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Research

Perft at Scale: Building a Reliable Verification Pipeline

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This research entry is the first public snapshot of a larger effort: building a verification pipeline that can support aggressive chess-engine experimentation without losing confidence in correctness. Perft is not the whole story, but it is one of the best places to find regressions early and to keep performance claims honest.

Why this matters

As experimentation gets more ambitious, the cost of a quiet mistake rises. A verification stack has to do more than report totals; it has to make it easy to isolate variance, compare runs, and keep a clean line between trusted baselines and active work.

Current direction

  • Reproducible baseline runs across known-good positions
  • Hardware-aware logging that captures runtime context
  • Exportable artifacts that make anomalies easier to inspect later
  • Clear separation between trusted verification passes and active development runs

This seeded article is already shaped like a production entry: lead, rationale, concrete method, and a place for artifacts when you are ready to attach them.