Author

Amir Saeidi

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Jul 2026

Strategies for Deploying Large Language Models for Ascertaining Clinical Outcomes and Sites of Metastases From Radiology Impressions in Patients With Cancer.

Open-source LLMs, when fine-tuned using labeled data, can effectively automate the ascertainment of key radiophenotypic variables using only the impression section of radiology reports, without the full report text, suggesting that these models may provide a scalable approach for phenotypic characterization of patients with cancer in real-world clinical settings.

S. A. Naqvi, I. Riaz, Amir Saeidi et al. · JCO Clinical Cancer Informat... · 0 citations

#natural language process... Preprint Aug 2026

CAST: Critique-Aware Supervision for Training Reliable Long-Horizon Tool-Calling Agents

CAST, a critique-aware training framework that converts sparse task outcomes into action-level supervision for critique learning and policy optimization, is presented, demonstrating that critique-aware training improves the robustness of LLM agents in realistic dynamic environments.

Amir Saeidi, Zeng Zhang, Rishi Singh et al. · 0 citations