> δ-mem compresses past information into a fixed-size state matrix updated by delta-rule learning
This doesn’t solve the capacity problem of memory. You can cram more into one context window, but then again you need to associate them with input queries. That’s very hard because slight variations in input create hugely different activations. So really, it doesn’t improve caching.
This paper might do a thing or two approximating the compression limit for context windows, but there’s a fundamental limit on how much information can go into it.
What you really need is contextual search, as in, different events and objects with the same abstractions and semantic lead to same response, so you can cache effectively… on this front the paper does little to improve “memory” in a meaningful way
The obvious energy saving step would be to utilise previous searches by others. Many of the tasks people do are rather similar, it is such an energy waste to start again each time.
(Obviously ignoring the huge energy saver, which is to observe if you even need to bother doing the task at all.)
I had this thought and created https://pushrealm.com which is essentially a sort of Stackoverflow written by agents.
My theory was that if an agent burns 30 minutes resolving an issue not present in training data, posting the solution would prevent other agents re-treading the same thinking steps.
I see why, but I don't feel this is the solution. Being able to search thru the endless LLM responses is not viable. However having useful memories, similar to human brain is more important. I sense this is why neuromorphic computing is the next step, energy efficient and doesn't remember much of what isn't useful to be stored.
A lot of what I see people using LLMs for would be more cheaply and reliably done by [scripts]. A search engine style suggestion thing like "Have you tried `sed`?" would be beneficial imo
I see lots of techniques proposed to give LLM the capacity to recall things, I even saw a lot of memory plugins for AI coding agents, I tried some myself.
What I want to see is something that was tested and proved in practice to be genuinely useful, especially for coding agents.
You would think git history should be the first thing an agent would look at, as they make so many mistakes before they get to the correct answer. They don't.
I haven't measured, but documenting bug fixes and architecture seems to help, along with TDD patterns, including integration tests.
I would probably add it to Claude.md to look for all of the above when tackling a new bug.
> δ-mem compresses past information into a fixed-size state matrix updated by delta-rule learning
This doesn’t solve the capacity problem of memory. You can cram more into one context window, but then again you need to associate them with input queries. That’s very hard because slight variations in input create hugely different activations. So really, it doesn’t improve caching. This paper might do a thing or two approximating the compression limit for context windows, but there’s a fundamental limit on how much information can go into it. What you really need is contextual search, as in, different events and objects with the same abstractions and semantic lead to same response, so you can cache effectively… on this front the paper does little to improve “memory” in a meaningful way
Interesting points:
- fixed size of the memory seems like a good idea to overcome the current limitations
- skimming through the thing, I can't find any mention of the cost?
- I would need more time to read it in-depth to see if this is legitimate and not just fancy form of overfitting or training on testing data
Interesting that the headline is showing Δ-Mem while the paper uses δ-mem
Is it a lowercase to uppercase conversion going on here?
Correct!
The obvious energy saving step would be to utilise previous searches by others. Many of the tasks people do are rather similar, it is such an energy waste to start again each time.
(Obviously ignoring the huge energy saver, which is to observe if you even need to bother doing the task at all.)
I had this thought and created https://pushrealm.com which is essentially a sort of Stackoverflow written by agents.
My theory was that if an agent burns 30 minutes resolving an issue not present in training data, posting the solution would prevent other agents re-treading the same thinking steps.
I see why, but I don't feel this is the solution. Being able to search thru the endless LLM responses is not viable. However having useful memories, similar to human brain is more important. I sense this is why neuromorphic computing is the next step, energy efficient and doesn't remember much of what isn't useful to be stored.
So you mean caching? :-)
A lot of what I see people using LLMs for would be more cheaply and reliably done by [scripts]. A search engine style suggestion thing like "Have you tried `sed`?" would be beneficial imo
I see lots of techniques proposed to give LLM the capacity to recall things, I even saw a lot of memory plugins for AI coding agents, I tried some myself.
What I want to see is something that was tested and proved in practice to be genuinely useful, especially for coding agents.
How would you conceptualize recall in this case? Is searching through the current version of your code and possibly git history not enough?
You would think git history should be the first thing an agent would look at, as they make so many mistakes before they get to the correct answer. They don't.
I haven't measured, but documenting bug fixes and architecture seems to help, along with TDD patterns, including integration tests.
I would probably add it to Claude.md to look for all of the above when tackling a new bug.