#Tool-Use

A researcher released a 10MB LoRA adapter for Qwen3.5-4B that reads internal confidence signals from the model's activations to gate tool-use, reducing hallucination and improving error detection by 0.46 d′ compared to the base model. This approach addresses a key limitation of small language models—their inability to accurately verbalize confidence—by leveraging internal signals, enabling more reliable tool-use and private query handling without requiring larger models. The adapter was trained on 126 hand-authored items and evaluated with confidence intervals; it reduces private query leaks from 22% to 10% and is open-source under Apache-2.0.