@inproceedings{yan-etal-2026-exploring,
title = "Exploring Information Seeking Agent Consolidation",
author = "Yan, Guochen and
Wu, Jialong and
Tao, Zhengwei and
Li, Bo and
Zhang, Qintong and
Xu, Jiahao and
Mi, Haitao and
Fang, Yuejian and
Shen, Qingni and
Zhang, Wentao and
Wu, Zhonghai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.967/",
pages = "20445--20470",
ISBN = "979-8-89176-462-0",
abstract = "Information-seeking agents have emerged as a powerful paradigm for knowledge-intensive tasks, yet today{'}s systems remain specialized for the open web, documents, or local knowledge bases, hindering scalable and cross-domain deployment. We present the first systematic empirical study of consolidating these information-seeking agents into a single foundation agentic model. We compare two paradigms{---}\textit{data-level mixing}, which trains a unified model on a mixture of datasets, and \textit{parameter-level merging}, which merges independently trained experts in parameter space{---}across 3 training scenarios, evaluating \textbf{26} representative parameter-level methods on \textbf{10} benchmarks. To compare across heterogeneous benchmarks, we introduce a geometric Composite Score and an Imbalance Score that describe overall performance and task skew. Our analysis shows that (i) well-designed parameter-level merging attains parity with data mixing at a fraction of its training cost and is order-agnostic; (ii) parameter-level merging structurally preserves out-of-domain capabilities that data mixing universally forgets; and (iii) cross-scenario stability is strongly tied to consolidation quality. We distil our observations into a method-selection guide and design principles for next-generation merging operators."
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<abstract>Information-seeking agents have emerged as a powerful paradigm for knowledge-intensive tasks, yet today’s systems remain specialized for the open web, documents, or local knowledge bases, hindering scalable and cross-domain deployment. We present the first systematic empirical study of consolidating these information-seeking agents into a single foundation agentic model. We compare two paradigms—data-level mixing, which trains a unified model on a mixture of datasets, and parameter-level merging, which merges independently trained experts in parameter space—across 3 training scenarios, evaluating 26 representative parameter-level methods on 10 benchmarks. To compare across heterogeneous benchmarks, we introduce a geometric Composite Score and an Imbalance Score that describe overall performance and task skew. Our analysis shows that (i) well-designed parameter-level merging attains parity with data mixing at a fraction of its training cost and is order-agnostic; (ii) parameter-level merging structurally preserves out-of-domain capabilities that data mixing universally forgets; and (iii) cross-scenario stability is strongly tied to consolidation quality. We distil our observations into a method-selection guide and design principles for next-generation merging operators.</abstract>
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%0 Conference Proceedings
%T Exploring Information Seeking Agent Consolidation
%A Yan, Guochen
%A Wu, Jialong
%A Tao, Zhengwei
%A Li, Bo
%A Zhang, Qintong
%A Xu, Jiahao
%A Mi, Haitao
%A Fang, Yuejian
%A Shen, Qingni
%A Zhang, Wentao
%A Wu, Zhonghai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yan-etal-2026-exploring
%X Information-seeking agents have emerged as a powerful paradigm for knowledge-intensive tasks, yet today’s systems remain specialized for the open web, documents, or local knowledge bases, hindering scalable and cross-domain deployment. We present the first systematic empirical study of consolidating these information-seeking agents into a single foundation agentic model. We compare two paradigms—data-level mixing, which trains a unified model on a mixture of datasets, and parameter-level merging, which merges independently trained experts in parameter space—across 3 training scenarios, evaluating 26 representative parameter-level methods on 10 benchmarks. To compare across heterogeneous benchmarks, we introduce a geometric Composite Score and an Imbalance Score that describe overall performance and task skew. Our analysis shows that (i) well-designed parameter-level merging attains parity with data mixing at a fraction of its training cost and is order-agnostic; (ii) parameter-level merging structurally preserves out-of-domain capabilities that data mixing universally forgets; and (iii) cross-scenario stability is strongly tied to consolidation quality. We distil our observations into a method-selection guide and design principles for next-generation merging operators.
%U https://aclanthology.org/2026.findings-emnlp.967/
%P 20445-20470
Markdown (Informal)
[Exploring Information Seeking Agent Consolidation](https://aclanthology.org/2026.findings-emnlp.967/) (Yan et al., Findings 2026)
ACL
- Guochen Yan, Jialong Wu, Zhengwei Tao, Bo Li, Qintong Zhang, Jiahao Xu, Haitao Mi, Yuejian Fang, Qingni Shen, Wentao Zhang, and Zhonghai Wu. 2026. Exploring Information Seeking Agent Consolidation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 20445–20470, Budapest, Hungary. Association for Computational Linguistics.