3 citations · 4 across the 5 of their papers we have counts for
5 papers
MAFA: A Multi-Agent Framework for Enterprise-Scale Annotation with Configurable Task Adaptation
Mahmood Hegazy, Aaron Rodrigues, Azzam Naeem
We present MAFA (Multi-Agent Framework for Annotation), a production-deployed system that transforms enterprise-scale annotation workflows through configurable multi-agent collabor…
Persistent Instability in LLM's Personality Measurements: Effects of Scale, Reasoning, and Conversation History
Tommaso Tosato, Saskia Helbling, Yorguin-Jose Mantilla-Ramos +5
Large language models require consistent behavioral patterns for safe deployment, yet there are indications of large variability that may lead to an instable expression of personal…
MAFA: A multi-agent framework for annotation
Mahmood Hegazy, Aaron Rodrigues, Azzam Naeem
Modern consumer banking applications require accurate and efficient retrieval of information in response to user queries. Mapping user utterances to the most relevant Frequently As…
Enhancing and Scaling Search Query Datasets for Recommendation Systems
Aaron Rodrigues, Mahmood Hegazy, Azzam Naeem
This paper presents a deployed, production-grade system designed to enhance and scale search query datasets for intent-based recommendation systems in digital banking. In real-worl…
Diversity of Thought Elicits Stronger Reasoning Capabilities in Multi-Agent Debate Frameworks
Mahmood Hegazy
Large language models (LLMs) excel in natural language generation but often confidently produce incorrect responses, especially in tasks like mathematical reasoning. Chain-of-thoug…