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Literature Search

Althea’s Deep Research agent automates the literature review process. It accepts a natural language query, retrieves relevant papers, and synthesizes them into a structured scientific report.

The agent executes a multi-stage workflow to convert a high-level intent into a verified summary.

  1. Query Generation: The system decomposes the user’s prompt into specific academic search queries.
  2. Retrieval: It executes parallel searches across academic databases and the web to identify papers, patents, and technical articles.
  3. Analysis: The agent reads abstracts and key sections of retrieved documents to extract relevant findings.
  4. Synthesis: An inference step identifies common themes, resolves conflicting results, and structures the information.

Case Study: Fire Detection via Remote Sensing

Section titled “Case Study: Fire Detection via Remote Sensing”

Consider a request for a technical feasibility study involving specific constraints.

“I’m looking for scientific literature related to fire detection from remote sensing data. I’m thinking about the possibility of developing a system which can automatically and autonomously finetune a model based on specific geospatial datasets… Can you find relevant papers we could test against doing fire detection with AI systems, and do an assessment of relevant datasets on Zenodo?”

Research Query

The agent initiates retrieval across academic indices and dataset repositories.

Research in Progress

The output is a structured report. In this instance, the agent identified Test-Time Adaptation (TTA) and Meta-Learning as the primary architectural patterns for the user’s proposed workflow.

Report Generation

Beyond literature, the agent performed a technical assessment of candidate datasets on Zenodo (Sen2Fire, EO4WildFires, Fire-D), evaluating them by sensor type (Sentinel-2, Sentinel-5P), label availability, and format compatibility.

Final Report

This process compresses the discovery phase of research, delivering a verified landscape of the field in minutes.

If you’re already reading a paper or research direction in Lacuna, Althea can start from that page’s context.

That is usually the better path for focused questions:

“What is the core claim of this paper?”

“What would I need to reproduce this result?”

“Find the closest neighboring papers and compare the assumptions.”

Lacuna gives Althea the local artifact: paper metadata, extracted concepts, figures, neighboring work, and source links. Literature Search expands outward from there when the question needs a broader sweep.