condition-normalization

Research

Compare and standardize experimental conditions across papers

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Condition Normalization

Purpose

Build a structured understanding of how experimental conditions vary across papers, then define normalization schemes that enable fair method-to-method comparison. Addresses the fundamental problem that papers evaluate under different settings, making raw score comparison misleading.

Stages

Stage 1: Condition Extraction

For each method-paper pair, extract all evaluation conditions:

Training data: size, version, preprocessing, augmentation
Hardware: GPU type, memory, training time
Hyperparameters: learning rate schedule, batch size, epochs
Evaluation protocol: data split version, ensembling, post-processing
Random seeds: number of runs, seed selection, variance reported

Yield: Condition vectors per method-paper pair.

Stage 2: Difference Matrix

Build a matrix showing which conditions differ across methods:

Identify dimensions with high variance across papers
Identify dimensions that are controlled (same across all)
Quantify the impact of each dimension on reported scores (if literature exists)

Yield: Condition difference matrix with impact annotations.

Stage 3: Normalization Scheme

Define rules for adjusting scores to common conditions:

Compute-normalized comparison (score per FLOP)
Data-normalized comparison (score per training example)
Time-normalized comparison (score per GPU-hour)
Identify which normalizations are valid vs. speculative

Yield: Normalization rule set with validity bounds.

Stage 4: Fair Comparison Baseline

Apply normalization to produce fair comparison subsets:

Controlled comparison: methods evaluated under identical conditions
Adjusted comparison: methods with score adjustments applied
Pareto frontier: compute-vs-performance optimal set

Yield: Fair comparison tables with methodology notes.

Minimum Yield

Metric	Floor
Condition dimensions cataloged	5
Methods with full condition vectors	10
Normalization rules defined	3
Fair comparison sets produced	2

SOPs Used

condition-cataloging (for Stage 1)
compute-normalization (for Stage 3)
performance-table-assembly (for Stage 4)

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