arXiv cs.AI· Zhe Xu, Tianhao Liao, Zhenqing Ling, Ying Shen, Yaliang Li, Daoyuan Chen·· 3 小时前AI 评分38
长上下文时代如何重新思考检索：免训练语义树实现自适应上下文构建
Rethinking Retrieval in the Long-Context Era: Training-Free Semantic Trees for Adaptive Context Construction
AI 导读
一项 arXiv 研究提出名为 \ours 的免训练框架，用语义抽象指导证据选择，同时保留原文用于回答。该框架从全文一次生成可复用的语义层级，并通过结构—语义打分与词法归因选取节点。在三个 LongBench 任务上，该框架在四个前沿回答模型上平均 QA-F1 较全上下文提示提升 1.72 分，同时答案上下文长度减少 58.55%，代码已开源。
正文 · 原文
Abstract:Long-context language models can increasingly process entire documents, making full-context prompting a practical alternative to passage retrieval. However, greater capacity determines how much information a model can receive, but not which information best supports a query. This distinction motivates rethinking retrieval: when the full document already fits, can selecting and organizing its evidence improve answering? The challenge is to retain supporting facts and relations while keeping the context focused on the question. To reconcile these goals, we introduce \ours, a training-free framework that uses semantic abstraction to guide selection while retaining original text for answering. \ours constructs a reusable semantic hierarchy from the full document in a single generation, linking node descriptions to source spans. Given a query, structural--semantic scoring and fine-grained lexical attribution nominate shared source nodes; their union with ancestor context preserves structural relations and complete local text. Across four frontier answer models, \ours improves average QA-F1 by 1.72 points over full-context prompting on three LongBench tasks, surpassing the evaluated baselines on the four-model average while reducing answer-context length by 58.55\%. Our code is available at the $\href{this https URL}{repository}$.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.34146 [cs.CL] |
| (or arXiv:2610.34146v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34146 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhe Xu [view email]
[v1]
Sun, 25 Oct 2026 08:28:39 UTC (759 KB)
来源：arXiv cs.AI · arxiv.org