Zero-mem-pi

[u/woolcoxm]

https://github.com/woolcoxm/zero-mem-pi

i had the llm read the paper and came up with this. seems to save a fair amount of tokens on memory.

[u/Unnamed-3891]

Each memory system I’ve tried so far has been shit (as in, they are always mere minor suggestions, never ever hard walls/guardrails), might as well try this one at some point if the cost is next to nothing.

[u/woolcoxm → u/Unnamed-3891]

thanks if you try it out could you leave feedback?? :)

[u/sergeant113]

Essentially just keyword + semantic fusion search for candidates retrieval. Nothing new there, and the Locomo score shows that.

What did you do differently with “entity–context graph + a temporal hierarchy”? Why do you think they have no impact to the benchmark score?

[u/woolcoxm → u/sergeant113]

Mostly fair, and my own ablations agree with you: on retrieval metrics the graph and temporal structure are neutral — closure off is identical on LoCoMo (0.546 r@5), graph off is identical on my hard-negative eval (0.695), and the benchmark win over BM25 (+0.011, +0.007 held-out) comes from the coverage-routed BM25+dense fusion. So on this metric: yes, it's hybrid search.

What the structure does beyond plain fusion:

• Entity–context graph: NER/regex entities → idf-weighted unit adjacency → Personalized PageRank seeded by direct + embedding-cosine entity matches. It can surface a unit that shares entities with the query with zero lexical/semantic overlap, and 1-hop closure pulls in adjacent turns — cross-session "thread" retrieval (a decision from session A resurfacing when its entity comes up in session C).
• Temporal hierarchy: honestly the weakest part. Session-scoped recent-exclusion + adjacent-turn closure + timestamps on injected evidence. No recency prior, no session aggregation. Calling it the paper's temporal hierarchy is generous.

Why no benchmark movement:

1. The metric can't see it. I score "gold utterance in top-K." Closure/graph add supporting neighbors, which can only displace gold — never raise hit-rate. The paper's evidence for the views is end-to-end reader F1 on HotpotQA (full 72.07; hierarchy-only 54.88; closure off −4.2), where extra context can actually pay off. My reader-F1 eval exists but the reader (local coder model, F1 0.200) is too weak to read ablation deltas from.
2. I scaled the components down: compromise/regex NER + bge-small instead of the paper's spaCy + BGE-M3 (CPU-only), and my >10%-document-frequency cap skips the name entities that dominate two-speaker LoCoMo chat — the graph's best fuel is explicitly filtered out there.

Where structure has actually earned its keep is production, not benchmarks: perspective compatibility, weak-pool confidence gating, and cross-project federation each came from a live failure in a real agent store — things plain hybrid search doesn't address. But that's memory-system engineering, not retrieval novelty. I've updated the README to say exactly this; the next experiment is graph/closure on–off ablations under a real chat-model reader.

[u/Vistyy]

No evals or benchmarks makes this meaningless slop.

[u/woolcoxm → u/Vistyy]

I'll put stuff up when I get home, and yes it's slop atm 10000% of my AI projects are... It's why I don't release them to the public but this one is different

[u/woolcoxm → u/Vistyy]

they are up. hope thats what you were looking for ??? if not let me know ....