# REVELIO -- Universal Multimodal Task Load Estimation for Cross-Domain Generalization

arXiv: https://arxiv.org/abs/2509.01642 · HTML: https://mlsanity.com/paper/2509.01642/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 72 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Deep Networks with Stochastic Depth](https://mlsanity.com/paper/1603.09382/) (2016)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [xLSTM: Extended Long Short-Term Memory](https://mlsanity.com/paper/2405.04517/) (2024)
- [img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation](https://mlsanity.com/paper/2012.07791/) (2020)
- [Py-Feat: Python Facial Expression Analysis Toolbox](https://mlsanity.com/paper/2104.03509/) (2021)
- [The Arousal video Game AnnotatIoN (AGAIN) Dataset](https://mlsanity.com/paper/2104.02643/) (2021)
- [MAUS: A Dataset for Mental Workload Assessmenton N-back Task Using Wearable Sensor](https://mlsanity.com/paper/2111.02561/) (2021)
- [Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset](https://mlsanity.com/paper/2011.00958/) (2020)
- [MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks](https://mlsanity.com/paper/2210.03065/) (2022)

## Abstract (arXiv)

Task load detection is essential for optimizing human performance across diverse applications, yet current models often lack generalizability beyond narrow experimental domains. While prior research has focused on individual tasks and limited modalities, there remains a gap in evaluating model robustness and transferability in real-world scenarios. This paper addresses these limitations by introducing a new multimodal dataset that extends established cognitive load detection benchmarks with a real-world gaming application, using the n-back test as a scientific foundation. Task load annotations are derived from objective performance, subjective NASA-TLX ratings, and task-level design, enabling a comprehensive evaluation framework. State-of-the-art end-to-end model, including xLSTM, ConvNeXt, and Transformer architectures are systematically trained and evaluated on multiple modalities and application domains to assess their predictive performance and cross-domain generalization. Results demonstrate that multimodal approaches consistently outperform unimodal baselines, with specific modalities and model architectures showing varying impact depending on the application subset. Importantly, models trained on one domain exhibit reduced performance when transferred to novel applications, underscoring remaining challenges for universal cognitive load estimation. These findings provide robust baselines and actionable insights for developing more generalizable cognitive load detection systems, advancing both research and practical implementation in human-computer interaction and adaptive systems.