paper-scale-research-loop

name	paper-scale-research-loop
description	Use this skill when the goal is to move AutoLabOS outputs from workflow completion toward evidence-backed, baseline-bearing, paper-scale research quality.
contract_version	1
contract_kind	codex_skill
runtime_contract	true
gate	paper_scale_evidence_ceiling
validation	paper_quality_bar_review

Paper-Scale Research Loop

Purpose

Drive real experiments, hypothesis refinement, evidence review, and manuscript improvement until the strongest branch is honestly judged against paper-scale standards.

This skill exists to prevent:

• workflow completion from being misreported as research completion
• write_paper completion from being misreported as paper readiness
• polished text from being mistaken for evidence-backed claims

Use this skill when

Use this skill when the user asks to:

• assess research quality
• improve experiment quality
• judge manuscript readiness honestly
• push a topic toward paper scale
• decide whether outputs are only a memo, a draft, or a serious experimental paper candidate

Required research discipline

Keep the broad topic fixed, but allow hypotheses and experiment design to evolve.

Always evaluate:

• broad topic
• current hypothesis
• related-work grounding
• baseline or comparator presence
• executed experiments
• quantitative result tables
• claim-to-evidence linkage
• limitations and failure cases
• strongest-branch selection
• reviewer-grade evidence scale: sample size, one-example gains, seed replication, train-budget depth, and whether claimed interactions exceed the observed grid evidence

Hard gate

Downgrade the output if any of the following are missing:

• fixed broad topic
• explicit current hypothesis
• grounded related work
• baseline/comparator
• real executed experiment
• quantitative comparison table
• claim-to-evidence linkage
• limitations/failure cases

Also downgrade or block paper-scale progression when:

• the headline gain can be explained by a single changed evaluation example
• positive tuning claims have no repeated-seed support
• evaluation sets or optimizer steps are only smoke/preflight scale
• a method-centered paper misses canonical method references needed for related-work grounding

Review-gate continuation rule

When a review gate blocks paper-scale progression, treat the block as a governed outcome, not as a writing problem.

• Do not force write_paper after a paper-quality reject unless the user explicitly asks for a downgraded memo/draft.
• Follow the recommended transition or backtrack target before attempting manuscript generation again.
• If the objective metric is not met, preserve the negative result and lower the claim ceiling instead of polishing around it.
• If implementation executes fewer conditions, seeds, baselines, or comparisons than the approved design required, treat that as an evidence-scope blocker until rerun or explicitly downgraded.
• Require the next hypothesis/design pass to state what evidence gap it is repairing.
• When review emits node-strengthening recommendations, feed them into the meta-harness or the named upstream node rather than trying to repair the problem only in write_paper.

Output format

1. Current artifact status
2. Research-question status
3. Hypothesis status
4. Related-work depth
5. Experimental evidence status
6. Baseline/comparator status
7. Quantitative result status
8. Claim-to-evidence linkage status
9. Limitations/failure-case status
10. Honest output genre recommendation
11. Strongest next action

Guardrails

• Do not reward polish without evidence.
• Do not over-credit abstract-only or plan-only runs.
• Do not call something paper-ready unless the evidence supports it.
• Prefer the strongest validated branch, not the most verbose one.