@inproceedings{guo-etal-2026-agora,
title = "{AGORA}: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning",
author = "Guo, Honglin and
Zhang, Qi and
Zhang, Yu and
Li, Weijie and
Zheng, Rui and
Lei, Zhikai and
Peng, Qiyuan and
Xi, Zhiheng and
Gui, Tao and
Zhang, Qi",
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.1237/",
pages = "28211--28228",
ISBN = "979-8-89176-459-0",
abstract = "Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study {\_}archive-grounded reasoning{\_}: locating sparse evidence across a large, messy collection of workplace files, reconciling inconsistent terminology, units, and time conventions, and computing an answer. Existing benchmarks address only parts of this setting and none jointly stresses archive-groundedness, agentic exploration, and cross-domain coverage. We introduce **Agora**, a benchmark pairing 362 questions with eight domain collections of 9,664 authentic documents and 372M tokens, far exceeding any model{'}s context window, so agents must explore deliberately rather than scan exhaustively. Agora is built by an agentic pipeline combining cross-document task synthesis, leakage-preventing obfuscation, and difficulty filtering. Evaluating eight models, we find the task far from solved: even the strongest reaches only 59.4{\%} accuracy, with notable variation across domains."
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<abstract>Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study _archive-grounded reasoning_: locating sparse evidence across a large, messy collection of workplace files, reconciling inconsistent terminology, units, and time conventions, and computing an answer. Existing benchmarks address only parts of this setting and none jointly stresses archive-groundedness, agentic exploration, and cross-domain coverage. We introduce **Agora**, a benchmark pairing 362 questions with eight domain collections of 9,664 authentic documents and 372M tokens, far exceeding any model’s context window, so agents must explore deliberately rather than scan exhaustively. Agora is built by an agentic pipeline combining cross-document task synthesis, leakage-preventing obfuscation, and difficulty filtering. Evaluating eight models, we find the task far from solved: even the strongest reaches only 59.4% accuracy, with notable variation across domains.</abstract>
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%0 Conference Proceedings
%T AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning
%A Guo, Honglin
%A Zhang, Qi
%A Zhang, Yu
%A Li, Weijie
%A Zheng, Rui
%A Lei, Zhikai
%A Peng, Qiyuan
%A Xi, Zhiheng
%A Gui, Tao
%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 guo-etal-2026-agora
%X Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study _archive-grounded reasoning_: locating sparse evidence across a large, messy collection of workplace files, reconciling inconsistent terminology, units, and time conventions, and computing an answer. Existing benchmarks address only parts of this setting and none jointly stresses archive-groundedness, agentic exploration, and cross-domain coverage. We introduce **Agora**, a benchmark pairing 362 questions with eight domain collections of 9,664 authentic documents and 372M tokens, far exceeding any model’s context window, so agents must explore deliberately rather than scan exhaustively. Agora is built by an agentic pipeline combining cross-document task synthesis, leakage-preventing obfuscation, and difficulty filtering. Evaluating eight models, we find the task far from solved: even the strongest reaches only 59.4% accuracy, with notable variation across domains.
%U https://aclanthology.org/2026.emnlp-main.1237/
%P 28211-28228
Markdown (Informal)
[AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning](https://aclanthology.org/2026.emnlp-main.1237/) (Guo et al., EMNLP 2026)
ACL
- Honglin Guo, Qi Zhang, Yu Zhang, Weijie Li, Rui Zheng, Zhikai Lei, Qiyuan Peng, Zhiheng Xi, Tao Gui, and Qi Zhang. 2026. AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 28211–28228, Budapest, Hungary. Association for Computational Linguistics.