arXiv AI September 12, 2026 By Bincheng Gu, Min Gao, Zongwei Wang, Yibing Bai, Yulan He, Junliang Yu

Beyond Confidence: Stability-Aware Test-Time Adaptation for LLM Reasoning

The paper introduces TASCO, a test‑time adaptation framework that enhances Large Language Model reasoning by incorporating local stability into confidence‑based adaptation while keeping the model frozen. TASCO optimizes a lightweight task‑level prefix using two perturbation strategies—Random Perturbation for distributional stability and Sharpness‑Aware Perturbation for worst‑case sensitivity—to ensure that high confidence aligns with correctness. Experiments show that TASCO improves reasoning accuracy and token efficiency across various LLMs and benchmarks, and behavioral analyses confirm that it maintains stable confidence without over‑concentrating the predictive distribution.

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