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Scenario 01 - Research Agent Data Source

Research Agent Data Source

SciBase provides searchable, citable, and traceable knowledge objects for Auto-Research, Deep Research, and scientific workflow agents. It turns papers, patents, books, evidence spans, and citation context into a stable upstream knowledge layer for agents.

Research Agent data source illustration
How It Works

From research questions to traceable evidence packs

The core of this scenario is not to "search results" on behalf of people, but to help agents break research questions into retrieval plans, evidence collections, citation chains, and auditable outputs.

Input

Research questions, topic boundaries, method keywords, domain constraints, and which paper, patent, or book types matter.

Process

Expand retrieval paths from multi-source indexes; extract evidence spans, citation context, method contrasts, and source-quality signals.

Output

Agent-facing evidence packs, traceable survey material, candidate citations, research lineage, and directions for follow-up questions.

Detailed Directions

Scenario Examples

Each direction maps to a task unit an agent can invoke directly or compose with others.

Research question understanding and retrieval planning
Direction 01

Research question understanding and retrieval planning

The agent turns natural-language questions into executable retrieval strategies: domain, objects, time range, key methods, exclusion criteria, and acceptable evidence types.

Multi-source evidence pack generation
Direction 02

Multi-source evidence pack generation

Around one question, aggregate evidence spans from papers, patents, books, and datasets while preserving source, location, version, and quality signals so answers stay grounded in provenance.

Automated synthesis and viewpoint alignment
Direction 03

Automated synthesis and viewpoint alignment

The agent can produce survey-style drafts from evidence packs while mapping conclusions, method differences, and open debates back to original spans so human reviewers can verify quickly.

Agent-native API calls
Direction 04

Agent-native API calls

Upstream agents do not need to assemble complex data pipelines; task-oriented APIs return search results, evidence packs, citation context, and follow-up suggestions.

Next

Explore more core scenarios

Research Agent focuses on research workflows; the other two scenarios cover technical novelty search and enterprise knowledge mining respectively.