How Emotion Shapes the Behavior of LLMs and Agents: A Mechanistic Study
arXiv:2604.00005v1 Announce Type: new
Abstract: Emotion plays an important role in human cognition and performance. Motivated by this, we investigate whether analogous emotional signals can shape the behavior of large language models (LLMs) and agents. Existing emotion-aware studies mainly treat em...
Perspective: Towards sustainable exploration of chemical spaces with machine learning
arXiv:2604.00069v1 Announce Type: new
Abstract: Artificial intelligence is transforming molecular and materials science, but its growing computational and data demands raise critical sustainability challenges. In this Perspective, we examine resource considerations across the AI-driven discovery pi...
arXiv:2604.00066v1 Announce Type: new
Abstract: Although Deep Reinforcement Learning has proven highly effective for complex decision-making problems, it demands significant computational resources and careful parameter adjustment in order to develop successful strategies. Evolution strategies offe...
After ChatGPT’s breakthrough, the race to define the next frontier of generative AI accelerated. One of the most talked-about innovations was OpenAI’s Sora, a text-to-video AI model that promised to transform digital content creation.
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, ...
Structural Pass Analysis in Football: Learning Pass Archetypes and Tactical Impact from Spatio-Temporal Tracking Data
arXiv:2603.28916v1 Announce Type: new
Abstract: The increasing availability of spatio-temporal tracking data has created new opportunities for analysing tactical behaviour in football. However, many existing approaches evaluate passes primarily through outcome-based metrics such as scoring probabil...
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...
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...
ProText: A Benchmark Dataset for Measuring (Mis)gendering in Long-Form Texts
We introduce ProText, a dataset for measuring gendering and misgendering in stylistically diverse long-form English texts. ProText spans three dimensions: Theme nouns (names, occupations, titles, kinship terms), Theme category (stereotypically male, stereotypically female, gender-neutral/non-gendere...
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...
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...
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...
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 (...
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 ...
As artificial intelligence becomes central to national security, experts grapple with a technology that remains unpredictable, unregulated, and increasingly powerful.
Less Gaussians, Texture More: 4K Feed-Forward Textured Splatting
Existing feed-forward 3D Gaussian Splatting methods predict pixel-aligned primitives, leading to a quadratic growth in primitive count as resolution increases. This fundamentally limits their scalability, making high-resolution synthesis such as 4K intractable. We introduce LGTM (Less Gaussians, Tex...
If the last wave of AI felt like hiring a very smart intern, this one feels more like managing an entire organization that never sleeps (and occasionally argues with itself).
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...
Drop-In Perceptual Optimization for 3D Gaussian Splatting
Despite their output being ultimately consumed by human viewers, 3D Gaussian Splatting (3DGS) methods often rely on ad-hoc combinations of pixel-level losses, resulting in blurry renderings. To address this, we systematically explore perceptual optimization strategies for 3DGS by searching over a di...