
w/o Aug
with Aug
Change
Acc. (%)
Fl (%)
Acc. (5)
Fl
Acc. (%)
(%)
79.34
2.91
XGB
83.67
82.05
81.25
79.78
-2.89
-2.76
VGG (feat)
80.66
79.17
77.93
76.31
-3.38
-3.61
ResNet (feat)
76.39
74.28
74.11
71.80
-2.98
-3.34
VGG (raw)
73.87
73.00
71.45
70.58
3.28
33.11
ResNet (raw)
69.69
67.04
67.10
64.72
-3.71
-3.46
CogMoE
91.48
90.41
94.52
93.25
+3.04
+2.84
A.4.4 EXPERT ABLATION
To quantify each expert's contribution to CogMoE's performance, we conducted an ablation study in which we disabled one expert at time. Table summaarzes the resulting drops accuracy and Fl. We observe that disabling the Noise Resilient Expert (NRE) leads to 2.8% drop in accuracy and 2.0% drop F1, while removing the Contextual Refinement Expert (CRE) causes 1.9% and 1.3% reduction, respectively. Relying solely on the High-Fidelity Expert (HFE) incurs the largest degradation (9.2% accuracy, 9.6% F1), highlighting that each expert plays a vital role in sustaining CogMoE's overall performance.
A.4.5 CORTEX Loss ABLATION
To quantify the impact of each CORTEX Loss component, we disable one term at a time and measure its effect classification accuracy, noise and refinement errors, and expert utilization. Table T2
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Table 11: Expert ablation results: impact CogMoE accuracy and F1 when removing individual experts.
Configuration
Accuracy Drop (%)
FI Drop (%)
Wo Noise Resilient Expert (NRF)
2.8
2.0
Wio Contextuality Refinemeut Expert (CRE)
1.9
1.3
Only High-Fidelity Expert (HPE)
9.6
summarizes these results. We observe that disabling the Noise Suppression Loss (Cnoise) results in 1.2% drop in accuracy and a 105.7% increase noise error, highlighting its denoising role. Removing the Refinement Loss (Cretinement) incurs a 1.5% accuracy loss and doubles refinement error, underscoring its importance for missing-date recovery. Finally, omitting the gating regularizer (R (Rgme) causes 7.3% accuracy decrease and overloads the model with HFE handling 81% of all samples, demonstrating the necessity of balanced expert utilization. The remaining 19% of inputs are split between the NRE and the CRE.
Table 12:
Ablation of CORTEX Loss components on CL-Drive.
Ablated Term
AAce (%)
ANoise Error (%)
ARteiiemmnnt Error (%)
Expert Utilization (%)
Without Lyme
-1.2
+105.7
Without Lesteronn
-1.5
+100.0
Without Reg
77.3
IFF((L.0), NRE(9.0). CRE((100)
A.5 DATA AND MODEL COMPONENT
A.5.1 PHYSIOLOGICAL SIGNALS
In CL research, physiological signals provide objective insights into how individuals respond to various tasks. This paper focuses on four key signals, each offering distinct markers of cognitive and physiological states. Electrocardiography (ECG, 512 Hz) records the heart's 's electrical activity, with heart rate and heart rate variability commonly linked to stress and physical exertion. Electroen- cephalography (EEG, 256 Hz) measures brain activity, where specific frequency bands (c.g., alpha and gamma waves) are associated with attention and memory load. Electrodermal activity (EDA, 128 Hz) tracks changes in skin conductance, reflecting sympathetic nervous system activation and emotional arousal. Finally, eye tracking (gaze, 50 Hz) captures visual attention, offering insights into mental focus and regions of interest. Despite their value for CL prediction, these signals are susceptible to noise and artifacts, such as motion artifacts in EEG and ECG, environmental factors in EDA. low temporal resolution in gaze tracking, and occasional sensor drop-offs. Thus, proper pre-processing, including data alignment and enhancement, is essential to address these challenges.
