santa-method

A dual-review convergence loop mitigates single-agent bias by spawning two independent, memory-isolated reviewers to evaluate outputs against a fixed rubric until both pass. This lockstep iteration with fresh reviewers detects regressions and ensures safe results.

You verify high-stakes production code and regulatory documents by applying a deterministic verdict gate with a fixed rubric. Two independent reviewers evaluate the generated output and loop until both pass or escalate the result.

Yes, you can use this bias-mitigation method for batch generation tasks where accuracy matters. The process applies a fixed rubric, memory-isolated reviewers, and a convergence loop with a maximum iteration cap to ensure reliable outputs.

The maximum iteration cap in a deterministic verdict gate limits the convergence loop cycles for AI safety. It prevents endless review rounds by escalating outputs if memory-isolated reviewers cannot both pass the fixed rubric within the cap.

Lockstep iterations use fresh reviewers each round to avoid memory carryover and prevent bias confirmation. This ensures each convergence loop cycle evaluates the generated output independently against the fixed rubric for reliable results.

You should not use a dual-review gate for low-stakes content verification where iteration overhead outweighs accuracy benefits. The convergence loop and memory-isolated reviewers are designed for high-stakes production code, regulatory documents, and batch generation tasks.