Baseline agent — LLM-as-agent symbolic-regression solver

A reference solver for RealSR v3: a multi-turn LLM agent that inspects each task's training data in a Python sandbox, fits constants, and submits a formula module. It reads ONLY the public task (tasks/<type>/<task>/) — context, inputs, target, scoring metric, and data/train.csv — never the private scoring/ tree.

The FIXED interface (system prompt, tool-call protocol, submission contract) lives in the harness and is shared by every solver — see ../harness/AGENT_INTERFACE.md (read this to understand how to test / how to plug in your own agent, evolving or otherwise).

run_baseline.py — run the agent on one task → writes a submission module.
agent.py — the turn loop (a ~40-line wrapper over harness/agent_protocol.step); task.py (public-task loader); call_llm_api.py (multi-provider LLM client); utils.py.

Requirements

pip install numpy scipy pandas pyyaml openai anthropic google-genai

Set the API key for whichever provider your model uses (read from the environment by call_llm_api.py):

export OPENAI_API_KEY=...        # gpt5, gpt5mini, …
export ANTHROPIC_API_KEY=...     # claude-opus-4-7, …
export OPENROUTER_API_KEY=...    # or-… aliases
export GOOGLE_API_KEY=...        # gemini-3.1-pro

Run one task

cd baseline_agent
python run_baseline.py ../tasks/typeI/cepheid_period_luminosity__M_W gpt5mini
# → writes submissions/cepheid_period_luminosity__M_W.py

Add --score to immediately score it with the sibling harness (needs the private scoring/ tree present):

python run_baseline.py ../tasks/typeI/cepheid_period_luminosity__M_W gpt5mini --score

Options: --max-turns N (default 20), --out DIR (default submissions).

Run the whole benchmark

cd baseline_agent
for d in ../tasks/typeI/*/ ../tasks/typeII/*/ ; do
    python run_baseline.py "$d" gpt5mini --out submissions
done

Then score every submission (numeric):

for d in ../tasks/typeI/*/ ../tasks/typeII/*/ ; do
    t=$(basename "$d")
    python ../harness/evaluate_numeric.py score "$d" "submissions/$t.py" > "numeric_out/$t.json"
done

and run validity (../harness/VALIDITY_JUDGE.md, cc subagent). Report the two score columns side by side — there is no weighted total (see the benchmark README → How scores are defined).

Models

Aliases are resolved in call_llm_api.py (OpenAI / Anthropic / Google / DeepSeek / OpenRouter). Use any alias listed there, e.g. gpt5, gpt5mini, claude-opus-4-7, gemini-3.1-pro, deepseek-reasoner.