Counterfactual-Variant Prover Connector for AI agents.

It is a specialized capability that forces your AI agent to think from first principles. It prevents the AI from giving you 'canned' answers to famous logic puzzles by making it ignore its training data and focus only on your specific rules.

It works by forcing the agent to identify a classic puzzle template first, then explicitly map out every single difference between your prompt and that classic version before it's allowed to calculate the final answer.

Yes, it is excellent for math logic where the rules or constants are non-standard. It ensures the AI uses your specific numbers rather than assuming common values like gravity or standard unit weights.

Recitation bias happens when an AI recognizes a famous problem and just repeats the answer it saw on the internet. This leads to errors if you've changed even one small detail of the puzzle.

You can connect it through the Vinkius catalog. Once connected, you simply tell your agent to use the capability whenever you give it a complex logic puzzle or a multi-step reasoning task.

No, it's specifically designed for logic puzzles, math problems, and complex rule-based reasoning. For general tasks like summarizing text or writing emails, you don't need to use it.

By introducing structural friction. When an agent is forced to fill a schema requiring explicit separation of variables, mapping of differences, and step-by-step logic, it cannot rely on automatic token generation. The capability rejects any attempt to skip these steps or leak classic parameters.

If no classic signature is detected, the model sets recitationSignatureDetected to false, maps variables, and solves it. However, if the text contains keywords of known puzzles (e.g. Monty Hall, Cheryl), the engine enforces the full counterfactual check to avoid semantic traps.

Yes. It works as an orthogonal check. While the Critical Thinking Prover checks overall cognitive quality, the Counterfactual-Variant Prover focuses specifically on variable isolation and preventing memorization loops in logic and mathematics.