We measured our memory system on the public LongMemEval-V2 Small benchmark using its own official scorer. Here is the result and everything needed to verify it — published on our terms, whether or not it is ever posted elsewhere.
Self-reported · official scorer · independent verification pendingLongMemEval-V2's headline metric, LAFS, rewards improving the accuracy-vs-latency frontier — being more accurate at a given speed. Our entry improves that frontier at the fast end: best accuracy in the sub-second regime, roughly six points above the same-speed baseline, hundreds of times faster than the high-accuracy agent baselines that take minutes per query.
| System | Accuracy | Latency | Note |
|---|---|---|---|
| RAG (query → slice + notes) | 51.0% | 0.2 s | released baseline |
| UDI v0.3.1 (ours) | 56.76% | 0.187 s | sub-second frontier |
| AgentRunbook-R | 58.6% | 26.9 s | released baseline |
| Codex (coding agent) | 69.9% | 177.2 s | released baseline |
| AgentRunbook-C | 74.9% | 108.3 s | released baseline |
At the same speed as the fastest baseline, we answer ~6 points more correctly. The high-accuracy systems win on raw accuracy but cost 100–950× the latency.
The frozen result package is content-hashed. When the package is reviewed, these digests let anyone confirm the bytes are exactly what produced the number above — nothing swapped, nothing tuned after the fact.
The full method, matched baselines, and negative results live in the Lab. If the number is wrong, the fastest way to show it is to reproduce the scorer on the public benchmark — and we will publish what you find, positive or negative.