arXiv:2510.26451v1 Announce Type: cross Abstract: Graph condensation (GC) has gained significant attention for its ability to synthesize smaller yet informative graphs. However, existing studies often overlook the robustness of GC in scenarios where the original graph is corrupted. In such cases, we observe that the performance of GC deteriorates significantly, while existing robust graph learning […]
Inside CORE-KG: Evaluating Structured Prompting and Coreference Resolution for Knowledge Graphs
arXiv:2510.26512v1 Announce Type: cross Abstract: Human smuggling networks are increasingly adaptive and difficult to analyze. Legal case documents offer critical insights but are often unstructured, lexically dense, and filled with ambiguous or shifting references, which pose significant challenges for automated knowledge graph (KG) construction. While recent LLM-based approaches improve over static templates, they still generate […]
PRISM: Proof-Carrying Artifact Generation through LLM x MDE Synergy and Stratified Constraints
arXiv:2510.25890v1 Announce Type: cross Abstract: PRISM unifies Large Language Models with Model-Driven Engineering to generate regulator-ready artifacts and machine-checkable evidence for safety- and compliance-critical domains. PRISM integrates three pillars: a Unified Meta-Model (UMM) reconciles heterogeneous schemas and regulatory text into a single semantic space; an Integrated Constraint Model (ICM) compiles structural and semantic requirements into […]
Can AI be Accountable?
arXiv:2510.26057v1 Announce Type: new Abstract: The AI we use is powerful, and its power is increasing rapidly. If this powerful AI is to serve the needs of consumers, voters, and decision makers, then it is imperative that the AI is accountable. In general, an agent is accountable to a forum if the forum can request […]
Transferring Causal Effects using Proxies
arXiv:2510.25924v1 Announce Type: cross Abstract: We consider the problem of estimating a causal effect in a multi-domain setting. The causal effect of interest is confounded by an unobserved confounder and can change between the different domains. We assume that we have access to a proxy of the hidden confounder and that all variables are discrete […]
Aeolus: A Multi-structural Flight Delay Dataset
arXiv:2510.26616v1 Announce Type: cross Abstract: We introduce Aeolus, a large-scale Multi-modal Flight Delay Dataset designed to advance research on flight delay prediction and support the development of foundation models for tabular data. Existing datasets in this domain are typically limited to flat tabular structures and fail to capture the spatiotemporal dynamics inherent in delay propagation. […]
A Process Mining-Based System For The Analysis and Prediction of Software Development Workflows
arXiv:2510.25935v1 Announce Type: cross Abstract: CodeSight is an end-to-end system designed to anticipate deadline compliance in software development workflows. It captures development and deployment data directly from GitHub, transforming it into process mining logs for detailed analysis. From these logs, the system generates metrics and dashboards that provide actionable insights into PR activity patterns and […]
Lean4Physics: Comprehensive Reasoning Framework for College-level Physics in Lean4
arXiv:2510.26094v1 Announce Type: new Abstract: We present **Lean4PHYS**, a comprehensive reasoning framework for college-level physics problems in Lean4. **Lean4PHYS** includes *LeanPhysBench*, a college-level benchmark for formal physics reasoning in Lean4, which contains 200 hand-crafted and peer-reviewed statements derived from university textbooks and physics competition problems. To establish a solid foundation for formal reasoning in physics, […]
Application and Validation of Geospatial Foundation Model Data for the Prediction of Health Facility Programmatic Outputs — A Case Study in Malawi
arXiv:2510.25954v1 Announce Type: cross Abstract: The reliability of routine health data in low and middle-income countries (LMICs) is often constrained by reporting delays and incomplete coverage, necessitating the exploration of novel data sources and analytics. Geospatial Foundation Models (GeoFMs) offer a promising avenue by synthesizing diverse spatial, temporal, and behavioral data into mathematical embeddings that […]
A General Incentives-Based Framework for Fairness in Multi-agent Resource Allocation
arXiv:2510.26740v1 Announce Type: cross Abstract: We introduce the General Incentives-based Framework for Fairness (GIFF), a novel approach for fair multi-agent resource allocation that infers fair decision-making from standard value functions. In resource-constrained settings, agents optimizing for efficiency often create inequitable outcomes. Our approach leverages the action-value (Q-)function to balance efficiency and fairness without requiring additional […]