Self-Improving Open Scientific Agents

About this multi-agent system

A self-improving research loop for scientific AI agents.

This agent is a showcase for AI systems that do not just answer once and stop. It routes a scientific request through specialist agents, critiques the output, measures improvement against benchmark gates, stages human-approved memory, and prepares the next run with a clearer plan.

Critique loopGenerates a draft, challenges it, tracks failure modes, and records why the next version should improve.

Benchmark gateUses quality, risk, reproducibility, and critic signals before any lesson becomes reusable memory.

Open AI architectureDemonstrates OpenClaw-style orchestration and Hermes-compatible backend lanes in a public research sandbox.

AI Lab connectorFeeds lessons back into Statistics, Numerical Analysis, and Optimal Control/OR companion agents.

Shared Scientific Agent Protocol scientific-agent-protocol-v1

The self-improving agent uses the same seven-stage handoff contract to judge and improve the Statistics, Numerical Analysis, and Optimal Control + OR agents.

1 Question

Turn the request into a scoped scientific question and data/model brief.

2 Assumptions

Record data, model, numerical, and decision assumptions before method choice.

3 Method Choice

Select a defensible statistical, numerical, or optimization method with alternatives.

4 Code Artifact

Produce Python, R, and MATLAB/Octave drafts with aligned variables and parameters.

5 Diagnostics

Run checks for missingness, stability, feasibility, uncertainty, or reproducibility.

6 Critique

Let the judge/critic challenge overclaims, weak evidence, and missing tests.

7 Final Memo

Return a cautious memo with evidence, limits, next steps, and downloadable artifacts.

Download the memo, notebook, audit CSV, or a zipped artifact bundle from the latest self-improvement run.

Self-Improving Open Scientific Agents

Control Room