Novelty Check

For EACH core claim, search using ALL available sources:

1. Web Search (via WebSearch):

   • Search arXiv, Google Scholar, Semantic Scholar
   • Use specific technical terms from the claim
   • Try at least 3 different query formulations per claim
   • Include year filters for 2024-2026

2. Known paper databases: Check against:

   • ICLR 2025/2026, NeurIPS 2025, ICML 2025/2026
   • Recent arXiv preprints (2025-2026)

3. Read abstracts: For each potentially overlapping paper, WebFetch its abstract and related work section

Phase C: Cross-Model Verification

Call REVIEWER_MODEL via llm-chat MCP (mcp__llm-chat__chat) with xhigh reasoning:

# model: 由 .claude/settings.json 配置

Prompt should include:

• The proposed method description
• All papers found in Phase B
• Ask: "Is this method novel? What is the closest prior work? What is the delta?"

Phase D: Novelty Report

Output a structured report:

## Novelty Check Report

### Proposed Method
[1-2 sentence description]

### Core Claims
1. [Claim 1] — Novelty: HIGH/MEDIUM/LOW — Closest: [paper]
2. [Claim 2] — Novelty: HIGH/MEDIUM/LOW — Closest: [paper]
...

### Closest Prior Work
| Paper | Year | Venue | Overlap | Key Difference |
|-------|------|-------|---------|----------------|

### Overall Novelty Assessment
- Score: X/10
- Recommendation: PROCEED / PROCEED WITH CAUTION / ABANDON
- Key differentiator: [what makes this unique, if anything]
- Risk: [what a reviewer would cite as prior work]

### Suggested Positioning
[How to frame the contribution to maximize novelty perception]

Important Rules

• Be BRUTALLY honest — false novelty claims waste months of research time
• "Applying X to Y" is NOT novel unless the application reveals surprising insights
• Check both the method AND the experimental setting for novelty
• If the method is not novel but the FINDING would be, say so explicitly
• Always check the most recent 6 months of arXiv — the field moves fast