arXiv AI October 26, 2026 By Adeyemi Adeseye, Aisvarya Adeseye, Hannu Tenhunen, Jouni Isoaho

Evidence-Grounded SLM Behavioral Prediction Architecture for Future Assistance Use

The paper introduces Evidence‑Grounded Local Behavioral Prediction (ELBP), a four‑layer architecture that uses deterministic interaction processing, dual‑view behavioral representation, small language model (SLM) prediction, and evidence verification with abstention to forecast future assistance use. Evaluated on three instruction‑tuned SLMs, the best model (Qwen2.5‑3B) achieved an F1‑score of 0.80 and PR‑AUC of 0.84, outperforming raw‑event prompting and XGBoost by significant margins. With evidence verification and selective abstention, the system’s selective F1 rose to 0.84 while maintaining 91% coverage and 96% evidence support, demonstrating that structured behavioral representation and evidence‑verified prediction enhance reliability in on‑device SLM forecasting.

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