buaazhanghaonan/adversarial-research-copilot/novelty-check

Novelty Check Skill

Check whether a proposed method/idea has already been done in the literature: $ARGUMENTS

Constants

• REVIEWER_MODEL = gpt-5.4 — Model used via a secondary Codex agent. Must be an OpenAI model (e.g., gpt-5.4, o3, gpt-4o)

Instructions

Given a method description, systematically verify its novelty:

Phase A: Extract Key Claims

1. Read the user's method description
2. Identify 3-5 core technical claims that would need to be novel:

• What is the method?
• What problem does it solve?
• What is the mechanism?
• What makes it different from obvious baselines?

Phase B: Multi-Source Literature Search

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

1. Known paper databases: Check against:

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

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

Phase C: Cross-Model Verification

Call REVIEWER_MODEL via spawn_agent (spawn_agent) with xhigh reasoning:

reasoning_effort: xhigh

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