# SCALAR: Self-Calibrating Adaptive Latent Attention Representation Learning

arXiv: https://arxiv.org/abs/2510.16474 · HTML: https://mlsanity.com/paper/2510.16474/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 41 references that are in the map.
- First posted on arXiv 18 Oct 2025.

## Builds on (most cited first, 21 of 21)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Dynamic Convolution: Attention over Convolution Kernels](https://mlsanity.com/paper/1912.03458/) (2019)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [Ladder Variational Autoencoders](https://mlsanity.com/paper/1602.02282/) (2016)
- [Understanding and Improving Layer Normalization](https://mlsanity.com/paper/1911.07013/) (2019)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization](https://mlsanity.com/paper/2012.04456/) (2020)
- [Well-tuned Simple Nets Excel on Tabular Datasets](https://mlsanity.com/paper/2106.11189/) (2021)
- [DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences](https://mlsanity.com/paper/1811.02114/) (2018)
- [WideDTA: prediction of drug-target binding affinity](https://mlsanity.com/paper/1902.04166/) (2019)
- [Supervised Dimensionality Reduction for Big Data](https://mlsanity.com/paper/1709.01233/) (2017)
- [ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models](https://mlsanity.com/paper/2412.04756/) (2024)
- [Robust Partial Least Squares Using Low Rank and Sparse Decomposition](https://mlsanity.com/paper/2407.06936/) (2024)
- [A Survey on Immersive Cyber Situational Awareness Systems](https://mlsanity.com/paper/2408.07456/) (2024)
- [Machine Learning Driven Smishing Detection Framework for Mobile Security](https://mlsanity.com/paper/2412.09641/) (2024)
- [The Future of AI: Exploring the Potential of Large Concept Models](https://mlsanity.com/paper/2501.05487/) (2025)
- [Supervised Multiple Kernel Learning approaches for multi-omics data integration](https://mlsanity.com/paper/2403.18355/) (2024)
- [What Skills Do Cyber Security Professionals Need?](https://mlsanity.com/paper/2502.13658/) (2025)

## Built on by (most cited first, 5 of 5)

- [Australian Bushfire Intelligence with AI-Driven Environmental Analytics](https://mlsanity.com/paper/2601.06105/) (2026)
- [Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.04442/) (2026)
- [AgenticVM: Agentic AI for Adaptive Software Vulnerability Management](https://mlsanity.com/paper/2605.01739/) (2026)
- [Comparative Analysis of Large Language Models in Healthcare](https://mlsanity.com/paper/2604.10316/) (2026)
- [CausalNav: Reliability-Certified Causal World Models for Control under Physical-Parameter Shift](https://mlsanity.com/paper/2608.07809/) (2026)

## Abstract (arXiv)

High-dimensional, heterogeneous data with complex feature interactions pose significant challenges for traditional predictive modeling approaches. While Projection to Latent Structures (PLS) remains a popular technique, it struggles to model complex non-linear relationships, especially in multivariate systems with high-dimensional correlation structures. This challenge is further compounded by simultaneous interactions across multiple scales, where local processing fails to capture crossgroup dependencies. Additionally, static feature weighting limits adaptability to contextual variations, as it ignores sample-specific relevance. To address these limitations, we propose a novel method that enhances predictive performance through novel architectural innovations. Our architecture introduces an adaptive kernel-based attention mechanism that processes distinct feature groups separately before integration, enabling capture of local patterns while preserving global relationships. Experimental results show substantial improvements in performance metrics, compared to the state-of-the-art methods across diverse datasets.