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连接静态与代理式 RAG 用于台湾历史问答
该论文对 agentic RAG 与静态 RAG 进行受控对比，二者共用同一生成器和混合检索后端。结果显示两者总体表现相近，但在 70.83% 的问题上答案不同，优势在平均后基本抵消；逐题 oracle 选择比更优的单管线综合分高 0.2417，说明存在明显的逐题选择空间。作者据此提出后验选择器，比较两种回答及其引用证据，显著优于任一单管线，并恢复 60.34% 的 oracle 提升空间。结论是聚合比较会掩盖检索策略在问题层面的差异，利用互补性可能比追求单一最优管线更有价值。
AI推荐理由 arXiv 论文，作者与摘要明确，属可验证的一手研究。核心新信息：在共享同一生成器与混合检索后端的前提下，对 agentic RAG 与静态 RAG 做受控对比，发现两者聚合表现相近但在 70.83% 的问题上结果不同、优势相互抵消；oracle 逐题选择可将综合分提升 0.2417，作者提出的后验选择器显著优于任一单管线并恢复 60.34% 的 oracle 空间。局限：仅在台湾历史问答这一特定领域验证，摘要未给出绝对分数与数据集规模，结论是否可迁移到其他领域尚不确定。
72
正文
Computer Science > Computation and Language
arXiv:2609.23056 (cs)
[Submitted on 19 Sep 2026]
Title:Bridging Static and Agentic RAG for Taiwanese Historical Question Answering
Authors:Kai-Hsin Chen, Wei-Yu Chen, Xuanjun Chen, Jyh-Shing Roger Jang
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Abstract:Agentic retrieval-augmented generation (RAG) enables language models to adapt retrieval based on previously retrieved evidence, but it remains unclear whether such adaptive orchestration consistently outperforms well-designed static pipelines. We conduct a controlled comparison of agentic and static RAG for Taiwanese historical question answering, sharing the same generator and hybrid retrieval backend. Despite similar aggregate performance, the two pipelines differ on 70.83% of questions, with their advantages largely canceling out when averaged. An oracle that selects the better response per question improves the composite score by 0.2417 over the better individual pipeline, revealing substantial headroom for question-level selection. We therefore introduce a post-hoc selector that compares the two responses and their cited evidence, significantly outperforming either individual pipeline and recovering 60.34% of the oracle headroom. These results show that aggregate comparisons can obscure meaningful question-level differences between retrieval strategies, suggesting that exploiting their complementarity may be more fruitful than seeking a universally superior pipeline.
Subjects:
Computation and Language (cs.CL); Information Retrieval (cs.IR)
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arXiv:2609.23056 [cs.CL]
(or
arXiv:2609.23056v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.23056
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arXiv-issued DOI via DataCite (pending registration)
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From: Kai-Hsin Chen [view email]
[v1]
Sat, 19 Sep 2026 14:43:26 UTC (2,196 KB)
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