works on

From the 1 of 6 linked papers with an AI index.

collaborators

6 papers

quant-ph2026

Optimal Quantum Differential Privacy via Fisher Information Spectral Analysis

Justice Owusu Agyemang, Jerry John Kponyo, Elliot Amponsah +1

The paper proposes a geometry‑aware quantum differential privacy mechanism that aligns noise with the eigenstructure of the Quantum Fisher Information, achieving dramatically lower…

cs.SE2026

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…

cs.MM2026

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…

cs.DC2026

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/…

cs.DC2026

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.…

cs.CR2026

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…