Author

Marjan Asadinia

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Conference Sep 2026

The Faithfulness Gap: Evaluating Dependency Faithfulness in LLM Reasoning via Minimal Support Recovery

Current evaluations of logical reasoning in large language models (LLMs) often conflate correct answer prediction with correct logical derivation. We formalize dependency-faithful reasoning: given facts, rules, and a query, a model must output both an entailment label and the rule identifiers forming the minimal suppor...

Solomon Xu, Marjan Asadinia · Coins · 0 citations

Conference Sep 2026

FORGE: An Interpretable Framework for LLM-Assisted Log Anomaly Detection

System log anomaly detection (LogAD) requires balancing detection quality, interpretability, and deployment efficiency. While deep learning approaches achieve strong performance, they often rely on large labeled datasets and lack transparency. Recent large language model (LLM)-based methods improve reasoning but introd...

A. Israni, Marjan Asadinia · Coins · 0 citations

Conference Sep 2026

Safe and Explainable AI for Personalized Insulin Dosing in Type 1 Diabetes

People with Type 1 Diabetes (T1D) make frequent insulin dosing decisions, where both over- and under-dosing carry serious health consequences. Existing digital tools are often reactive, relying on current glucose measurements while under-using key physiological factors such as insulin-on-board, carbohydrate absorption,...

Y. Pansheriya, Marjan Asadinia · Coins · 0 citations