---
name: novelty-check
description: "Verify the novelty of research ideas. GPT cross-validation. Trigger phrases: novelty check, has anyone done this, check novelty."
argument-hint: [method-or-idea-description]
---

# Novelty Verification
Verify novelty of: $ARGUMENTS

## Goal
Perform a strict check on whether a method, idea, or experimental setting is actually new. The default stance is skepticism, not help-seeking for supporting evidence.

## Working Principles
- Brutally honest: do not relax the standard just to make something look new.
- `Applying X to Y` is not novel by default unless the application produces an unexpected mechanism, theoretical explanation, or clearly different experimental phenomenon.
- Check the novelty of both the `METHOD` and the `EXPERIMENTAL SETTING`.
- If the method itself is not new, but the findings, conclusions, experimental setup, or failure analysis are new, state that distinction explicitly.
- Always search the last 6 months of arXiv.
- Do not rely on titles alone; read the abstract and, when necessary, the key parts of related work, intro, method, and appendix.

## Workflow

### Phase A: Extract Core Claims
First break the user's method description into 3-5 core technical claims. Each one should be as specific as possible.

For each claim, answer:
- What is the method?
- What problem does it solve?
- What is the key mechanism?
- What is the essential difference from an obvious baseline?

Rewrite the story-like description into searchable technical propositions and avoid vague phrasing.

### Phase B: Multi-source Literature Search
Run multi-source retrieval for each claim, prioritizing recent work and similar settings.

For each claim, try at least 3 search-query sets, and make them complementary:
- Direct technical terms
- Synonyms / abbreviations / related task names
- "Problem + mechanism" combinations
- "Method + dataset / setting" combinations

#### Required Search Channels
1. WebSearch: arXiv / Google Scholar / Semantic Scholar / conference homepages
2. `python3 .claude/tools/arxiv_fetch.py search "QUERY" --max 10`
3. `python3 .claude/tools/semantic_scholar_fetch.py search "QUERY" --max 10`
4. `python3 .claude/tools/exa_search.py search "QUERY" --max 10` (if available)
5. `python3 .claude/tools/openalex_fetch.py search "QUERY" --max 10` (if available)

#### Search Priorities
- ICLR / NeurIPS / ICML 2025-2026
- arXiv preprints from the last 6 months
- Papers close to the method mechanism, not only papers on the same task
- Papers close to the experimental setting, not only papers using the same method

#### Decision Strategy
- Record potentially overlapping papers first; do not exclude them too early
- Prefer reading the abstract, intro, related work, and method sections
- If overlap looks suspicious, also read the experimental setup and appendix

#### Recording Requirements
For each candidate paper, record:
- Title
- Year
- Venue / status
- Relevant point
- The specific reason it may overlap
- Why it might still be a different work

If a data source is unavailable, explicitly record the fallback reason and continue with the others; do not stop the task.

### Phase C: GPT Cross-Validation
Send the method description from Phase A and all candidate papers found in Phase B to `/codex:rescue --fresh --wait` for a second review.

The cross-validation prompt must include:
- proposed method description
- the full candidate paper list
- ask:
  - `Is this method novel?`
  - `What is the closest prior work?`
  - `What is the delta?`

Use high reasoning effort.

The goal of cross-validation is not to find even more papers. It is to force out the closest prior art, the smallest difference, and the risk of pseudo-novelty.

### Phase D: Output Report + Wiki Integration
The output must be in English and follow a fixed structure.

#### Report Format
```markdown
## Novelty Check Report
### Method Under Review
### Core Claims
- Claim 1: ... (novelty: high / medium / low; closest paper: ...)
- Claim 2: ... (novelty: high / medium / low; closest paper: ...)

### Recent Prior Work
| Paper | Year | Venue / Status | Overlap Point | Key Difference |
|---|---:|---|---|---|

### Overall Assessment
- score X/10
- recommendation: continue / continue cautiously / abandon
- key differentiator: ...
- positioning advice: ...
```

#### Evaluation Scale
- `high`: current search shows no close prior art, and the difference is concrete and technical
- `medium`: there is related prior work, but there is still a clear and defensible technical delta
- `low`: mostly a reorganization of known methods, task switching, dataset switching, hyperparameter changes, or standard engineering changes

#### Wiki Integration
If the project has `research-wiki/`, also ingest the knowledge there:
- Create a `claim` entity for each core claim
- Create a `paper` entity for each newly found paper
- Add claim-paper / paper-paper relation edges
- Rebuild `query_pack`

Prefer existing tools such as `.claude/tools/research_wiki.py`; if the wiki does not exist, skip silently and do not error.

Ingestion rules:
- Only write high-confidence information
- Claim names should be short, stable, and reusable
- Edges must include evidence; do not create empty links

## Completion Criteria
Only finish when all of the following are complete:
1. 3-5 core claims have been extracted
2. Multi-source search has been completed, including the last 6 months of arXiv
3. Candidate paper abstracts have been read, and related work / method sections were read when necessary
4. GPT cross-validation has been completed
5. A structured English report has been produced
6. If `research-wiki/` exists, the corresponding writes and `query_pack` rebuild have been completed

## Failure Handling
- Missing tools: record the missing item and degrade gracefully
- Too few search results: expand synonyms, abbreviations, higher-level terms, and experimental settings
- Too many search results: prefer the most recent, most similar, and most likely overlapping work
- Conflicting evidence: read the original abstract and method sections first; do not rely on intuition
