condition-standardization

name	condition-standardization
description	Standardize evaluation condition differences across papers — 20 methods, 60 data points, 30 web searches budget
dependencies	{"tactics":["condition-normalization"],"sops":["compute-normalization","condition-cataloging","performance-table-assembly"]}

Condition Standardization

Purpose

Papers evaluate methods under different conditions (data splits, preprocessing, hardware, hyperparameter budgets). This strategy identifies condition differences, builds a normalization scheme, and produces fair-comparison baselines where results are adjusted to equivalent conditions.

Budget

Resource	Floor	Target
Methods analyzed	15	20
Data points standardized	40	60
Web searches	20	30
Condition dimensions cataloged	5	10

State Ledger

<HARD-GATE>
| Metric | Current | Target | Status |
|--------|---------|--------|--------|
| Methods analyzed | 0 | 20 | BLOCKED |
| Data points standardized | 0 | 60 | BLOCKED |
| Condition dimensions | 0 | 10 | — |
| Normalization rules defined | 0 | 5 | — |
| Fair comparison sets | 0 | 3 | — |
</HARD-GATE>

Cannot exit until data_points_standardized >= 48 (80% of target).

Available Tactics

• condition-normalization — Compare and standardize experimental conditions

Available SOPs

• condition-cataloging — Record all conditions per method-paper pair
• compute-normalization — Normalize by compute budget (Pareto analysis)
• performance-table-assembly — Produce unified comparison table

Execution Guidance

1. Catalog all condition dimensions across extracted data points
2. Identify which conditions meaningfully affect reported scores
3. Group methods by comparable condition sets
4. Define normalization rules (e.g., adjust for data size, compute budget)
5. Apply compute-normalization for Pareto-optimal analysis
6. Produce fair comparison subsets where conditions are controlled
7. Annotate which comparisons are direct vs. adjusted

Output Format

{
  "condition_dimensions": [
    {
      "name": "string",
      "values_observed": ["string"],
      "impact_on_score": "high|medium|low|unknown",
      "normalization_rule": "string|null"
    }
  ],
  "fair_comparison_sets": [
    {
      "name": "string",
      "controlled_conditions": ["string"],
      "methods_included": ["string"],
      "adjusted_scores": []
    }
  ],
  "pareto_analysis": {
    "compute_vs_performance" 
     
  

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
condition-normalization	Compare and standardize experimental conditions across papers

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
compute-normalization	Normalize results by compute budget (Pareto analysis)
condition-cataloging	Record evaluation conditions (data splits, hyperparams, hardware, seeds) from a paper
performance-table-assembly	Assemble unified comparison table with confidence interval annotations