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TOPIC · ENTITY #6811

expected calibration error (ECE)

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RESEARCH · 1 SOURCE · arXiv cs.AI

ELBP: Evidence-Grounded SLM architecture improves on-device behavioral prediction (arXiv:2610.32193v1)

This arXiv preprint introduces Evidence-Grounded Local Behavioral Prediction (ELBP), a four-layer architecture that combines deterministic event processing, dual-view behavioral representations, SLM-based prediction, and evidence verification with abstention to anticipate future assistance use from interaction events. In experiments with instruction-tuned models ≤3B parameters, Qwen2.5-3B plus ELBP reached a full-set F1 of 0.80 and PR-AUC of 0.84 (≈15.9% relative gain over raw-event prompting and 17.6% over XGBoost) and substantially reduced ECE; selective prediction with evidence verification achieved F1 0.84 at 91% coverage with 96% evidence support.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

Auditable, calibrated selective-deferral framework for dementia-related crash severity prediction (arXiv:2609.22694v1)

This arXiv preprint presents a reproducible, leakage-controlled evaluation and a selective-deferral framework for dementia-related crash severity prediction using 4,781 Texas crash records. The authors compare structured, narrative, fusion, calibrated fusion, BERT-family, and local LLM baselines; report Gemma (leakage-controlled) as highest observed macro-F1 = 0.545 (95% CI [0.507, 0.583]), the best calibrated fusion model with macro-F1 = 0.522 and ECE = 0.033, and show that selective deferral (70% coverage) raises macro-F1 to 0.573 and lowers severity cost to 0.577 while deferring cases for proposed human review.

6.0