Literature Deep Research Strategy (Enhanced)

A systematic approach to comprehensive literature research that starts with target disambiguation to prevent missing details, uses evidence grading to separate signal from noise, and produces a content-focused report with mandatory completeness sections.

KEY PRINCIPLES:

1. Target disambiguation FIRST - Resolve IDs, synonyms, naming collisions before literature search
2. Right-size the deliverable - Use Factoid / Verification Mode for single, answerable questions; use full report mode for “deep research”
3. Report-first output - Default deliverable is a report file; an inline answer is allowed (and recommended) for Factoid / Verification Mode
4. Evidence grading - Grade every claim by evidence strength (mechanistic paper vs screen hit vs review vs text-mined)
5. Mandatory completeness - All checklist sections must exist, even if "unknown/limited evidence"
6. Source attribution - Every piece of information traceable to database/tool
7. English-first queries - Always use English terms for literature searches and tool calls, even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language

Workflow Overview