1 citations · 1 across the 4 of their papers we have counts for
6 papers
Deep Agentic Search for Repository-Level Code Question Answering: An Empirical Study
Amirkia Rafiei Oskooei, Bora Ilci, Alperen Kayim +5
Code agents spend much of their effort simply locating the right code inside a repository. Two approaches dominate current practice. In Semantic Search, the agent retrieves code bl…
Asynchronous Pipeline Parallelism for Real-Time Multilingual Lip Synchronization in Video Communication Systems
Eren Caglar, Amirkia Rafiei Oskooei, Mehmet Kutanoglu +2
This paper introduces a parallel and asynchronous Transformer framework designed for efficient and accurate multilingual lip synchronization in real-time video conferencing systems…
Generative AI for Video Translation: A Scalable Architecture for Multilingual Video Conferencing
Amirkia Rafiei Oskooei, Eren Caglar, Ibrahim Sahin +2
The real-time deployment of cascaded generative AI pipelines for applications like video translation is constrained by significant system-level challenges. These include the cumula…
Natural Language Summarization Enables Multi-Repository Bug Localization by LLMs in Microservice Architectures
Amirkia Rafiei Oskooei, S. Selcan Yukcu, Mehmet Cevheri Bozoglan +1
Bug localization in multi-repository microservice architectures is challenging due to the semantic gap between natural language bug reports and code, LLM context limitations, and t…
BreakFun: Jailbreaking LLMs via Object Instantiation under Simulated Code Execution
Amirkia Rafiei Oskooei, Mehmet S. Aktas
Large Language Models (LLMs) are widely used because they process structures, syntax and code well, but this same ability also makes them paradoxically vulnerable. We introduce Bre…
When Many-Shot Prompting Fails: An Empirical Study of LLM Code Translation
Amirkia Rafiei Oskooei, Kaan Baturalp Cosdan, Husamettin Isiktas +1
Large Language Models (LLMs) with vast context windows offer new avenues for in-context learning (ICL), where providing many examples ("many-shot" prompting) is often assumed to en…