# DeepResearch$^{\\text{Eco}}$: A Recursive Agentic Workflow for Complex Scientific Question Answering in Ecology

Source: [DeepResearch$^{\\text{Eco}}$: A Recursive Agentic Workflow for Complex Scientific Question Answering in Ecology](<https://www.sota2.com/research/paper/68fa7a0c1e209131cea80c8a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:57:50.280Z

Authors: Jennifer D'Souza, Endres Keno Sander, Andrei Aioanei

Published: 2025-07-14T17:47:28.000Z

## Abstract

We introduce DeepResearch$^{\\text{Eco}}$, 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2507.10522>)

[PDF](<https://arxiv.org/pdf/2507.10522.pdf>)