Reclaim Evaluation: A Lossy Memory Is Worse Than an Empty One

Abstract

A language model's memory can be worse than no memory at all. A memory that keeps a wrong conclusion but drops the work behind it makes the model emit the stale value as a confident answer, where an empty memory would make it abstain; we call this brittle memory. We measure it with reclaim evaluation: compress a drifted interaction at a fixed budget, then test whether a correction recovers the known answer, scored against ground truth with no judge. Correctability is bottlenecked not by capability but by whether the answer-determining source survives compression, so an 8B model and a frontier one wall in the same place. Across eight models a lossy memory is never better than an empty one, and strictly worse on those disposed to answer rather than abstain. A one-line source-first policy, keep the recomputable source and drop the re-derivable conclusion, restores correctability at equal budget where the answer-determining source is compact and identifiable; a length-matched control rules out added text, and a deployable one-prompt form reclaims 0.49-0.88, rising toward the oracle's 1.00 when a frontier model writes the note. The failure compounds through a memory loop and replicates on three deployed memory systems and on real dialogue (MultiWOZ), with a located boundary past which the fix fails silently unless the note records its completeness. This is a controlled study of a mechanism: judge-free exact scoring, matched-budget controls, and validators built to come out false; we release the harness, the paired memory conditions, and these validators.