r/AIMemory

10 Biologically inspired memory experiments with visual examples

10 Biologically inspired memory experiments with visual examples

I ran a series of 10 experiments testing this a while back and finally got around to putting together a full write-up with charts, data tables, and visualisations over at d/rksci (https://drksci.com/research-engram#2-ten-mechanisms) [click individual experiment headings in the grid for the visual demo and specifics].

Here is a breakdown of all 10 mechanisms and how each works:

  • Tide: Ranks newer facts above earlier ones using recency-weighted dense re-ranking to resolve conflicting claims.
  • Pheromone: Leaves decaying trails along useful memory paths so subsequent reads follow proven routes.
  • Ring: Evaluates continuous retention and buffer pruning criteria across multi-turn survival laps.
  • Web: Traverses entity relationship graphs to pull connected knowledge across multi-hop reasoning tasks that dense embeddings miss.
  • Bloodhound: Trains a tiny kernel to navigate memory paths by working backward from verified answers.
  • Horizon: Maps memory into a hyperbolic Poincaré disk where broad concepts sit at the center and niche details live at the edges.
  • Loom: Resolves memory conflicts using mutual-kNN agreement, where facts with more agreeing neighbors win out.
  • Coral: Models memory growth like a reef using calcification, heir nodes, and an immune response to prune bad data.
  • Canopy: Grows an offline topic tree seven branches wide for the reader model to navigate top-down.
  • Glyph: Encodes memory in a self-explaining notation so a completely different model can read it cold without prior tuning.

The full benchmark comparisons against standard BM25 and dense RAG baselines are up on the page.

u/BuckChancey — 7 days ago

We need to stop pretending memory harnesses aren't already recursive self-improvement.

Look, everyone loves to draw this clean, academic line in the sand: “State persistence isn’t structural recursion! A memory harness is just state t+1 reading state t, it’s not rewriting its own weights!”

Who are we kidding?

It’s why they say, what they said.

When you hook a persistent memory harness up to an autonomous AI research loop—logging what compiled, what threw a segfault, why the last five architectural tweaks failed, and feeding all of that back into the next generation of code generation—the line completely evaporates.
You don't need a magical sci-fi substrate shift for RSI to start happening. It’s happening right now through compounding workspace feedback. The system uses its own historical output as a synthetic curriculum, iterating past human bottlenecks one logged failure and successful diff at a time.
If the memory layer is where the autonomous research agent stores its evolutionary history and benchmarks to reboot its next iteration, then the memory harness is the engine of the loop.

Change my mind.

reddit.com
u/OmegaEpidex — 14 days ago

People building agents, how much of a problem is long term memory actually?

I've been working on long term memory for agents and have a rough PoC together. Trying to get a better sense of where people are actually struggling with this, and whether there's a business here beyond another way to save and retrieve conversation.

In my PoC so far with some limitations and edge cases I'm working out my write speeds have been around 1 second or less. And I don't just mean inserting text into a database. I mean processing new information and figuring out how it changes what's already in memory.

I'm mostly interested in agents that keep learning from conversations, documents, tools etc. over weeks or months. Eventually some of that information is going to disagree, become outdated, or turn out to have been wrong.

What are people doing when two sources contradict each other? Keeping the latest thing doesn't always make sense. Sometimes something genuinely changed, sometimes one source is wrong, and sometimes you just don't have enough information to decide. Are you handling that explicitly or leaving it to the model when the information gets retrieved?

Then there's how much the agent should trust what it's learned. Something it inferred isn't the same as something it was directly told, and different sources aren't necessarily equally reliable. A few independent sources backing something up should count for more than five summaries repeating the same original claim. I'd want the agent to become more or less confident as evidence comes in, rather than everything being either a saved fact or deleted.

Same with information going stale. A price from six months ago and someone's date of birth shouldn't age the same way. Some things need to be checked again if they haven't been confirmed in a while. Other things should stick around. Forgetting something and deciding it's no longer reliable aren't really the same thing either.

And when the agent gets something wrong, can you trace where it came from? Which source it trusted, what it inferred, why it changed its mind? Or are you digging through old conversations trying to reconstruct it yourself?

That's roughly what I'm working on. For anyone using Mem0 or other memory systems, how much of this is already handled well, and what have you still had to build around them? Also interested in people who just retrieve the original material and find that's enough

Does write latency actually matter in your application? Do you need new information available before the agent's next action, or can memory updates happen in the background without causing problems?

If you've built your own, how much time has gone into it, including maintaining it? What made you build rather than use something existing?

I'm trying to understand whether a dedicated product could take enough of that work off your hands to be worth paying for, or whether the important parts are too specific to your application.

Would be useful to hear what you're building and what actually broke. “We spent three weeks fixing this” tells me a lot more than “agents need better memory.”

reddit.com
u/EntropyPilot — 14 days ago