| name | topic-refinement |
| description | Guidelines for refining broad or fuzzy ideas into concrete research topics. Use when user input is vague to guide ExtractKeywords and RefineIdea. Includes RESEARCH_TOPIC_SCHEMA and refinement tactics. |
Topic Refinement
Guidelines for turning broad or fuzzy research ideas into concrete, executable topics. Use when the user's idea is vague (e.g. "调研某技术", "research AI") to guide ExtractKeywords and RefineIdea.
Broad vs Specific
When to refine further:
- Broad: Idea is a domain ("AI", "机器学习") or activity ("research") without scope
- Specific: Idea names a method, comparison, or deliverable ("Compare BERT vs GPT for code completion", "Survey federated learning 2020–2024")
If broad, apply refinement tactics before ExtractKeywords.
RESEARCH_TOPIC_SCHEMA
A well-refined topic typically has:
| Field | Description |
|---|
| title | Short, concrete title (e.g. "Federated Learning for Medical Imaging") |
| keywords | 3–5 technical terms for arXiv search |
| tldr | 1–2 sentence summary |
| abstract | 2–4 sentences: problem, approach, contribution |
| refinement_reason | Why this scope was chosen (if narrowed from broader idea) |
Refinement Tactics
When the idea is too broad:
- Narrow by domain: "AI in healthcare" → "AI for medical image diagnosis"
- Narrow by output: "Research X" → "Produce comparison report on X vs Y"
- Add constraints: "Survey frameworks" → "Survey Python web frameworks (2020+)"
- Split by dimension: "Evaluate tools" → "Evaluate by performance" + "Evaluate by ecosystem"
For ExtractKeywords
After refinement, keywords should be:
- Technical and domain-specific
- 3–5 terms
- Aligned with the narrowed scope
For RefineIdea
When the idea was refined, ensure the output:
- Reflects the narrowed scope
- Has clear questions or goals that match the refined topic
- Includes refinement_reason if the scope was narrowed from a broader idea
