[HN Gopher] AgenticMemory: Zettelkasten inspired agentic memory ...
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AgenticMemory: Zettelkasten inspired agentic memory system
Author : simonpure
Score : 49 points
Date : 2025-03-03 18:09 UTC (4 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| th0ma5 wrote:
| Has a section for results that seems to just define what results
| are. Other than mirroring what other software is doing, how do we
| know any of these concepts are viable in the long term?
| barbazoo wrote:
| > 4.4 Empricial Results
|
| https://arxiv.org/pdf/2502.12110
| theodorewiles wrote:
| yes i have been thinking about this for some time. One other
| thing with zettle I'm not sure was implemented here is you can
| have topic notes that just refer / summarize other notes; it
| would be very interesting if these could be autonomously created
| by some kind of clustering algorithm based on underlying links.
| Kind of like summary-of-summary.
|
| Also curious if there might be some improvements if you dont rely
| on semantic similarity and just do all the pairwise "how related
| are these memories and in what way" LLM test like
| https://www.superagent.sh/blog/reag-reasoning-augmented-gene....
| MrLeap wrote:
| I've been waiting to see some paper that is like "a shallow tree
| of key/values is all you need" to tackle model plasticity.
|
| AI memory seems predominately a tension between compression and
| lookup speed.
|
| These large vectors are keys for lookups, a language of
| expression, and a means of compression. Learning new things is
| always easier when you can map it back to something you already
| know. There's this page about forest fire simulations in a
| scientific computing book I read back in college more than a
| decade ago. I remember it viscerally because I've solved 100
| different problems with that as its seed. I can barely remember
| anything else in the book. I don't remember it because I read it
| over and over, I remember it because it was useful and kept being
| useful.
|
| If some new technique or idea is 90% similar to something I
| already know, I'll learn it easily. If it's 60%, I need to churn
| it around, put in a lot of learning effort. If it's 0%, it's
| noise from this angle.
|
| > 4. Establishes meaningful links based on similarities > 5.
| Enables dynamic memory evolution and updates
|
| Wondering how much compression occurs in #5.
| manmal wrote:
| Fast forward and B+ trees end up making sense (again).
| fudged71 wrote:
| Coming from the TFT space of Roam/Tana/Obsidian... this is
| awesome to see.
|
| Further still, it would be neat to see a hybrid system where
| humans and agents collaborate on building and maintaining a
| knowledgebase.
| fallinditch wrote:
| This is interesting. Does this approach mean that it becomes
| possible to use conversations with an LLM to fine tune the LLM in
| very specific ways?
|
| In other words, if this AgenticMemory can give structure to
| unstructured conversations, and if this makes conversational
| feedback more useful for the model to learn, then can we use it
| to continually refine the model to be better at our particular
| use case?
| manmal wrote:
| That's cosmic. I was wondering about the feasibility of exactly
| this system today while driving to work. Zettelkasten seems
| formalized/rule-based enough to work, and the limited context of
| each Zettel should be ideal for LLMs.
|
| I guess the biggest risk is that two related notes don't end up
| getting connected, so the agent can get stuck in a local optimum.
| I guess once a certain total number of notes has been reached, it
| becomes quasi impossible to make all the connections, because
| there are just too many possibilities?
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