A.5.2 MODALITY SPECIFIC ENCODER
Our encoders adopt unified yet adaptable pipeline to process all four modalities, projecting each signal into a shared 256-dimensional embedding space through lightweight linear layers. This design handles diverse sampling rates and preserves key features. The pipeline includes linear transfor- mation, LayerNorm, and ReLU activation for normalization and feature enhancement. For ECG, we incorporate channel attention block to dynamically highlight informative electrodes and frequency components, addressing their higher complexity. Using a squeeze-an wwweeez--an--eeee and-excitation mechanism, this block adaptively recalibrates relevant ECG features like heart rate variability. To balance feature re- tention and prevent overfitting, we apply modality-speciff dropout rates (0.2 for EEG and ECG, 0.15 for EDA, and 0.1 for gaze). The resulting embeddings (Zm E 256) are concatenated end-to-end and projected into unified representation via multimodal fusion layer for consistent downstream processing. A cross- cross-attention block then learns inter-modal dependencies by dynamically weighting each modality, enhancing relevant signals while suppressing noise.
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100
Accuracy
35
30
85
80
2
Number of Experts
Figure 7: Best accuracy achieved with three experts combination in the CogMoB architecture.
A.6 STANDARD CROSS-MODAL MOE TRANSFORMER
The cross-modal MoE transformer extends the standard Transformer architecture to handle multi- modal data, capturing inter-modal dependencies and adapting to variations input characteristics. Like traditional transformers, it uses multi-head self-attention followed by feed-forward network (FFN), However, instead of a standard FFN, integrates a Mixture of Experts module, which re- places the FFN layers with multiple expert networks, each specializing in different aspects of the data. A gating function dynamically selects and activates a subset of top-k experts based on the in- put, allowing for efficient resource allocation and adaptability diverse input characteristics. This combination of cross-modal attention and expert-based routing effectively models heterogeneous multimodal inputs, making the architecture particularly suitable for complex applications, such as those involving physiological data.
A.7 RATIONALE BEHIND THE MoE DESIGN
Figure 7 illustrates CogMoB's classification accuracy the CL-Drive four-modality task as the number of experts increases from one to six. Starting with a single expert, accuracy is 91.40%; incorporating second expert boosts performance 92.44%, and it reaches its maximum 94.52% with three experts. Adding a fourth expert reduces accuracy to 92.10% and the fifth and sixth experts drive further declines 90.15% and 90.00%, respectively. These trends highlight the opti- mal balance achieved with three specialists and the diminishing returns and additional complexity associated with larger expert ensembles.
Accordingly, we adopt a three-expert architecture comprising:
High Fidelity Expert (HFE): Designed for high-SNR signals (SNR 15 dB), HFE employs lightweight FFN with two fully connected layers, ensuring efficient feature extraction without un- necessary complexity. Increasing the depth beyond two layers resulted negligible accuracy gains (< 0.1%).
Noise Resilient Expert (NRE): The NRE handles noisy signals using residual connections to retain fine-grained details and noise-aware normalization to mitigate distortions. Empirical results show an 18% reduction in feature degradation, demonstrating superior performance over conventional denoising methods.
Contextual Refinement Expert (CRE): CRE refines interpolated values by leveraging cross-modal embeddings, enhancing the reliability of missing data imputation. Compared static imputation, CRE achieves a lower RMSE (< 0.12), ensuring accurate feature refinement even in the absence of multiple modalities.
Overall, our MoE framework improves noise tolerance by 25% over traditional models, making highly effective in practical settings where data quality varies dynamically.
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Table 13: Sensitivity of CORTEX Loss coefficients.
