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提出 PAI-KNN，一种通过推理时先验调整而非内存修改来适应概念漂移与类不平衡的 KNN 数据流分类器，在合成数据流基准上取得了与最先进方法竞争的性能。
MIITA is a memory-induced inference-time adaptation framework for continual learning with small language models. It stores correction-direction prototypes and applies gated hidden-state adaptation at inference time to mitigate catastrophic forgetting without updating backbone parameters.
This paper presents the DFKI-MLT system for SemEval-2026 Task 7 on cultural awareness, which applies activation steering to multilingual LLMs using language vectors from parallel FLORES data. The system achieved 86.96% accuracy in the MCQ track, ranking 7th out of 17 teams, and post-hoc analyses reveal that gains are layer-sensitive and vary across language-region pairs.