3 citations · 3 across the 10 of their papers we have counts for
11 papers
Cartograph: Federated Tool Discovery with Operator-Attested Retrieval for AI Agents
Justice Owusu Agyemang, Michael Agyare, Kwame Opuni-Boachie Obour Agyekum +3
The Model Context Protocol (MCP) enables AI agents to discover and call tools, but loading every definition becomes expensive as connected catalogs grow. We present Cartograph, a f…
Entropy Gate: Entropy Quenching for Near-Lossless Token Compression in LLM Pipelines
Justice Owusu Agyemang, Jerry John Kponyo, Kwame Opuni-Boachie Obour Agyekum +3
LLM pipelines waste substantial token budgets on low-information content: repeated context, verbose responses, and redundant boilerplate. We introduce Entropy Gate, a token compres…
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…
Optimal Quantum Differential Privacy via Fisher Information Spectral Analysis
Justice Owusu Agyemang, Jerry John Kponyo, Elliot Amponsah +1
The Quantum Fisher Information (QFI) metric governs a fundamental duality: it quantifies both how precisely a parameter can be estimated (metrology) and how distinguishable two qua…
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…
Resilient Write: A Six-Layer Durable Write Surface for LLM Coding Agents
Justice Owusu Agyemang, Jerry John Kponyo, Elliot Amponsah +2
LLM-powered coding agents increasingly rely on tool-use protocols such as the Model Context Protocol (MCP) to read and write files on a developer's workstation. When a write fails…
A transformer-based deep q learning approach for dynamic load balancing in software-defined networks
Evans Tetteh Owusu, Kwame Agyemang-Prempeh Agyekum, Marinah Benneh +4
This study proposes a novel approach for dynamic load balancing in Software-Defined Networks (SDNs) using a Transformer-based Deep Q-Network (DQN). Traditional load balancing mecha…