Accuracy (%)
A Ace (%)
0.5
0.6
1.0
93.8
-0.7
0.75
0.6
1.0
94.5
0.0
1.0
0.6
1.0
93.1
-1.4
0.75
0.3
1.0
93.2
-1.3
0.75
0.8
1.0
92.9
-1.6
0.75
0.6
0.5
93.7
-0.8
0.75
0.6
2.0
93.3
-1.2
1.0 (rand)
1.0 (rand)
1.0 (rand)
91.2
-3.3
A.8 EMPIRICAL INSIGHTS BEHIND CORTEX Loss
To better understand the design choices underlying the CORTEX Loss function, we conducted series of empirical analyses. Our findings highlight three key factors that significantly influence task performance: expert routing efficiency, quality-aware representation refinement, and dynamic loss weighting. To improve generalization, we apply data-augmentation: Gaussian noise injection, random temporal shifting, and channel dropout while treating the original, pre-augmented signal segments the clean reference and GT for all MSE-based losses.
First, we observed strong correlation (r = 0.81) between quality-awar aware expert routing and classifi- cation accuracy, indicating that optimal expert allocation plays crucial role in improving predictive performance. Specifically, samples that were dynamically routed to the most appropriate expert based signal quality exhibited lower error rates compared those processed without quality- based gating. This underscores the importance of adaptive expert selection in mitigating the impact of modality-specific noise and missing data.
Second, effective refinement of representations correspoding to recovered or low-quality modal- ities was found be essential for maintaining accurate CL predictions, particularly in high- noise conditions or when entire modalities were missing. In scenarios involving multi-modality dropout, refinement-driven signal modeling recovery yielded statistically significant improvements (p < 0.01), reinforeing the necessity of incorporating refinement constraints within the loss func- tion.
Finally, dynamic loss weighting, which adapts to variations in signal quality and expert utilization, outperformed static loss weighting by 15% in overall classification accuracy. This suggests that loss function that dynamically adjusts its emphasis based on signal quality is better suited for han- dling complex multimodal data. contrast, static loss weighting often led to suboptimal learning, particularly in settings with high variance across modality contributions.
These empirical insights, along with the CORTEX loss ablation study presented in Appendix A.4 instrumental in shaping the design of the CORTEX Loss function, ensuring it effectively bal- ances expert utilization, representation refinement, and adaptive loss scaling. By incorporating these principles, CORTEX Loss optimally aligns with the challenges of multimodal signal processing, im- proving both predictive accuracy and model reliability in practical CL. applications.
A.9 CORTEX LOss COFFFICIENTS
We tuned the weighting coefficients (y, X, 3) of the CORTEX Loss using Optuna. The search ranges were: 7 E 1.0),
[0.3, 0.8), Bui E [0.5, 2.0], a E [0.01, 0. The selected values used in experiments are: = 0.75, X 0.6, Bmit 1.0, = 0.05, with 3max 0.2. These values provided balance between task accuracy, auxiliary objectives, and stable expert utilization.
In addition to the coefficient search, we evaluated the sensitivity of the CORTEX Loss to its weight- ing parameters. Specifically, we performed a sweep 200 configurations across the ranges reported above. The selected values (y 0.75, À 0.6, Pii 1.0, a 0.05) emerged consistently across multiple seeds. Ta summarizes representative configurations from the sweep, evaluated on the four-modality CL--Drive setting. Accuracy remains within 2% of the optimal value across broad
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parameter range, indicating that CogMoE does not rely precise coefficient tuning. Even with random initialization (7 = À = 3 = 1.0), the model still achieves 91.2% accuracy (a 3.3% drop), demonstrating that the architecture is inherently robust to coefficient variations.
Table 14: Effect of different B-schedules expert balance and accuracy.
Schedule Type
HFE (%)
NRE (%)
CRE (%)
Ace (%)
Adaptive (ours)
35.4
33.1
31.5
94.5
Fixed 3 = 1.0
38.2
31.4
30.4
92.7
Aggressive 0.2
33.1
34.2
32.7
93.1
No regularizer
81.0
9.0
10.0
87.2
To further evaluate robustness, also examined expert behavior across the broader hyperparameter
ranges used our Optuna search. Across Piitt E [0.5, 2.0] and E [0.3, 0.8]. expert routing remained balanced (std. 8%) and accuracy varied by less than 2%, indicating that CogMoE is not sensitive to precise coefficient settings. Collapse was observed only when the gating regularizer was removed entirely (B 0).
