collaborators

7 papers

cs.CV2026

Moving Like a Human: Ego-Motion-Normalized Temporal Signatures for Real-Time Aerial Person Tracking on Milliwatt-Class Hardware

Akbar Anbar Jafari, Cagri Ozcinar, Gholamreza Anbarjafari

Follow-me person tracking must run on the drone itself, where affordable companion computers offer only a few effective int8 GFLOP/s. At typical follow distances a person spans 10-…

cs.LG2026

Complex-Valued Unitary Representations as Classification Heads for Improved Uncertainty Quantification in Deep Neural Networks

Akbar Anbar Jafari, Cagri Ozcinar, Gholamreza Anbarjafari

Modern deep neural networks achieve high predictive accuracy but remain poorly calibrated: their confidence scores do not reliably reflect the true probability of correctness. We p…

cs.AI2026

Responsible AI: The Good, The Bad, The AI

Akbar Anbar Jafari, Cagri Ozcinar, Gholamreza Anbarjafari

The rapid proliferation of artificial intelligence across organizational contexts has generated profound strategic opportunities while introducing significant ethical and operation…

cs.AI2026

A Lightweight Modular Framework for Constructing Autonomous Agents Driven by Large Language Models: Design, Implementation, and Applications in AgentForge

Akbar Anbar Jafari, Cagri Ozcinar, Gholamreza Anbarjafari

The emergence of LLMs has catalyzed a paradigm shift in autonomous agent development, enabling systems capable of reasoning, planning, and executing complex multi-step tasks. Howev…

cs.LG2025

Dynamic Nested Hierarchies: Pioneering Self-Evolution in Machine Learning Architectures for Lifelong Intelligence

Akbar Anbar Jafari, Cagri Ozcinar, Gholamreza Anbarjafari

Contemporary machine learning models, including large language models, exhibit remarkable capabilities in static tasks yet falter in non-stationary environments due to rigid archit…

cs.CV2025

A Mathematical Framework for AI Singularity: Conditions, Bounds, and Control of Recursive Improvement

Akbar Anbar Jafari, Cagri Ozcinar, Gholamreza Anbarjafari

AI systems improve by drawing on more compute, data, energy, and better training methods. This paper asks a precise, testable version of the "runaway growth" question: under what m…