Zero-shot LLMs exhibit intervention bias in educational advising, over-recommending actions by 43 percentage points, while supervised DT and XGBoost models achieve near-zero calibration error and macro-F1 of 0.79.
Tethered Reasoning: Decoupling Entropy from Hallucination in Quantized LLMs via Manifold Steering,
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Deterministic Decisions for High-Stakes AI. A Zero-Egress Pipeline with the Deployability of RAG and the Accuracy of Machine Learning
Zero-shot LLMs exhibit intervention bias in educational advising, over-recommending actions by 43 percentage points, while supervised DT and XGBoost models achieve near-zero calibration error and macro-F1 of 0.79.