22


Reviews:
[{"review_id": "WZmM0CBCuT", "reviewer": "Reviewer_4mhF", "summary": "This paper proposes CogMoE and outlines 4 main contributions: 1) Quality-Aware Multimodal Synchronization and Recovery module, 2) three signal-quality-specific expert modeling, 3) CORTEX Loss, and 4) experiments (though this is generally not considered as a contribution of novelty of the work). In experiments on two public datasets, the authors show consistent gains over baselines.", "questions": "No further questions at this stage.", "limitations": "", "rating": null, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The paper studies handling, evaluating signal quality, and interpolating missing signal using other modalities, which is where many otherwise strong models fail in the wild. Reporting results across multiple modality combinations is useful for practitioners who must decide which sensors to deploy, and the paper makes an effort to tie modeling choices to operational constraints (noisy segments, dropouts, cross-sensor lag). Conceptually, using expert pathways specialized for “clean” vs “noisy/reconstructed” inputs is an interesting way to avoid generic fusion algorithms and may be broadly applicable beyond cognitive-load prediction.", "weaknesses": "The paper reads rushed, and the contribution is not crisp. Several choices are taken as givens without ablation (the ablation section is one paragraph). For example, why CWT for representing a signal? Given that CWT is a sifting process, the high-frequency data will be lost, and the data is normalized in a sense. The method CMWT representation for alignment/recovery is introduced in the work, but does not compare to reasonable alternatives (e.g., STFT features, time-domain DTW variants, learned alignment modules). The custom loss also appears as a package rather than with per-term deltas, which makes it hard to attribute gains or evaluate the necessity of each element. On evaluation, the paper discusses “alignment” and “robust fusion” but does not analyze how the number of signal sources and their measured quality jointly affect accuracy; instead, it relies on aggregate tables. \n\nMinor: Acronyms are introduced inconsistently (sometimes before definition, sometimes multiple times). \n\nThe work likely contains a solid idea, but it needs clearer writing, a sharper statement of what is novel, and targeted ablations to make the methodology and its advantages unambiguous.", "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": "6SXnsvVU2y", "reviewer": "Reviewer_HFpj", "summary": "The paper proposes CogMoE, a multimodal framework for cognitive load prediction that is explicitly guided by signal quality rather than modality identity. The framework contains a pre-processing stage for feature extraction, time frequency synchronization, multi-modal data recovery. followed by a signal-quality-specific MoE modeling stage. The proposed framework shows improved performance on several benchmarks.", "questions": "NA", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is clearly written: the motivation is well articulated, and the model design follows directly from it. Shifting MoE routing from modality identity to measured reliability is an interesting idea and a clever fit for physiological data. The experimental coverage is solid, spanning EEG, ECG, EDA, and gaze.", "weaknesses": "1. While the signal-quality–specific MoE is an interesting and domain-appropriate idea—especially given the authors’ argument that aligned EEG, ECG, EDA, and gaze often reflect overlapping aspects of the same cognitive state—a direct, controlled comparison against alternative routing rules (e.g., modality-based experts) is needed to substantiate the claimed benefit.