# MuViS: Multimodal Virtual Sensing Benchmark

arXiv: https://arxiv.org/abs/2603.24602 · HTML: https://mlsanity.com/paper/2603.24602/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 13 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Inductive Bias of Deep Convolutional Networks through Pooling Geometry](https://mlsanity.com/paper/1605.06743/) (2016)
- [Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling](https://mlsanity.com/paper/2402.10211/) (2024)
- [MAESTRO : Adaptive Sparse Attention and Robust Learning for Multimodal Dynamic Time Series](https://mlsanity.com/paper/2509.25278/) (2025)

## Built on by (most cited first, 1 of 1)

- [Exposing Blind Spots in Deep Imbalanced Regression Evaluation](https://mlsanity.com/paper/2609.25152/) (2026)

## Abstract (arXiv)

Virtual sensing aims to infer hard-to-measure quantities from accessible measurements and is central to perception and control in physical systems. Despite rapid progress from first-principle and hybrid models to modern data-driven methods research remains siloed, leaving no established default approach that transfers across processes, modalities, and sensing configurations. We introduce MuViS, a domain-agnostic benchmarking suite for multimodal virtual sensing that consolidates diverse datasets into a unified interface for standardized preprocessing and evaluation. Using this framework, we benchmark established approaches spanning gradient-boosted decision trees and deep neural network (NN) architectures, and show that none of these provides a universal advantage, underscoring the need for generalizable virtual sensing architectures. MuViS is released as an open-source, extensible platform for reproducible comparison and future integration of new datasets and model classes.