Program Prototype

AI Scientist

Hypothesis → Experiment → Report

Summary

The AI Scientist is the lab's internal instrument: it standardizes experiments, manages baselines, logs assumptions, and produces concise research notes with uncertainty and reproducibility hooks. The system emphasizes transparency, auditable reports, and discipline in evaluation, enabling faster iteration while maintaining scientific rigor.

In Scope
  • Experiment templates (dataset → protocol → metrics)
  • Baseline registry + ablation tracking
  • Report generator (results + uncertainty + failure modes)
  • Reproducibility hooks and audit trails
Out of Scope
  • Automated hypothesis generation
  • External publication or peer review
  • Domain-specific knowledge beyond general methodology
Evidence Capsule
What Exists Now
  • Experiment templates and workflow standardization
  • Baseline registry and ablation tracking framework
  • Report generator with uncertainty and failure mode reporting
What Comes Next
  • Enhanced reproducibility metrics and validation
  • Integration with external research tools and platforms
  • Advanced uncertainty quantification and decision support
Evaluation Protocol

Evaluation focuses on reproducibility rates, time-to-result metrics, and quality of decision-making under uncertainty. Output must be traceable from claim → experiment → evidence, with all assumptions logged and verifiable.

The protocol measures how effectively the system accelerates research while maintaining scientific rigor. Key metrics include experiment reproducibility, report quality, and the ability to make informed decisions based on uncertain results.

Access & Governance
  • Public Overview: This page and high-level documentation are publicly accessible.
  • Controlled Demo: Interactive demonstrations available to qualified researchers upon request.
  • Partner Evaluation: Full technical documentation, evaluation protocols, and access to experimental harnesses available to research partners.