Large Language Models (LLMs) frequently exhibit performance degradation in specialized domains due to distribution shifts. While unsupervised Test-Time Adaptation (TTA) offers a lightweight solution, existing methods suffer from critical flaws: input-centric adaptation lacks reasoning constraints, while output-centric optimization falls into a "Confidence Trap" of reinforced hallucinations. We instead study Few-Shot Anchored TTA, where a small labeled support set is available, and propose Anchor-Tethered Test-Time Adaptation (AT-TTA), a framework that synergizes linguistic alignment with reliable reasoning. AT-TTA employs Dual-View Uncertainty Calibration, minimizing input perplexity for domain alignment and sequence-level output energy for generation consistency. Furthermore, to mitigate catastrophic drift, we construct a "Semantic Anchor" by fine-tuning on the labeled support set and its filtered augmentations, which serves as a stable domain prior and constrains the adaptation trajectory via an Energy-Guided Adaptive Replay strategy. Extensive experiments on the AdaptEval benchmark demonstrate strong performance across diverse domains, effectively preventing model collapse while maintaining high inference efficiency.

When completing knowledge-intensive tasks, humans sometimes need an answer and a corresponding reference passage for auxiliary reading. Previous methods required obtaining pre-segmented article chunks through additional retrieval models. This paper explores leveraging the parameterized knowledge stored during the pre-training phase of large language models (LLMs) to recall reference passage from any starting position independently. We propose a two-stage framework that simulates the scenario of humans recalling easily forgotten references. Initially, the LLM is prompted to recall document title identifiers to obtain a coarse-grained document set. Then, based on the acquired coarse-grained document set, it recalls fine-grained passage. In the two-stage recall process, we use constrained decoding to ensure that content outside of the stored documents is not generated. To increase speed, we only recall a short prefix in the second stage, and then locate its position to retrieve a complete passage. Experiments on KILT knowledge-sensitive tasks have verified that LLMs can independently recall reference passage locations in various task forms, and the obtained reference significantly assists downstream tasks.