ChartDiff: A Large-Scale Benchmark for Comprehending Pairs of Charts
arXiv:2603.28902v1 Announce Type: new
Abstract: Charts are central to analytical reasoning, yet existing benchmarks for chart understanding focus almost exclusively on single-chart interpretation rather than comparative reasoning across multiple charts. To address this gap, we introduce ChartDiff, ...
Mitigating Forgetting in Continual Learning with Selective Gradient Projection
arXiv:2603.26671v1 Announce Type: new
Abstract: As neural networks are increasingly deployed in dynamic environments, they face the challenge of catastrophic forgetting, the tendency to overwrite previously learned knowledge when adapting to new tasks, resulting in severe performance degradation on...
Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach
arXiv:2603.26948v1 Announce Type: new
Abstract: Existing approaches for predictive process monitoring are sub-symbolic, meaning that they learn correlations between descriptive features and a target feature fully based on data, e.g., predicting the surgical needs of a patient based on historical ev...
Concerning Uncertainty -- A Systematic Survey of Uncertainty-Aware XAI
arXiv:2603.26838v1 Announce Type: new
Abstract: This paper surveys uncertainty-aware explainable artificial intelligence (UAXAI), examining how uncertainty is incorporated into explanatory pipelines and how such methods are evaluated. Across the literature, three recurring approaches to uncertainty...
arXiv:2603.25839v1 Announce Type: new
Abstract: Deep neural networks exhibit a simplicity bias, a well-documented tendency to favor simple functions over complex ones. In this work, we cast new light on this phenomenon through the lens of the Minimum Description Length principle, formalizing superv...
Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control
arXiv:2603.25771v1 Announce Type: new
Abstract: Reinforcement learning (RL), owing to its adaptability to various dynamic systems in many real-world scenarios and the capability of maximizing long-term outcomes under different constraints, has been used in infectious disease control to optimize the...
Incorporating contextual information into KGWAS for interpretable GWAS discovery
arXiv:2603.25855v1 Announce Type: new
Abstract: Genome-Wide Association Studies (GWAS) identify associations between genetic variants and disease; however, moving beyond associations to causal mechanisms is critical for therapeutic target prioritization. The recently proposed Knowledge Graph GWAS (...
Pure and Physics-Guided Deep Learning Solutions for Spatio-Temporal Groundwater Level Prediction at Arbitrary Locations
arXiv:2603.25779v1 Announce Type: new
Abstract: Groundwater represents a key element of the water cycle, yet it exhibits intricate and context-dependent relationships that make its modeling a challenging task. Theory-based models have been the cornerstone of scientific understanding. However, their...
GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation
arXiv:2603.26266v1 Announce Type: new
Abstract: Large vision-language models have endowed GUI agents with strong general capabilities for interface understanding and interaction. However, due to insufficient exposure to domain-specific software operation data during training, these agents exhibit s...
Semi-Automated Knowledge Engineering and Process Mapping for Total Airport Management
arXiv:2603.26076v1 Announce Type: new
Abstract: Documentation of airport operations is inherently complex due to extensive technical terminology, rigorous regulations, proprietary regional information, and fragmented communication across multiple stakeholders. The resulting data silos and semantic ...
Safe Reinforcement Learning with Preference-based Constraint Inference
arXiv:2603.23565v1 Announce Type: new
Abstract: Safe reinforcement learning (RL) is a standard paradigm for safety-critical decision making. However, real-world safety constraints can be complex, subjective, and even hard to explicitly specify. Existing works on constraint inference rely on restric...
Memory Bear AI Memory Science Engine for Multimodal Affective Intelligence: A Technical Report
arXiv:2603.22306v1 Announce Type: new
Abstract: Affective judgment in real interaction is rarely a purely local prediction problem. Emotional meaning often depends on prior trajectory, accumulated context, and multimodal evidence that may be weak, noisy, or incomplete at the current moment. Althoug...
Dynamic Fusion-Aware Graph Convolutional Neural Network for Multimodal Emotion Recognition in Conversations
arXiv:2603.22345v1 Announce Type: new
Abstract: Multimodal emotion recognition in conversations (MERC) aims to identify and understand the emotions expressed by speakers during utterance interaction from multiple modalities (e.g., text, audio, images, etc.). Existing studies have shown that GCN can...
Transformer-Based Predictive Maintenance for Risk-Aware Instrument Calibration
arXiv:2603.20297v1 Announce Type: new
Abstract: Accurate calibration is essential for instruments whose measurements must remain traceable, reliable, and compliant over long operating periods. Fixed-interval programs are easy to administer, but they ignore that instruments drift at different rates ...
BrainSCL: Subtype-Guided Contrastive Learning for Brain Disorder Diagnosis
arXiv:2603.19295v1 Announce Type: new
Abstract: Mental disorder populations exhibit pronounced heterogeneity -- that is, the significant differences between samples -- poses a significant challenge to the definition of positive pairs in contrastive learning. To address this, we propose a subtype-gu...
Why Agents Fail: The Role of Seed Values and Temperature in Agentic Loops
In the modern AI landscape, an agent loop is a cyclic, repeatable, and continuous process whereby an entity called an AI agent — with a certain degree of autonomy — works toward a goal.
Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams
arXiv:2603.18032v1 Announce Type: new
Abstract: Anomaly and failure detection methods are crucial in identifying deviations from normal system operational conditions, which allows for actions to be taken in advance, usually preventing more serious damages. Long-lasting deviations indicate failures,...
arXiv:2603.17063v1 Announce Type: new
Abstract: Transformers are the dominant architecture in AI, yet why they work remains poorly understood. This paper offers a precise answer: a transformer is a Bayesian network. We establish this in five ways.
First, we prove that every sigmoid transformer wi...
XLinear: Frequency-Enhanced MLP with CrossFilter for Robust Long-Range Forecasting
arXiv:2603.15645v1 Announce Type: new
Abstract: Time series forecasters are widely used across various domains. Among them, MLP (multi-layer perceptron)-based forecasters have been proven to be more robust to noise compared to Transformer-based forecasters. However, MLP struggles to capture complex...
How to Achieve Prototypical Birth and Death for OOD Detection?
arXiv:2603.15650v1 Announce Type: new
Abstract: Out-of-Distribution (OOD) detection is crucial for the secure deployment of machine learning models, and prototype-based learning methods are among the mainstream strategies for achieving OOD detection. Existing prototype-based learning methods genera...
RFX-Fuse: Breiman and Cutler's Unified ML Engine + Native Explainable Similarity
arXiv:2603.13234v1 Announce Type: new
Abstract: Breiman and Cutler's original Random Forest was designed as a unified ML engine -- not merely an ensemble predictor. Their implementation included classification, regression, unsupervised learning, proximity-based similarity, outlier
detection, miss...
Continual Fine-Tuning with Provably Accurate and Parameter-Free Task Retrieval
arXiv:2603.13235v1 Announce Type: new
Abstract: Continual fine-tuning aims to adapt a pre-trained backbone to new tasks sequentially while preserving performance on earlier tasks whose data are no longer available. Existing approaches fall into two categories which include input- and parameter-adap...
Introducing Feature-Based Trajectory Clustering, a clustering algorithm for longitudinal data
arXiv:2603.13254v1 Announce Type: new
Abstract: We present a new algorithm for clustering longitudinal data. Data of this type can be conceptualized as consisting of individuals and, for each such individual, observations of a time-dependent variable made at various times. Generically, the specific...