research-papers

ML research task

Topic: $ARGUMENTS

Context to read first

• Read CLAUDE.md to understand the current model type, dataset, and problem (classification, regression, time series, etc.)
• Read mlflow_results/latest_run.json to understand current performance — this defines what "improvement" means

Research steps

1. Search for recent papers (2022–2025) on the topic. Use queries like:

   • "[topic] survey 2024"
   • "[topic] state of the art benchmark"
   • "[topic] practical improvements"
   • "arxiv [topic] [current model family]"

2. For each relevant paper/technique found, extract:

   • Core idea in 2-3 sentences
   • Performance gain reported on benchmarks similar to the current task
   • Implementation complexity (drop-in change, requires new architecture, needs more data)
   • Whether it's compatible with the current training stack (PyTorch, MLflow, Databricks)

3. Look for:

   • Data augmentation strategies for the current data type
   • Learning rate schedulers that outperform the current setup
   • Regularization techniques (label smoothing, mixup, stochastic depth, etc.)
   • Architecture modifications relevant to the current model
   • Loss function alternatives if the task is classification/detection

4. Save the full research summary to research/<topic>_<date>.md

Output format

Write a structured report with sections:

• TL;DR: one paragraph, the single most impactful thing to try
• Top 3 recommendations: each with a concrete code snippet or config change
• Why it should work: connect the paper's claims to the current experiment's results
• Implementation steps: what to change in scripts/train.py or configs/

ultrathink