This paper introduces Athena, a novel impact analysis approach that combines dependence graph information with conceptual coupling using deep representation learning.

InfoAtlas is a foundation model that estimates statistical mutual information (MI) in a single forward pass, achieving state-of-the-art accuracy with a massive speedup compared to traditional iterativ…

The paper establishes that for quantifier-free dependence logic formulas, the property of k-coherence is equivalent to first-order rewritability, and analyzes the computational complexity of checking…

This paper studies the existence of polynomial measures of dependence between two random variables that satisfy the data processing inequality and vanish on independence. It proves that no such polyno…

This paper introduces an entropy-based method to generate multiple plausible causal maps (atlases) that accurately reflect the inherent structural ambiguity in complex systems, moving beyond single, o…

This paper investigates the prevalence and impact of relation chains in software review workflows using data from Gerrit, finding that they increase merge times and review effort propagation.

This paper presents a pipeline for transforming historical sources into structured data using machine learning tools and the GRAM-framework, enabling automated, skeletal graphing of actions.

This paper introduces ZipLine, a system for integrative analysis of multivariate graphs through a unified predicate language and learning algorithm.

TRIAGE is a framework for evaluating and diagnosing failures in document-grounded graph-RAG systems by attaching stage-specific metrics.

This paper introduces a new approach for scalable causal discovery in directed acyclic graphs (DAGs) with clustered data and local cluster-level effects.

The paper formalizes the concept of a causal pathway for rare events, showing that testable implications can be derived solely from this pathway abstraction, simplifying complex causal modeling.

The paper analyzes a large dataset of JavaScript packages to demonstrate that a small number of vulnerable dependencies can propagate vulnerabilities across a disproportionately large number of packag…

This paper systematically evaluates the consistency of popular causal discovery benchmarks against real-world scientific literature, revealing significant variability in their accuracy.

This paper proposes Trellis, a data foundation that treats experience graphs from long-horizon agentic tasks as first-class, governed, queryable database state.