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This work proposes a new method of learning from positive and unlabeled (PU) examples in highly imbalanced datasets under two labeling mechanisms—selecting positives completely at random (SCAR) and selecting at random (SAR).
This work shows that generative and symbolic AI, paired together, can turn cross-sectional cohorts into a transparent, forecast-oriented framework for modeling disease progression, demonstrated here in ccRCC.
This work investigates a neuro-symbolic agentic framework to enhance the reasoning capabilities of SLMs, specifically Gemma 3 (1B, 4B) and Llama 3.2 (3B), using the CLUTRR kinship benchmark.
A modular Context-Augmented Prompting framework that enables agentic tool use at inference time: a trained GNN expert model provides a predictive hint with confidence, and a GNN extracts an instance-specific explanatory subgraph via a necessity-based edge-drop intervention.
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