Can Proactive Recall Replace Query-Based retrieval in persistent AI memory?

Granis87
Mon, 23 Feb 2026 23:07:15 +0000

**I’ve been working on MnemoCore, an open-source infrastructure for persistent AI
cognition. The goal is not just session-level memory but a long-term cognitive
substrate that survives across interactions and agents.

A few of the core design choices so far:

3-tier Hot/Warm/Cold storage with temporal decay modelling

HDV/VSA encoding via a companion module called HAIM
(16,384-dimensional binary vectors with XOR-binding)

Per-trace source provenance tracking

Most existing approaches (RAG, MemGPT, and similar) are reactive. Memory is
only retrieved when the system receives an explicit query. That works fine for
lookup tasks, but it misses something important: humans don’t wait to be asked
before a relevant memory surfaces. Association happens continuously, in the
background, triggered by context.

So my research question is this:

Can a persistent AI memory system proactively surface relevant memories, without
an explicit retrieval query, by monitoring ongoing inference in real time?

Some directions I’m exploring for the triggering mechanism:

Attention pattern shifts between inference steps

Cosine drift thresholds in the embedding space

Topic boundary detection as a signal for memory injection

I’d love to hear thoughts from anyone who has worked on similar problems. Is
proactive recall architecturally feasible at inference time without prohibitive
overhead? Are there papers or projects I should be looking at that tackle this
from a different angle?
**
GitHub - RobinALG87/MnemoCore-Persistent-Cognitive-Ai-Memory: MnemoCore v2.0.0-beta Latest**

Thanks in advance.**

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