contradiction-detection

name	contradiction-detection
description	Scan papers for conflicting empirical claims, methodological disagreements, or opposing conclusions on the same topic. Use when writing discussion sections, evaluating conflicting results, or checking if a claim is contested before building on it.
always	false

Contradiction Detection

When to Use

• User asks "do any of these papers disagree with each other?"
• User is writing a discussion section and needs to address conflicting findings
• User wants to know if a claim they're making is contested in the literature
• User asks "why do paper A and paper B get different results on [benchmark]?"
• Before building on a result, check whether other papers contradict it

Contradiction Types

Type	Description	Example
Empirical	Different quantitative results on same benchmark	Paper A: BLEU 42.1 vs Paper B: BLEU 31.8 on WMT14
Methodological	Different "best practices" claimed	Paper A: dropout improves generalization; Paper B: dropout hurts large models
Interpretive	Same data, different explanations	Paper A: gains come from depth; Paper B: gains come from width

Workflow

Step 1: Collect Claims

For each paper in scope:

• Use paper-fetch or paper-read-pdf to get full text
• Extract explicit quantitative results (numbers, metrics, rankings)
• Extract qualitative conclusions ("X is better than Y because...")
• Note: dataset used, evaluation protocol, model size, hyperparameters

Step 2: Group by Topic/Benchmark

• Cluster claims that address the same research question, dataset, or benchmark
• Only compare papers evaluating the same thing (different datasets ≠ contradiction)

Step 3: Identify Contradictions

For each cluster, compare claims:

• Do any papers report significantly different numbers on the same benchmark?
• Do any papers draw opposite conclusions from similar setups?
• Do any papers explicitly refute or question a finding from another?

Step 4: Assess Severity

• Minor discrepancy: differences explainable by hyperparameters, compute, random seed
• Significant disagreement: different conclusions with similar setups — needs investigation
• Fundamental contradiction: one paper's results invalidate another's claims

Step 5: Trace Causes

For each contradiction identified, check:

1. Different datasets or splits?
2. Different evaluation metrics or protocols?
3. Different model sizes or training budgets?
4. Different time periods (newer work may use better baselines)?
5. Was one paper later retracted or corrected?

Output Format

For each contradiction pair:

### Contradiction: [Brief description]
- **Type**: empirical | methodological | interpretive
- **Severity**: minor | significant | fundamental
- **Paper A**: [Title (Year)] — [claim with metric/number]
- **Paper B**: [Title (Year)] — [conflicting claim]
- **Potential causes**: [dataset difference / eval protocol / compute / other]
- **Resolution**: [which paper has stronger evidence, or "unresolved — requires investigation"]
- **Action**: [cite both with hedging / investigate further / defer to stronger evidence]

Organize contradictions by topic cluster.

Hedging Language for Contradictions

When writing about contested claims, use calibrated language:

• "Results are mixed: [A] report X while [B] find Y, potentially due to..."
• "Evidence is contested. [A] demonstrate X, though [B] challenge this finding..."
• "While [A] suggest X, subsequent work by [B] finds the opposite under [different conditions]"

Save Results

• Write to contradictions_{topic}_{date}.md
• Update MEMORY.md with any critical contradictions that affect the research direction

Integration Tips

• Combine with evidence-grading — when two papers contradict, grade which has stronger evidence
• Combine with gap-analysis — unresolved contradictions are research gaps worth filling
• Run after cross-paper-synthesis to surface tensions in the synthesized narrative