Marginal Baseline Eval for AI Safety Metrics

A lot of work on AI safety eventually turns into a statement about measurement: a model is more aligned, an intervention reduces behavior, a monitor predicts failure or a benchmark improves. If the number that supports that statement mostly follows model size, training loss, architecture or another simple baseline it can give a sense of confidence without offering proof.

MBE is meant to check that before a measurement becomes part of research or rules. It asks what information the measurement provides beyond things we already know whether that help works across models and tasks and whether the measurement reacts properly when the real danger changes. It does not try to say which measurement is best or worst. A measurement might be helpful in one situation and not reliable in another; the goal is to find out where each claim should be trusted.

The project’s main contribution to safety is measurement reliability. As systems grow more complex and harder to understand safety choices will depend more on substitutes, behavior tests, monitors and automated checks. A weak signal can hide a change in ability. Make a bad intervention look good. MBE will therefore include a case study focused on safety along with studies on images and language.

Built and released mbe-eval, an MIT-licensed Python package with command-line tools for marginal-baseline metric auditing. The project also includes a public Kaggle notebook, calibration tests, frozen experimental protocols, reproducibility documentation, and a claim-level evidence ledger.

I also built and released TrainTools, a companion package that makes it easier to record structured training data and produce logs suitable for reproducible analysis and metric auditing. Outside MBE, I maintain several smaller research and engineering projects, including the Mineral World Model. These side projects are exploratory, but they reflect a broader interest in open research infrastructure and model evaluation.

Completed exploratory audits covering 680 logged image and language-model runs and more than 40 candidate metrics. These experiments identified both surviving signals and important failure modes, including leakage in an early language experiment. I publicly withdrew the affected result and corrected the protocol rather than quietly discarding it.

Received an Emergent Ventures grant in January as general support for my independent research; it was not awarded to or used specifically for MBE. My broader work includes computational cosmology and a recent paper investigating the , where careful data processing, statistical controls, sensitivity analysis, and reproducible pipelines are central. That work provided much of the methodological foundation I now bring to MBE. Since then, I have also released open-source research tools and developed the exploratory evidence and protocols behind this project. The funding requested here is specifically for MBE’s confirmatory experiments, protected holdout, public benchmark, and independent replication.