Ryan A. Rossi

EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networks

Sparse GNN training reduces computation, but deciding which edges to keep can be costly. Reusing one sparse graph is cheap, but locks training to a fixed topology, while varying it across epochs can require repeated sampling or recomputation. We introduce EDiS (Edge-Disjoint Subgraph sparsification framework), which se...

Prompt2Skill: Unsupervised Skill Optimization From Natural Language Instructions

Skills are external artifacts that Large Language Models (LLMs) consume at inference time to improve their performance on specialized domains by incorporating relevant procedural and domain knowledge. Expert-authored skills are expensive to produce, and the resulting artifacts are not optimized for the specific model t...

Scaffold: Support Graph Theory Based Sparsification for Graph Neural Networks

Graph neural networks (GNNs) rely on message passing over graph edges, making their computational and memory costs strongly dependent on graph density. Graph sparsification offers a natural way to reduce these costs, but removing edges indiscriminately can distort important communication structure and degrade predictiv...

Reasoning Graph-Structured Question Answering: Datasets and Insights from LLM Benchmarking

Unifying Graph Neural Networks Through a Common Layer Equation

A common layer equation is introduced that represents covered architectures through seven components: an update domain, channel set, propagation bank, per-channel message maps, channel-fusion operator, ego/residual map, and update map, which exposes the empirical inverse problem of mapping measurable graph and task pro...

Personalized Auto-Research: Towards a True AI Co-Scientist

This work introduces the problem of personalized auto-research, which conditions every stage of the research process on a representation of the individual researcher, and proposes a general and flexible framework that threads a graph-grounded researcher context through retrieval, hypothesis search, experimentation, wri...