What Social Memory Forgets: Measuring What Decision Accuracy Cannot Detect

Large language models increasingly operate as communities of interacting agents that divide responsibilities, delegate tasks, and establish leadership roles. As these communities exchange findings and build on one another's work, understanding how they form and maintain collective memory becomes essential to understanding their collective behavior. We introduce ORALMEM, a framework for separating memory fidelity, the decision relevance of memory errors, and agents' use of remembered evidence. ORALMEM tracks source relationships through compression and evaluates whether an observed distortion changes the optimal decision, making it possible to distinguish errors that matter for a task from those that its outcome cannot expose. In our controlled competing-incident task, correct decisions coexist with distorted source relationships. The three observed contradictions leave the optimal allocation unchanged, so correct actions do not establish that coordinators detected or compensated for the loss. A prospective allocation cohort reveals a complementary failure. Most memories preserve source relationships under outcome-blind human annotation, yet one coordinator makes correct allocations in only 34 of 60 cases with untouched memory. Explicit source-attribution notes raise this to 55 of 60, including benefits when the relation was already preserved in memory. A matched reminder can also change decisions without adding source evidence. Together, these findings expose a blind spot in outcome-only evaluation. It can reward successful coordination while leaving the integrity of collective memory and its use untested. ORALMEM provides a structured way to determine what decision success actually tells us about what a team remembers.