Comment by altmanaltman

5 days ago

I am sorry but the whole "biological memory" thing seems like marketing fluff on basic cache mechanisms.

You said it cuts token usage by 84% but isn't that typical for any typical chunked RAG system?

And why did you specifically chose to test against the LoMoCo dataset when there's a lot of issues with it and it being very easy to cheat?

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altmanaltman

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SachitRafa  2 days ago

Hi Builder here, biological is here doing the rhetorical work. The actual mechanism here is exponential decay with category specific half lives, recall based reinforcement and pruning context which falls below threshold.

The main difference between a cache and this framework is that it prunes data not only based on recency but also based on importance and category failures fades fast, strategies persists longer, facts stays longer and assumptions fades faster so on.

The 84% is against storing everything forever. The parameter where it beats RAG is handling contradictions and maintaining the memory size near constant with active pruning of data.

Have also benchmarked it against LongMemEval-S dataset the results are in the repo

xhevahir  5 days ago

And a neural network is really just a composed, non-linear parameterized function that maps input vectors to output vectors. Sometimes metaphors or analogies do contribute something valuable.

mtrifonov  4 days ago

Decay-as-eviction is just LRU, fair. Type-conditional half-life is worth defending, though.

A user's job and personality should be effectively permanent. Their stated intent for this week should fade in days. Their emotional state from a single message should be gone by tomorrow. Decay everything at one rate and you're back to LRU with the problems you're calling out.

The "biological" framing isn't really doing much work. Ebbinghaus is one curve and fine, but it's not where the leverage is. Type-conditional half-life is. Without that, this is a cache.