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How to check for an input mismatch before blaming the model with Falsify the Problem

Test whether training and evaluation inputs differ before attributing poor results to model capacity.

Who it is forA machine learning researcher whose experiment underperformed and who is about to increase model size.

The problemTeams often assume the model lacks capability when preprocessing differences may explain the result.

The outcomeA verdict on whether the model capability framing survives, with a named test comparing the same saved inputs through both preprocessing paths.

What you need first

  • Git installed on your machine
  • A terminal for the install commands, separate from the agent prompt
  • A host agent that supports Agent Skills or reusable Markdown instructions, such as Codex

Steps

  1. Open a terminal in the root of your project.
  2. mkdir -p .agents/skills
  3. git clone https://github.com/ordinary-s/falsify-the-problem.git .agents/skills/falsify-the-problem
  4. In your agent, invoke the Falsify the Problem skill by name.
  5. Describe the underperforming experiment and ask the skill to test the input path before any change to model capacity.

Prompt for your agent

Use the falsify-the-problem skill on this experiment: <the experiment, the saved inputs, and the training and evaluation preprocessing code>. Test whether an input-path mismatch contributes before attributing the result to model capability. Verify that the output names the single primary discriminating test used in each round.

Caveat. The skill can only compare inputs it can access, so results depend on the saved data and code you share with the host agent.

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