Honcho AI Memory Fix Stops Derived Outputs from Inflating Dream Triggers

Open-source AI memory framework Honcho addressed a scheduling logic flaw via pull request #573, where derived memory documents such as deductive and inductive outputs were incorrectly counted alongside raw explicit inputs when evaluating dream-trigger thresholds. This meant a collection with only 30 genuine user observations could still pass a threshold of 50 if derived documents padded the total count to 80 or more. The fix restricts eligibility checks to documents tagged with level equal to 'explicit', ensuring only real new inputs can trigger a dream cycle. Additionally, progress checkpoints are now recorded after a dream run succeeds rather than at the moment a job is enqueued, preventing failed or in-flight tasks from falsely advancing the scheduler state. The patch also separates tracking of new-input eligibility, completed-run progress, and currently pending jobs into distinct mechanisms to avoid feedback loops common in any pipeline where derived data can re-enter as fresh input.