Novelty Check

# Novelty Check Skill

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

## Constants

- REVIEWER_MODEL = `gpt-5.5` — Model used via Codex MCP. Must be an OpenAI model (e.g., `gpt-5.5`, `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

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 Codex MCP (`mcp__codex__codex`) with xhigh reasoning.
When the method description plus the Phase-B paper list is more than a short
note, avoid pasting it inline into the MCP prompt. Write a dossier file such as
`NOVELTY_DOSSIER.md` (or a project-local equivalent) containing the method
description, core claims, candidate papers, and the exact questions below, then
send only the file path:
```
mcp__codex__codex:
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    Read the novelty dossier at <absolute path to NOVELTY_DOSSIER.md> and
    follow all instructions in it.
```
Dossier contents 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:

```markdown
## 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
- **Anti-hallucination for Closest Prior Work.** Every paper in the prior-work table must pass pre-search verification via `verify_papers.py` (canonical name resolved per [`shared-references/integration-contract.md`](../shared-references/integration-contract.md) §2; 3-layer arXiv / CrossRef / Semantic Scholar fallback inside the helper itself). Policy D1 (primary + degraded-output fallback): if the helper is unresolved **or** its invocation fails, tag candidate entries `[UNVERIFIED]` and surface the uncertainty rather than dropping them. Never fabricate arXiv IDs, DOIs, or titles from memory. Full protocol in [`shared-references/citation-discipline.md`](../shared-references/citation-discipline.md) § Pre-Search Verification Protocol.

## Review Tracing

After each `mcp__codex__codex` or `mcp__codex__codex-reply` reviewer call, save the trace following `shared-references/review-tracing.md` (Policy C — forensic; never silently skip). Use `save_trace.sh` (resolved per the chain in `shared-references/integration-contract.md` §2) or write files directly to `.aris/traces/<skill>/<date>_run<NN>/`. Respect the `--- trace:` parameter (default: `full`).