AtMem reports that a fine-tuned memory-write classifier improved its offline score while failing the practical job of retaining useful information. Its production selection process blocked deployment.
V2 reached 73.79% fully correct transitions on a sealed evaluation, but an always-decline-storage baseline scored 91.10%. In a matched, 100-item sequential replay, learned variants failed required STORE, UPDATE and DUPLICATE actions. A later selective research model chose not to store on all 100 replay cases.
Evidence & limits. A maintainer’s negative engineering report, not a peer-reviewed or independently reproduced result. The replay was fictional and inspected during development; labels were machine-generated, with one model family and seed. The rules baseline also performed poorly and over-stored. The report is dated October 17; the checkpoints are older and the publication timezone was unavailable.
Measure per-action recall and compare against always-reject baselines. Then replay decisions against the system’s own evolving store before opening the deployment gate. This result does not prove learned memory is generally inferior.