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20232026
most citedDomain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization

1 citations · 3 across the 11 of their papers we have counts for

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cs.IR2026

Trustworthy Domain-Specific AI for Structured Knowledge Retrieval and Reasoning

Ryan C. Barron

This dissertation presents a scalable architecture for transforming unstructured, domain-specific text into structured knowledge for retrieval and reasoning. It integrates semi-aut…

cs.LG2026

Historical Anchors for Scientific Forecasting on Human-Aware Knowledge Graphs

Siu Tin Wu, Ryan C. Barron, Phoebe Upton Sanderbeck +3

Predicting which scientific connections will form next can point researchers toward the most promising leads. One successful approach learns representations by sampling paths throu…

cs.IR2026

Generated Query Expansion Still Helps Strong Sparse Retrieval: A Controlled Study with SPLADE-v3

Ryan C. Barron, Cade W. Trotter, Maksim E. Eren +3

Scientific queries are often brief, while relevant papers use specialized vocabulary. Generated query expansion can bridge this mismatch, but earlier work suggests that its value s…

cs.SE2026

Identity or Prompt Noise? A Calibrated Invariance Audit of LLM Code Generation

Maksim E. Eren, Ryan Barron, Eric Michalak +2

Identity cues are irrelevant to a fixed programming specification, but raw counterfactual differences can arise from unequal samples and prompt wording. We audit 30.73 million exec…

cs.CY2026

Occupational Prompting Reveals Cultural Bias in Large Language Models

Maksim E. Eren, Andrea Brennen, Ryan C. Barron +1

Social roles shape expectations, priorities, and judgments, yet it remains unclear how large language models (LLMs) associate occupational identities with broader cultural value pa…