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arXiv · 2606.06906

EASE-TTT: Evidence-Aligned Selective Test-Time Training for Long-Context Question Answering

Xiaopeng Yuan·Zebin Wang·Suwen Wang·Zongxin Yang·Haohan Wang·Yushun Dong
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Abstract

Long-context question answering (QA) remains challenging for smaller language models even when answer-bearing evidence is already present in the input. Existing within-context retrieval methods localize and expose candidate evidence chunks for the question, but they stop at input-level evidence exposure rather than adapting the query-side attention parameters that control how the model allocates attention over full-context positions. In contrast, lightweight test-time adaptation methods, such as query-only test-time training (qTTT), leave evidence localization unresolved because their generic span-level self-supervised objectives do not identify which context positions support the current answer. In this paper, we propose Evidence-Aligned SElective Test-Time Training (EASE-TTT), a within-context retrieval-augmented test-time training framework that converts selected evidence chunks into a soft attention supervision target over their token positions. Instead of replacing the full context with retrieved chunks, EASE-TTT uses the resulting attention target to guide query-side adaptation, with the adapted model generating the final answer from the original full context. Experiments on six LongBench QA tasks and three small decoder-only language models show that EASE-TTT achieves the strongest macro-average performance among full-context inference, retrieval-only baselines, and qTTT, supporting evidence-aligned test-time adaptation in long-context QA.

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Xiaopeng Yuan, Zebin Wang, Suwen Wang, Zongxin Yang, Haohan Wang, Yushun Dong. 2026-06-05. EASE-TTT: Evidence-Aligned Selective Test-Time Training for Long-Context Question Answering. https://arxiv.org/abs/2606.06906

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