What Does Your Result Actually Establish?
A model outperforms its baseline. A simulation agrees with the data. A reduced representation preserves 99% of the variance.
None of these results, on its own, establishes the conclusion drawn from it.
Second Mind Method examines the evidence behind scientific AI and simulation results, identifies where their validity breaks down, and determines which tests are needed before publication, deployment or investment.
Independent
Scientific
Review
Your model performs better. Is the comparison scientifically valid?
We examine benchmark design, baseline fairness, information access, statistical uncertainty and competing explanations. A reported improvement may reflect the method, the evaluation protocol or an advantage hidden in the inputs.
What you receive: A documented assessment of supported claims, methodological weaknesses and the specific tests required to establish a credible improvement.
Decision Stress Testing
Your model predicts accurately. Does it still select the right action?
Small global errors can conceal physically important information. A displaced shock, a missed thermal hotspot or a lost detector response tail may change the preferred intervention.
We test representation changes, physical constraints, evaluation thresholds and decision costs. We locate ranking reversals, examine their consequences against reliable references and test possible corrections.
What you receive: A decision fidelity audit identifying where the recommendation changes, why it changes and what evidence supports a safer choice.
Evidence
Assessment
You have a result. Is the evidence sufficient to defend the claim?
Before a paper is submitted, a model is deployed or an investment is approved, we examine the chain connecting observations, assumptions, calculations and conclusions.
We identify unsupported interpretations, missing controls and untested physical assumptions, then prioritize the experiments or analyses that could resolve them.
What you receive: A structured evidence assessment showing what is established, what remains uncertain and which validation steps deserve priority.
Preparing to publish, deploy or invest in a scientific AI result?
Bring us the claim and the evidence behind it. We examine critical assumptions, test competing explanations and identify what still needs to be established before publication, deployment or investment.