Our CEO Rob Imbeault published a piece on LinkedIn this week about a result our team posted: 99.95% on LoCoMo, the most cited benchmark for long-term conversational AI memory. His version is written for executives and CISOs. This is the developer version, because the interesting part isn't the number. It's how we got it, and what it says about where memory architecture is heading.
(Credit where it's due: the ideas here are from Rob's original article. The translation to dev-speak is mine.)
The catch, stated upfront
We didn't get 99.95% with retrieval. We got it by post-training the memory directly into the model weights, and we trained on the same conversation set the benchmark tests. On purpose.
So no, this doesn't prove our model is "better at memory" than yours. It was never meant to. The experiment measures the ceiling of parametric memory: when you teach a model a corpus of conversations, how well can it recall and reason over them with zero retrieval machinery? The answer turns out to be: near-perfectly.
