BrainCore keeps long-term memory outside the prompt — in a spiking substrate that stores what was said, when it was said, and who said it. Seventeen months of conversation, four separate threads, and an answer it was never told.
Every "assistant that remembers" does one of two things: stuff the transcript back into the prompt, or chunk it, embed it, and return top-K by cosine. Both re-read the past instead of holding it. Which is why the same three failures repeat no matter how large the window gets.
Two layers with a hard boundary between them. The substrate stores and retrieves — it decides which three to eight utterances are relevant to your question. The language model reads that working set and phrases the answer. It never sees the rest of memory, so it has nothing to invent from.
Verbatim from a run over four conversations spanning May 2023 to October 2024. Nothing rewritten, nothing selected beyond what the run produced.
Q: When did Vitaly pass the anatomy exam?
A: 7 May 2023 (from "yesterday" relative to episode date 8 May 2023)
Resolved by arithmetic, not by a model. The date of the conversation is the anchor that lets "yesterday" still mean something a year later.
Q: Which design pattern did Vasya use in the transport module?
A: Factory
Six weeks separate the two conversations. Vasya never said "Factory"; Sergey never saw Vasya's code. The link is built when the question is asked, not when the facts were learned.
Q: What most likely happened to old Matryona?
A: Most likely she fell into the well, whose edge had collapsed.
No utterance says this. It is the best explanation of the observations — an inference, not a lookup.
Q: What colour is Oleg's car?
A: There is no data about this in memory.
Asked in the thread where Oleg appears only as a brother who moved away. His car was discussed — in a different conversation. The harder half of honesty is refusing about someone you actually know.
Sixty questions across ten abilities, over four isolated conversations and thirteen dated meetings. Scoring is deterministic — no language model judges the answers. The full report, including everything that did not work, is one click away.
What these numbers are not. Sixty questions is a small set, and it was written by the author of the system. This is a functional test — does it work — not an independent evaluation, and it is no substitute for a blind benchmark. The material is Russian-language and synthetic.
Retrieval quality holds as memory grows — 93% at 65 utterances, 85% at 16,000 — but latency does not: 21 ms becomes 2.3 s over the same range. Past roughly four thousand utterances this stops being usable in a conversation. That ceiling is ours to fix, and we are not going to hide it behind an average.
On the public LoCoMo benchmark the same engine scores 50%, and nine added neuro-mechanisms moved that number by exactly zero. An earlier paper of ours claiming 88.7% has been retracted: its metric counted an honest refusal as a correct answer. All of it is in the report — we would rather you read the failures than discover them.
The engine runs on your machine. Absorbing a conversation and recalling from it make no network calls at all — a language model is optional and only phrases the final sentence. Commercial tiers are not open yet, and we are not going to pretend otherwise.
Research build. Interfaces will change. Nothing here is a production guarantee.
I'm an engineer with 15 years of shipping production code. The last stretch I've spent on one question: why does every assistant forget, and why does none of them admit it.
The answer I kept arriving at is that memory was never built — it was approximated. A bigger window, a vector index, a summariser. All of them re-read the past; none of them hold it. And none can tell you that they don't have something, because "nearest match" always returns a match.
So here the memory lives outside the prompt, in a substrate that keeps what was said and when. The language model is on a short leash: it reads a handful of retrieved utterances and phrases the answer. It never gets to fill a gap, because it never sees one.
I'll also tell you what didn't work, on the same page as what did. Nine neuro-mechanisms that moved a public benchmark by exactly nothing. A paper of my own, retracted for measuring the wrong thing. Five of nine failures in my own test set that turned out to be my questions being wrong rather than the system. A memory that lies to you is worthless — and so is a benchmark that lies to me.
If you're building assistants that have to hold a conversation across months — support, therapy, tutoring, long-running agents — the failure modes on this page are probably familiar. I'd like to hear which of them hurts most in your domain.
Research build. Source access for evaluation partners on request.