@inproceedings{gangi-reddy-etal-2025-infogent,
title = "Infogent: An Agent-Based Framework for Web Information Aggregation",
author = {Gangi Reddy, Revanth and
Mukherjee, Sagnik and
Kim, Jeonghwan and
Wang, Zhenhailong and
Hakkani-T{\"u}r, Dilek and
Ji, Heng},
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.318/",
doi = "10.18653/v1/2025.findings-naacl.318",
pages = "5745--5758",
ISBN = "979-8-89176-195-7",
abstract = "Despite seemingly performant web agents on the task-completion benchmarks, most existing methods evaluate the agents based on a presupposition: the web navigation task consists of a linear sequence of actions with an end state that marks task completion. In contrast, our work focuses on web navigation for information aggregation, wherein the agent must explore different websites to gather information for a complex query. We consider web information aggregation from two different perspectives: i) Direct API-driven Access relies on a text-only view of the Web, leveraging external tools such as Google Search API to navigate the Web and a scraper to extract website contents. (ii) Interactive Visual Access uses screenshots of the webpages and requires interaction with the browser to navigate and access information. Motivated by these diverse information access settings, we introduce Infogent, a novel modular framework for web information aggregation involving three distinct components: Navigator, Extractor, and Aggregator. Experiments on different information access settings demonstrate that Infogent beats an existing SOTA multi-agent search framework by 7{\%} under Direct API-Driven Access on FRAMES and improves over an existing information-seeking web agent by 4.3{\%} under Interactive Visual Access on AssistantBench."
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<abstract>Despite seemingly performant web agents on the task-completion benchmarks, most existing methods evaluate the agents based on a presupposition: the web navigation task consists of a linear sequence of actions with an end state that marks task completion. In contrast, our work focuses on web navigation for information aggregation, wherein the agent must explore different websites to gather information for a complex query. We consider web information aggregation from two different perspectives: i) Direct API-driven Access relies on a text-only view of the Web, leveraging external tools such as Google Search API to navigate the Web and a scraper to extract website contents. (ii) Interactive Visual Access uses screenshots of the webpages and requires interaction with the browser to navigate and access information. Motivated by these diverse information access settings, we introduce Infogent, a novel modular framework for web information aggregation involving three distinct components: Navigator, Extractor, and Aggregator. Experiments on different information access settings demonstrate that Infogent beats an existing SOTA multi-agent search framework by 7% under Direct API-Driven Access on FRAMES and improves over an existing information-seeking web agent by 4.3% under Interactive Visual Access on AssistantBench.</abstract>
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%0 Conference Proceedings
%T Infogent: An Agent-Based Framework for Web Information Aggregation
%A Gangi Reddy, Revanth
%A Mukherjee, Sagnik
%A Kim, Jeonghwan
%A Wang, Zhenhailong
%A Hakkani-Tür, Dilek
%A Ji, Heng
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F gangi-reddy-etal-2025-infogent
%X Despite seemingly performant web agents on the task-completion benchmarks, most existing methods evaluate the agents based on a presupposition: the web navigation task consists of a linear sequence of actions with an end state that marks task completion. In contrast, our work focuses on web navigation for information aggregation, wherein the agent must explore different websites to gather information for a complex query. We consider web information aggregation from two different perspectives: i) Direct API-driven Access relies on a text-only view of the Web, leveraging external tools such as Google Search API to navigate the Web and a scraper to extract website contents. (ii) Interactive Visual Access uses screenshots of the webpages and requires interaction with the browser to navigate and access information. Motivated by these diverse information access settings, we introduce Infogent, a novel modular framework for web information aggregation involving three distinct components: Navigator, Extractor, and Aggregator. Experiments on different information access settings demonstrate that Infogent beats an existing SOTA multi-agent search framework by 7% under Direct API-Driven Access on FRAMES and improves over an existing information-seeking web agent by 4.3% under Interactive Visual Access on AssistantBench.
%R 10.18653/v1/2025.findings-naacl.318
%U https://aclanthology.org/2025.findings-naacl.318/
%U https://doi.org/10.18653/v1/2025.findings-naacl.318
%P 5745-5758
Markdown (Informal)
[Infogent: An Agent-Based Framework for Web Information Aggregation](https://aclanthology.org/2025.findings-naacl.318/) (Gangi Reddy et al., Findings 2025)
ACL
- Revanth Gangi Reddy, Sagnik Mukherjee, Jeonghwan Kim, Zhenhailong Wang, Dilek Hakkani-Tür, and Heng Ji. 2025. Infogent: An Agent-Based Framework for Web Information Aggregation. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 5745–5758, Albuquerque, New Mexico. Association for Computational Linguistics.