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Memory

Every test the AI has run, kept, rejected or rolled back, and the lesson each one left for the next.

sample data · engine not live yet

Nothing is deleted, and each entry hands a rule to the next experiment.

Every test

4 of 4 entries
  1. Entry #0312rejected

    Adaptive fee candidate

    Hypothesis
    Dynamic fees could improve LP returns without hurting organic volume.
    Method
    14-day replay + shadow on 1 in 10 swaps.
    Result
    Candidate rejected: organic volume −19.4% above a 1.8× volatility multiplier.
    Lesson for next time

    Red team before canary; cap multiplier at 1.8× in similar regimes.

    • sim #8421
    • red-team R-0091
    5.21.6
    Fig. 1candidatebaselinesim #8421
  2. Entry #0311rolled back

    Canary configuration

    Hypothesis
    A 1.3× multiplier holds organic flow during spikes.
    Method
    Live test on 1 in 6 blocks for 4 h.
    Result
    Configuration rolled back after slippage exceeded 0.5% at median trade.
    Lesson for next time

    Retain the rollback record; slippage guard precedes fee changes.

    • rollback #1
    • block 26,128,441
  3. Entry #0310accepted

    Simulation gate

    Hypothesis
    Requiring a passing simulation before deployment reduces rollbacks.
    Method
    Policy change in the ModuleRegistry level allowlist.
    Result
    Accepted: no un-simulated proposal reached shadow since.
    Lesson for next time

    Simulate before canary.

    • proposal #310
  4. Entry #0308rejected

    Treasury-heavy split

    Hypothesis
    Routing 40% of fees to treasury funds more bounties.
    Method
    Split simulation vs holder outcomes.
    Result
    Rejected: weakened auto-liquidity during thin depth.
    Lesson for next time

    Keep the treasury share at 25% while depth < target.

    • sim #7990

How lessons add up

entry · lesson · next experiment

Rule graph: each of the 4 memory entries leaves a future rule, and every rule feeds the next experiment: Awaiting the next hypothesis.

thread T-0001