The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap
arXiv:2604.11828v1 Announce Type: new
Abstract: Science is widely regarded as humanity's most reliable method for uncovering truths about the natural world. Yet the \emph{trajectory} of scientific discovery is rarely examined as an optimization problem in its own right. This paper argues that the b...
Linear Programming for Multi-Criteria Assessment with Cardinal and Ordinal Data: A Pessimistic Virtual Gap Analysis
arXiv:2604.09555v1 Announce Type: new
Abstract: Multi-criteria Analysis (MCA) is used to rank alternatives based on various criteria. Key MCA methods, such as Multiple Criteria Decision Making (MCDM) methods, estimate parameters for criteria to compute the performance of each alternative. Nonethele...
Memory-Guided Trust-Region Bayesian Optimization (MG-TuRBO) for High Dimensions
arXiv:2604.08569v1 Announce Type: new
Abstract: Traffic simulation and digital-twin calibration is a challenging optimization problem with a limited simulation budget. Each trial requires an expensive simulation run, and the relationship between calibration inputs and model error is often nonconvex...
Sustained Impact of Agentic Personalisation in Marketing: A Longitudinal Case Study
arXiv:2604.08621v1 Announce Type: new
Abstract: In consumer applications, Customer Relationship Management (CRM) has traditionally relied on the manual optimisation of static, rule-based messaging strategies. While adaptive and autonomous learning systems offer the promise of scalable personalisati...
Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing
arXiv:2604.07366v1 Announce Type: new
Abstract: Partial differential equations (PDEs) govern nearly every physical process in science and engineering, yet solving them at scale remains prohibitively expensive. Generative AI has transformed language, vision, and protein science, but learned PDE solv...
High-Precision Estimation of the State-Space Complexity of Shogi via the Monte Carlo Method
arXiv:2604.06189v1 Announce Type: new
Abstract: Determining the state-space complexity of the game of Shogi (Japanese Chess) has been a challenging problem, with previous combinatorial estimates leaving a gap of five orders of magnitude ($10^{64}$ to $10^{69}$). This large gap arises from the diffi...
SymptomWise: A Deterministic Reasoning Layer for Reliable and Efficient AI Systems
arXiv:2604.06375v1 Announce Type: new
Abstract: AI-driven symptom analysis systems face persistent challenges in reliability, interpretability, and hallucination. End-to-end generative approaches often lack traceability and may produce unsupported or inconsistent diagnostic outputs in safety-critic...
ReVEL: Multi-Turn Reflective LLM-Guided Heuristic Evolution via Structured Performance Feedback
arXiv:2604.04940v1 Announce Type: new
Abstract: Designing effective heuristics for NP-hard combinatorial optimization problems remains a challenging and expertise-intensive task. Existing applications of large language models (LLMs) primarily rely on one-shot code synthesis, yielding brittle heuris...
Proximity Measure of Information Object Features for Solving the Problem of Their Identification in Information Systems
arXiv:2604.04939v1 Announce Type: new
Abstract: The paper considers a new quantitative-qualitative proximity measure for the features of information objects, where data enters a common information resource from several sources independently. The goal is to determine the possibility of their relatio...
Structural Segmentation of the Minimum Set Cover Problem: Exploiting Universe Decomposability for Metaheuristic Optimization
arXiv:2604.03234v1 Announce Type: new
Abstract: The Minimum Set Cover Problem (MSCP) is a classical NP-hard combinatorial optimization problem with numerous applications in science and engineering. Although a wide range of exact, approximate, and metaheuristic approaches have been proposed, most me...
General Explicit Network (GEN): A novel deep learning architecture for solving partial differential equations
arXiv:2604.03321v1 Announce Type: new
Abstract: Machine learning, especially physics-informed neural networks (PINNs) and their neural network variants, has been widely used to solve problems involving partial differential equations (PDEs). The successful deployment of such methods beyond academic ...
arXiv:2604.02434v1 Announce Type: new
Abstract: We study structured abstraction-based reasoning for the Abstraction and Reasoning Corpus (ARC) and compare its generalization to test-time approaches. Purely neural architectures lack reliable combinatorial generalization, while strictly symbolic syst...
DySCo: Dynamic Semantic Compression for Effective Long-term Time Series Forecasting
arXiv:2604.01261v1 Announce Type: new
Abstract: Time series forecasting (TSF) is critical across domains such as finance, meteorology, and energy. While extending the lookback window theoretically provides richer historical context, in practice, it often introduces irrelevant noise and computationa...
Sven: Singular Value Descent as a Computationally Efficient Natural Gradient Method
arXiv:2604.01279v1 Announce Type: new
Abstract: We introduce Sven (Singular Value dEsceNt), a new optimization algorithm for neural networks that exploits the natural decomposition of loss functions into a sum over individual data points, rather than reducing the full loss to a single scalar before...
Forecasting Supply Chain Disruptions with Foresight Learning
arXiv:2604.01298v1 Announce Type: new
Abstract: Anticipating supply chain disruptions before they materialize is a core challenge for firms and policymakers alike. A key difficulty is learning to reason reliably about infrequent, high-impact events from noisy and unstructured inputs - a setting whe...
UQ-SHRED: uncertainty quantification of shallow recurrent decoder networks for sparse sensing via engression
arXiv:2604.01305v1 Announce Type: new
Abstract: Reconstructing high-dimensional spatiotemporal fields from sparse sensor measurements is critical in a wide range of scientific applications. The SHallow REcurrent Decoder (SHRED) architecture is a recent state-of-the-art architecture that reconstruct...
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...
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...
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...
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...