Observed arrival · 2026-11-14
Counterphase: a local audit for quantitative research
Counterphase lets quantitative teams test binary forecast records for missing outcomes, timing inconsistencies, and fragile research conclusions.
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- Quantitative teams validating binary forecast research
- Worth noticing
- The page warns that passing its tests cannot prove a model never leaked information or certify timestamps, returns, or compliance.
Field notes
The demonstration starts with 20 time slots: four selected outcomes and 16 unused ones. Its synthetic failing report records 17 counterfactual changes per policy arm, while the passing report records 34 completed challenges per arm without a counterexample in those tests. The page explicitly limits what those tests establish and says the local Python runner does not safely isolate arbitrary programs.
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