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/research

Conduct background research on a topic relevant to your ML project. This command generates a structured research artifact and saves it to the research directory.

Usage

/research

What It Does

  1. Analyzes project context -- Reads your codebase, recent experiments, and current challenges to determine what research would be most valuable.
  2. Gathers information -- Searches for relevant techniques, papers, best practices, and implementation strategies.
  3. Synthesizes findings -- Produces a structured Markdown document with a clear summary, key references, and actionable recommendations.
  4. Saves the artifact -- Writes the research note to .researchpad/experiments/research/ with a date-prefixed filename.

Example

> /research

This might produce a file like:

.researchpad/experiments/research/2025-04-15-learning-rate-scheduling.md

With contents:

# Learning Rate Scheduling Strategies

## Summary
Explored cosine annealing, warm restarts, and one-cycle policies
for improving convergence on the current dataset.

## Key Findings
- Cosine annealing with warm restarts showed 12% faster convergence
  in similar problem domains
- One-cycle policy is particularly effective for fine-tuning

## References
- Smith & Topin, 2019: Super-Convergence
- Loshchilov & Hutter, 2017: SGDR

## Recommendations
1. Try cosine annealing with T_0=10, T_mult=2
2. Compare against current StepLR schedule

Artifact Format

Each research artifact is a Markdown file saved to:

  • .researchpad/experiments/research/YYYY-MM-DD-topic.md

The file follows a consistent structure: title, summary, key findings, references, and recommendations. The dashboard renders these files in the Research panel.