autosteer 1.0.0

Research direction generator for autoresearch — analyzes experiment history and suggests next steps

autosteer

Research direction generator for autoresearch. Analyzes experiment history and suggests data-driven next steps instead of random-walking through experiment space.

Install

pip install autosteer

Usage

# Get 5 suggestions (default)
autosteer --results results.tsv

# Explore mode — favor untried directions (good when stuck)
autosteer --results results.tsv --strategy explore

# Exploit mode — double down on what works
autosteer --results results.tsv --strategy exploit

# More suggestions
autosteer --results results.tsv --num-suggestions 10

# Quick numbered list
autosteer --results results.tsv --quiet

Strategy Modes

Mode	When to Use
auto	Default. Balances explore/exploit based on experiment count.
explore	Early research, or stuck after 3+ discards. Favors untried categories.
exploit	You have proven wins. Doubles down on what works.

Output

Each suggestion includes:

• Badge: [EXPLORE] or [EXPLOIT] indicating category status
• Risk level: low, medium, or high
• Rationale: Why this was ranked where it is

[1] [EXPLOIT] Tune learning rate warmup schedule          risk: low
    Rationale: Learning rate experiments have 3 keeps in 4 attempts.
[2] [EXPLORE] Try rotary position embeddings               risk: medium
    Rationale: Positional encoding category untested. High potential.

How It Works

• 20 built-in research directions specific to GPT pretraining
• Categorizes past experiments (architecture, hyperparams, optimizer, etc.)
• Keyword deduplication: won't re-suggest failed directions
• Git integration: reads diffs to classify experiments automatically
• Strategy-weighted scoring that adapts to experiment count

Requirements

• Python >= 3.10
• A results.tsv file from autoresearch
• Git repository (for diff-based experiment classification)

License

MIT