3 citations · 3 across the 7 of their papers we have counts for
7 papers
The Streaming Reservoir Convergence Theorem: A Prospect-Theoretic Framework for Multi-Provider Adaptive Streaming
Justice Owusu Agyemang, Jerry John Kponyo, Kwame Opuni-Boachie Obour Agyekum +4
We present the Streaming Reservoir Convergence Theorem (SRCT), a novel mathematical framework for multi-provider adaptive bitrate streaming that addresses three fundamental structu…
HiveMind: OS-Inspired Scheduling for Concurrent LLM Agent Workloads
Justice Owusu Agyemang, Jerry John Kponyo, Obed Kwasi Somuah +3
When multiple LLM coding agents share a rate-limited API endpoint, they exhibit resource contention patterns analogous to unscheduled OS processes competing for CPU, memory, and I/…
When Agents Go Quiet: Output Generation Capacity and Format-Cost Separation for LLM Document Synthesis
Justice Owusu Agyemang, Michael Agyare, Miriam Kobbinah +2
LLM-powered coding agents suffer from a poorly understood failure mode we term output stalling: the agent silently produces empty responses when attempting to generate large, forma…
Robustness Analysis of Machine Learning Models for IoT Intrusion Detection Under Data Poisoning Attacks
Fortunatus Aabangbio Wulnye, Justice Owusu Agyemang, Kwame Opuni-Boachie Obour Agyekum +3
Ensuring the reliability of machine learning-based intrusion detection systems remains a critical challenge in Internet of Things (IoT) environments, particularly as data poisoning…
Local-Splitter: A Measurement Study of Seven Tactics for Reducing Cloud LLM Token Usage on Coding-Agent Workloads
Justice Owusu Agyemang, Jerry John Kponyo, Elliot Amponsah +2
We present a systematic measurement study of seven tactics for reducing cloud LLM token usage when a small local model can act as a triage layer in front of a frontier cloud model.…
LLM-Redactor: An Empirical Evaluation of Eight Techniques for Privacy-Preserving LLM Requests
Justice Owusu Agyemang, Jerry John Kponyo, Elliot Amponsah +2
Coding agents and LLM-powered applications routinely send potentially sensitive content to cloud LLM APIs where it may be logged, retained, used for training, or subpoenaed. Existi…