Mustapha Babansoro Sanusi

An On-Device Explainable Random Forest Expert System for Multi-Condition Health Screening on Resource-Constrained Mobile Devices

This paper presents a four-layer on-device expert system architecture for multi-condition health screening on resource-constrained Android devices. The system uses a 50-tree Random Forest (4.2 MB) achieving 94.4% accuracy across 41 conditions with 28 ms inference latency. A sub-m...

R-MCTS: Retrospective Offline Correction for Multi-Agent LLM Task Planning

We present R-MCTS, a retrospective (offline) correction framework for LLM-based multi-agent embodied task planning: failed trajectories are recorded, a lightweight scorer estimates action quality from recorded data alone, a Monte Carlo Tree Search rooted at the failure point prop...