Publications

Peer-reviewed journal and conference papers in explainable AI, applied LLM systems, and trustworthy autonomous systems. ORCID: 0009-0003-3359-6939.

Journal Articles

  • 2026

    Domain-Specific vs General-Purpose Large Language Models in Orthodontics: A Blinded Comparison of AlimGPT, GPT-4o, Gemini, and Llama

    S. Aksakallı, B. Giray, C. Temel · Dentistry Journal (MDPI) · Q1 · Impact Factor 3.1 · PubMed-indexed

    A blinded, comparative study evaluating a domain-specific model (AlimGPT) against general-purpose LLMs (GPT-4o, Gemini, and Llama) on orthodontic question answering.

    Status: accepted (Feb 2026)

Conference Papers

  • 2026

    CT-SAFR: Safe and Interpretable Chain-of-Thought Reasoning for Autonomous Robots

    C. Temel · IEEE Conference on Artificial Intelligence (CAI 2026), Granada, Spain

    A multi-layered verification framework spanning structural, physical, semantic, and interpretability layers that addresses the faithfulness problem in Chain-of-Thought-enabled robots. It reports 94.2% detection of unsafe and hallucinated reasoning at sub-500 ms latency, and an 87% reduction in unsafe reasoning outputs in a warehouse-robot case study.

    Status: presented

  • 2026

    Towards Trustworthy Autonomous Robots: An Explainable AI-Based Decision Framework

    C. Temel · IEEE SoutheastCon 2026

    Introduces TRACE (Transparent Reasoning Architecture for Credible Execution), a model-agnostic four-layer framework that traces every autonomous action back to sensor evidence. It reports high evidence-traceability, temporal continuity, and decision-reconstructability across simulated decision cycles, motivated by EU AI Act and ISO 13482 auditability.

    Status: accepted

  • 2026

    Accepted paper, IEEE International Conference on Systems, Man, and Cybernetics (SMC 2026)

    C. Temel · IEEE SMC 2026

    Paper accepted for the IEEE SMC 2026 program. Title and full citation to be added on publication.

    Status: accepted