\n\n2.The final loss comprises multiple components and may be brittle without careful balancing. How sensitive is the model to weight coefficient lambda, gamma, beta, and how many total combination of these are searched?", "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": "WvjK59SZzs", "reviewer": "Reviewer_k4Qj", "summary": "This paper addresses the critical challenge of real-world cognitive load (CL) prediction, where physiological signal quality is often poor and variable. The authors propose **CogMoE**, a novel Mixture-of-Experts (MoE) framework that, unlike traditional models, dynamically routes data based on signal quality rather than modality. The two-stage framework first performs quality-aware synchronization and recovery of multimodal signals (EEG, ECG, etc.). Then, a cross-modal transformer routes representations to one of three experts specialized for different quality regimes: a High Fidelity Expert (HFE) for clean signals, a Noise Resilient Expert (NRE) for noisy ones, and a Contextual Refinement Expert (CRE) for recovered data . The model is trained with a novel **CORTEX Loss**, which jointly optimizes for task accuracy, balanced expert utilization, and quality-aware objectives. Experiments on the CL-Drive and ADABase datasets show state-of-the-art performance, with accuracy gains of up to 13% and 9.5%, respectively.", "questions": "1. CORTEX Loss Sensitivity: The CORTEX Loss introduces several new hyperparameters (e.g., $\\gamma, \\lambda$, and the $\\beta$ schedule). How sensitive is the model's performance and, critically, its expert balance, to the precise tuning of these values? Given that removing the gating regularizer entirely causes catastrophic collapse (Table 9), how difficult is it to find a stable $\\beta$ schedule?\n2. Real-Time Feasibility: Table 5 lists an inference time of 114ms. Does this end-to-end time include the \"Quality-Aware Synchronization and Recovery\" stage? Operations like CWT-based alignment and nuclear norm minimization for completion  can be computationally expensive. Is the entire pipeline fast enough for real-time deployment in a safety-critical context?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. Novelty of Core Concept: The paper's central idea of a \"signal-quality-guided\" MoE is highly novel and directly targets the primary bottleneck of real-world physiological sensing.\n2. Purpose-Built, Validated Experts: The three experts (HFE, NRE, CRE) are thoughtfully designed for specific quality regimes (clean, noisy, recovered) . Ablation studies (Table 8) rigorously prove that all three are necessary for robust performance.\n3. Specialized Loss Function: The CORTEX Loss is a key strength, intelligently designed with auxiliary losses ($\\mathcal{L}_{noise}$, $\\mathcal{L}_{refinement}$) to enforce expert specialization and a regularizer ($\\mathcal{R}_{gate}$) to prevent expert collapse, which is empirically validated (Table 9).", "weaknesses": "1. Supervision Dependency: The framework relies on supervised labels, which are expensive and difficult to obtain at scale for physiological data, limiting practical scalability.\n2. Limited Domain Evaluation: While the framework is general , the experiments are confined to two driving datasets (CL-Drive and ADABase). Its effectiveness in other noisy-sensor domains (e.g., healthcare) is not empirically demonstrated.", "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": "MwxVHgE2NB", "reviewer": "Reviewer_gQsN", "summary": "The manuscript presents a potentially valuable idea emphasizing signal quality during multimodal fusion but currently falls short in exposition, motivation, and experimental rigor, evaluation.", "questions": "- Justify the claim: “However, the fundamental bottleneck is not the lack of sensors or models, but the variable quality of physiological signals.” What empirical evidence or literature supports this assertion?\n\n- Provide motivation: Why was the decision made to “shift the focus of multimodal fusion to signal quality, the true bottleneck in practical CL prediction”? How does this design choice address limitations of prior fusion methods?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "CoGMoE / CogMoE", "weaknesses": "- Manuscript readability and presentation require improvement; the method is not explained in sufficient technical detail to assess reproducibility or novelty.\n\n- Terminology is inconsistent (e.g., CoGMoE vs CogMoE); a single canonical name and notation should be used throughout.\n\n- The problem statement and its significance are not clearly articulated; it is difficult to judge the scope and impact of the contribution.\n\n- Related work discussion is insufficient: prior techniques and their specific limitations are not described in depth, leaving motivation for a new method unclear.\n\n- Evaluation is limited in scope, both in metrics and experiments, preventing strong conclusions about effectiveness and generalizability.", "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)