DeepResearchEco: A Recursive Agentic Workflow for Complex Scientific Question Answering in Ecology

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Date

Advisor

Volume

4064

Issue

Journal

Series Titel

CEUR Workshop Proceedings

Book Title

Publisher

Bonn, Germany : CEUR-WS Team, c/o Michael Koch, Gesellschaft für Informatik e.V. (GI)

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Abstract

We introduce DeepResearchEco, a novel agentic LLM-based system for automated scientific synthesis that supports recursive, depth- and breadth-controlled exploration of original research questions—enhancing search diversity and nuance in the retrieval of relevant scientific literature. Unlike conventional retrieval-augmented generation pipelines, DeepResearch enables user-controllable synthesis with transparent reasoning and parameter- driven configurability, facilitating high-throughput integration of domain-specific evidence while maintaining analytical rigor. Applied to 49 ecological research questions, DeepResearch achieves up to a 21-fold increase in source integration and a 14.9-fold rise in sources integrated per 1,000 words. High-parameter settings yield expert-level analytical depth and contextual diversity. Source code available at: https://github.com/sciknoworg/deep-research.

Description

Keywords GND

Conference

SymGenAI4Sci 2025: First International Workshop on Symbolic and Generative AI for Science co-located with Semantics-2025, September 3–5, 2025, Vienna, Austria

Publication Type

BookPart

Version

publishedVersion

License

CC BY 4.0 International