Novelty Check Skill
Verify research idea novelty against recent literature. Use when user says "\u67e5\u65b0", "novelty check", "\u6709\u6ca1\u6709\u4eba\u505a\u8fc7", "check novelty", or wants to verify a research idea is novel before implementing.
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# 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 a secondary Codex agent. Must be an OpenAI model (e.g., `gpt-5.5`, `o3`, `gpt-4o`) - **REVIEWER_BACKEND = `codex`** — Default: Codex xhigh reviewer. Use `--reviewer: oracle-pro` only when explicitly requested; if Oracle is unavailable, warn and fall back to Codex xhigh. ## 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 `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: ```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 ## Review Tracing After each `spawn_agent` or optional `oracle-pro` reviewer call, save the trace following `../shared-references/review-tracing.md`. Write files directly to `.aris/traces/novelty-check/<date>_run<NN>/` and record searched claims, closest papers, reviewer route, raw response, and final novelty decision. Respect the `--- trace:` parameter when present (default: `full`).
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