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:

1. Narrow by domain: "AI in healthcare" → "AI for medical image diagnosis"
2. Narrow by output: "Research X" → "Produce comparison report on X vs Y"
3. Add constraints: "Survey frameworks" → "Survey Python web frameworks (2020+)"
4. 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