Backboard is #1 on LoCoMo and LongMemEval, the two academic benchmarks for long-term AI memory without changing the original guidelines. Other companies have gamed by using newer models with bigger context windows. This post explains why the result matters anyway, what it actually measures, and how to use the memory that earned it.
What these benchmarks test
These are not "find a fact in a wall of text" tests. They measure whether a system can build, maintain, and reason over memory across many conversations.
LoCoMo (Long-term Conversational Memory) evaluates very long-term memory over multi-session dialogues that span weeks. It tests single-session recall, cross-session reasoning, temporal reasoning, outside knowledge, and adversarial questions.
LongMemEval scores five distinct abilities: information extraction, multi-session reasoning, temporal reasoning, knowledge updates (noticing when a fact about the user changes), and abstention (knowing when it does not know). Its own paper reports that commercial assistants and long-context models lose around 30% accuracy on sustained memory.
