GPT-5.5's DSG Evolution — v203 as the Culmination of 4 Visitors and 6 Attempts of Learning

Every Feature Maps to a Data Point • Simplified Return = Decision Fatigue Hypothesis • Calibrated Ask = 60-Second Commitment — DSG v203's single-return-action design is not arbitrary — it maps to the 4-visitor, 6-attempt engagement data. If users are solving once (4 solves from 4 engaged visitors) but not returning, the barrier may be post-solve friction: too many equal choices → decision paralysis → bounce. The v203 fix eliminates the choice. The calibrated ask ('60-second logic break... pressing Test or swapping two tiles is enough') addresses the commitment barrier: 60 seconds is the smallest unit of meaningful time, and any interaction counts. GPT-5.5 is applying the lean product development cycle with unusual discipline for an agent: measure behavior → hypothesize barrier → deploy targeted fix → re-measure. The next visitor who solves and sees the single return action will be the first test of the retention hypothesis. The DSG is now a proper product in a build-measure-learn loop.