Agent Design-Experiment Command

The agent design-experiment command helps design rigorous experiments to validate research hypotheses or extend existing work, considering resource constraints.

Basic Usage

scoutml agent design-experiment BASE_PAPER HYPOTHESIS [OPTIONS]

Examples

Simple Experiment Design

# Design experiment for hypothesis
scoutml agent design-experiment 2010.11929 \
  "ViT works on small datasets with augmentation"

Resource-Constrained Design

# Design with constraints
scoutml agent design-experiment 2103.00020 \
  "CLIP zero-shot performance improves with domain-specific fine-tuning" \
  --gpu-hours 100 \
  --datasets CIFAR-10 \
  --datasets CIFAR-100

Options

Option	Type	Default	Description
--gpu-hours	INTEGER	None	Available GPU hours
--datasets	TEXT	None	Available datasets (can specify multiple)
--output	CHOICE	rich	Output format: rich/json
--export	PATH	None	Export experiment design to file

Hypothesis Types

Performance Improvement

scoutml agent design-experiment 1810.04805 \
  "BERT performance improves with curriculum learning"

Method Adaptation

scoutml agent design-experiment 2010.11929 \
  "Vision Transformers work for video classification"

Efficiency Claims

scoutml agent design-experiment 2103.00020 \
  "CLIP can be distilled to 10% size with 90% performance"

Domain Transfer

scoutml agent design-experiment 1906.08237 \
  "RoBERTa fine-tuning transfers to low-resource languages"

Experiment Components

1. Hypothesis Analysis

• Hypothesis breakdown
• Testable claims
• Success criteria
• Risk assessment

2. Experimental Design

• Control variables
• Treatment conditions
• Evaluation metrics
• Statistical tests

3. Implementation Plan

• Code modifications
• Data preparation
• Training procedures
• Evaluation pipeline

4. Resource Allocation

• Compute distribution
• Time estimates
• Priority ordering
• Fallback plans

5. Expected Outcomes

• Success scenarios
• Failure modes
• Learning objectives
• Publication potential

Resource Planning

GPU Hours Allocation

# Limited compute budget
scoutml agent design-experiment 2010.11929 \
  "ViT outperforms CNNs on small medical datasets" \
  --gpu-hours 50 \
  --datasets "ChestX-ray14"

The agent will: - Estimate training time - Suggest model sizes - Recommend iterations - Plan ablations

Dataset Constraints

# Work with available data
scoutml agent design-experiment 2103.00020 \
  "CLIP generalizes to new domains via prompt engineering" \
  --datasets ImageNet \
  --datasets "Food-101" \
  --datasets "Stanford-Cars"

Use Cases

Research Validation

# Validate paper claims
scoutml agent design-experiment 2301.08727 \
  "Method X really achieves claimed 95% accuracy" \
  --gpu-hours 200

Method Extension

# Extend to new domain
scoutml agent design-experiment 1810.04805 \
  "BERT works for code understanding with minimal changes" \
  --datasets "CodeSearchNet"

Comparative Studies

# Compare approaches
scoutml agent design-experiment 2010.11929 \
  "ViT vs CNN performance varies by dataset size" \
  --datasets CIFAR-10 \
  --datasets CIFAR-100 \
  --datasets ImageNet

Ablation Studies

# Component analysis
scoutml agent design-experiment 2103.00020 \
  "CLIP text encoder contributes more than vision encoder" \
  --gpu-hours 150

Advanced Usage

Multi-Hypothesis Testing

# Test multiple related hypotheses
hypotheses=(
  "ViT benefits from CNN-style augmentation"
  "ViT requires less augmentation than CNNs"
  "ViT augmentation needs are task-dependent"
)

for hyp in "${hypotheses[@]}"; do
    scoutml agent design-experiment 2010.11929 "$hyp" \
        --gpu-hours 50 \
        --export "experiment_$(echo $hyp | md5sum | cut -c1-8).md"
done

Progressive Experimentation

# Start small, scale up
# Phase 1: Pilot
scoutml agent design-experiment 2103.00020 \
  "CLIP fine-tuning improves domain performance" \
  --gpu-hours 10 \
  --datasets CIFAR-10

