Jason Academic Writing

Complete academic paper writing pipeline with integrity checks and multi-agent review system. Optimized prompts for Methods/Results/Discussion sections. Features self-counterargument framework, bias matrix, and overclaim self-audit. Use when writing research papers, need citation verification, anti-hallucination checks, multi-perspective review, or auditable process records.

Academic Writing Pipeline

End-to-end academic paper production with built-in quality gates and multi-agent review.

Pipeline Overview

text

Research → Write → Integrity Check → Review → Revise → Summary

Each stage has defined inputs/outputs and quality gates. The pipeline is non-linear: stages may loop (Review → Revise → Re-Review) until quality threshold met.

Stage Details

Stage 1: Research

Goal: Gather and organize evidence.

Actions:

1. Literature search via Semantic Scholar API
2. Filter by relevance score ≥ 0.5
3. Grade evidence level (A: meta-analysis, B: RCT, C: observational, D: opinion)
4. Output: research/evidence.json

Script: scripts/research.py

Stage 2: Write

Goal: Generate structured manuscript.

Actions:

1. Build argument chain from evidence
2. Generate sections: Abstract, Introduction, Methods, Results, Discussion
3. Track citation markers for each claim
4. Output: draft/manuscript.md

Script: scripts/write.py

Stage 3: Integrity Check (CRITICAL)

Goal: Anti-hallucination verification.

Check types:

• Citation verification: DOI exists? Authors match? Year correct?
• Data verification: Numbers match tables/figures?
• Claim verification: Evidence supports assertion?

Threshold: Must pass 100% of checks to proceed.

Script: scripts/integrity_check.py

APIs used:

• Semantic Scholar (https://api.semanticscholar.org)
• CrossRef DOI (https://api.crossref.org/works/)

Stage 4: Review (5-Person Panel)

Agents:

Role	Focus	Score Weight
Editor-in-Chief	Contribution, journal fit	30%
Methodology	Methods, stats, reproducibility	25%
Domain Expert	Related work, theory	20%
Devil's Advocate	Strongest counter-arguments	15%
Synthesizer	Merge opinions, roadmap	10%

Decision mapping:

• ≥80: Accept
• 65-79: Minor Revision
• 50-64: Major Revision
• <50: Reject

Script: scripts/review.py

Stage 5: Revise

Goal: Address reviewer feedback.

Actions:

1. Parse Synthesizer roadmap
2. Generate revision plan with priorities
3. Rewrite affected sections
4. Re-run Integrity Check

Script: scripts/revise.py

Stage 6: Process Summary

Goal: Auditable record.

Output: summary.json containing:

• Timeline of each stage
• Decision points and scores
• Integrity check results
• Reviewer scores and comments
• Revision history

Script: scripts/summary.py

Configuration

Edit config.yaml for:

• Model selection (default: qwen3.5-plus)
• Temperature (default: 0.3 for stability)
• Review thresholds
• API keys

Usage

bash

# Full pipeline
python scripts/main.py --topic "your research topic"

# Single stage
python scripts/main.py --stage integrity-check --input draft/manuscript.md

# With custom config
python scripts/main.py --config custom_config.yaml

Quality Gates

Gate	Requirement	Action on Fail
Evidence	≥5 Grade A/B sources	Return to Research
Integrity	100% verification	Return to Write
Review	≥65 score	Loop Revise
Final Integrity	100% verification	Block submission

Key Principles

1. Integrity First: Citation verification is non-negotiable
2. Quantified Review: Scores enable objective decisions
3. Loopable Pipeline: Revision cycles until threshold met
4. Auditable Output: Process Summary for journal submission

Reference Files

• references/review_rubric.md - Detailed scoring criteria
• references/evidence_levels.md - Evidence grading standards
• references/citation_styles.md - Journal formatting guides