@inproceedings{kim-etal-2026-every,
title = "Every Answer Has Its Own Path: Agentic Routing for Retrieval-Augmented Table Question Answering",
author = "Kim, A Young and
Shin, Jisu and
Han, Donghee and
Yi, Mun Yong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1589/",
pages = "36127--36151",
ISBN = "979-8-89176-459-0",
abstract = "Documents that users query mix tables with prose, but the QA systems built for them do not: retrieve-and-read pipelines flatten tables, while text-to-SQL pipelines ignore passages. Corporate filings, scientific reports, and Wikipedia articles all sit on this fault line, and a single user question routinely crosses it. We present ATR (AgenticTableRAG), an agentic framework for hybrid table-text question answering. ATR decomposes each question into ordered sub-queries and dispatches each to one of four execution primitives: text reading, cell retrieval, SQL execution, or a hybrid of cell grounding and SQL. When the retrieved evidence does not support the predicted answer, the router is re-invoked with the failed-route history and dispatches a different primitive. Value grounding composed with constrained SQL execution rules out column and value hallucinations by construction. The routing policy is distilled into a lightweight student model that transfers across benchmarks without additional inference-time LLM calls. A single ATR configuration leads on token F1 across three benchmarks without per-dataset tuning."
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<abstract>Documents that users query mix tables with prose, but the QA systems built for them do not: retrieve-and-read pipelines flatten tables, while text-to-SQL pipelines ignore passages. Corporate filings, scientific reports, and Wikipedia articles all sit on this fault line, and a single user question routinely crosses it. We present ATR (AgenticTableRAG), an agentic framework for hybrid table-text question answering. ATR decomposes each question into ordered sub-queries and dispatches each to one of four execution primitives: text reading, cell retrieval, SQL execution, or a hybrid of cell grounding and SQL. When the retrieved evidence does not support the predicted answer, the router is re-invoked with the failed-route history and dispatches a different primitive. Value grounding composed with constrained SQL execution rules out column and value hallucinations by construction. The routing policy is distilled into a lightweight student model that transfers across benchmarks without additional inference-time LLM calls. A single ATR configuration leads on token F1 across three benchmarks without per-dataset tuning.</abstract>
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%0 Conference Proceedings
%T Every Answer Has Its Own Path: Agentic Routing for Retrieval-Augmented Table Question Answering
%A Kim, A. Young
%A Shin, Jisu
%A Han, Donghee
%A Yi, Mun Yong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F kim-etal-2026-every
%X Documents that users query mix tables with prose, but the QA systems built for them do not: retrieve-and-read pipelines flatten tables, while text-to-SQL pipelines ignore passages. Corporate filings, scientific reports, and Wikipedia articles all sit on this fault line, and a single user question routinely crosses it. We present ATR (AgenticTableRAG), an agentic framework for hybrid table-text question answering. ATR decomposes each question into ordered sub-queries and dispatches each to one of four execution primitives: text reading, cell retrieval, SQL execution, or a hybrid of cell grounding and SQL. When the retrieved evidence does not support the predicted answer, the router is re-invoked with the failed-route history and dispatches a different primitive. Value grounding composed with constrained SQL execution rules out column and value hallucinations by construction. The routing policy is distilled into a lightweight student model that transfers across benchmarks without additional inference-time LLM calls. A single ATR configuration leads on token F1 across three benchmarks without per-dataset tuning.
%U https://aclanthology.org/2026.emnlp-main.1589/
%P 36127-36151
Markdown (Informal)
[Every Answer Has Its Own Path: Agentic Routing for Retrieval-Augmented Table Question Answering](https://aclanthology.org/2026.emnlp-main.1589/) (Kim et al., EMNLP 2026)
ACL