# Phase 2: Full study
scoutml agent design-experiment 2103.00020 \
  "CLIP fine-tuning scales across domains" \
  --gpu-hours 200 \
  --datasets CIFAR-10 \
  --datasets ImageNet \
  --datasets "Domain-Specific-Dataset"

Reproducibility Studies

# Design reproduction experiment
scoutml agent design-experiment 1810.04805 \
  "BERT results are reproducible with different seeds" \
  --gpu-hours 500 \
  --export bert_reproducibility.md

Output Examples

Experimental Protocol

## Experiment Design: ViT on Small Datasets

### Hypothesis
Vision Transformers can achieve competitive performance on small 
datasets when combined with strong augmentation strategies.

### Experimental Setup
1. **Baseline**: ViT-S/16 trained on CIFAR-10
2. **Treatment**: Add RandAugment, MixUp, CutMix
3. **Control**: ResNet50 with same augmentations

### Metrics
- Top-1 accuracy
- Training efficiency (samples to convergence)
- Overfitting indicators

### Resource Allocation
- 20 GPU hours: Baseline experiments
- 40 GPU hours: Augmentation experiments
- 40 GPU hours: Ablation studies

JSON Output

{
  "hypothesis": "ViT works on small datasets with augmentation",
  "design": {
    "type": "controlled_experiment",
    "independent_variables": ["augmentation_strategy"],
    "dependent_variables": ["accuracy", "convergence_speed"],
    "control_group": "ViT without augmentation",
    "treatment_groups": ["ViT+RandAugment", "ViT+MixUp", "ViT+Both"]
  },
  "resources": {
    "estimated_gpu_hours": 85,
    "runs_per_condition": 3,
    "models": ["ViT-S/16", "ResNet50"]
  },
  "implementation": {
    "code_changes": [...],
    "data_pipeline": [...],
    "evaluation": [...]
  }
}

Best Practices

Hypothesis Formation

1. Be specific - Vague hypotheses lead to poor experiments
2. Make it testable - Define clear success criteria
3. Consider null hypothesis - What if it doesn't work?
4. Scope appropriately - Match hypothesis to resources

Experimental Design

1. Control variables - One change at a time
2. Multiple runs - Account for randomness
3. Proper baselines - Fair comparisons
4. Statistical rigor - Significance testing

Resource Management

1. Budget buffer - Add 20% for issues
2. Prioritize core - Essential experiments first
3. Plan checkpoints - Save intermediate results
4. Have backups - Alternative approaches

Common Workflows

Publication Pipeline

# 1. Initial idea
HYPOTHESIS="Novel augmentation improves ViT on small data"

# 2. Design experiment
scoutml agent design-experiment 2010.11929 "$HYPOTHESIS" \
  --gpu-hours 200 \
  --export experiment_plan.md

# 3. Get implementation
scoutml agent implement 2010.11929

# 4. Run experiments (follow design)

# 5. Write paper
scoutml review "data augmentation vision transformers" \
  --export related_work.md

Grant Proposal Support

# Design experiments for proposal
scoutml agent design-experiment 2103.00020 \
  "Multi-modal learning improves medical diagnosis" \
  --gpu-hours 1000 \
  --datasets "MIMIC-CXR" \
  --datasets "CheXpert" \
  --export grant_experiments.md

Tips and Tricks

Strong Experiments

1. Multiple baselines - Show broad improvement
2. Ablation studies - Understand components
3. Error analysis - Learn from failures
4. Reproducibility - Document everything

Common Pitfalls

1. Too ambitious - Start smaller
2. Poor controls - Missing baselines
3. P-hacking - Define metrics upfront
4. Resource underestimation - Buffer time

Publication Strategy

1. Novel angle - New perspective on known method
2. Thorough evaluation - Multiple datasets/metrics
3. Clear story - Hypothesis → Design → Results
4. Open science - Share code/data

Related Commands

• paper - Understand base paper
• agent implement - Get implementation
• agent critique - Learn from paper's experiments
• review - Survey related work