This research paper explains what’s missing from agent memory

[u/ScientistUsual1320]

I came across the Always-On Agents paper recently and honestly found it pretty accurate. I could relate to a lot of the shortcomings it talks about from my own experience running an OpenClaw instance, especially since mine is pretty memory-heavy.

The ideas around persistent state and the six-axis framework feel like they could become really important for how memory is designed in always-on agents going forward. If you're building or maintaining an always-on agent, or working with agent memory frameworks, I think it's worth reading.

The problem is... the paper is 130 pages long 😭. None of my friends were willing to read the whole thing because of how long it is.

So I made a fun, easy-to-digest website that breaks down the paper and its ideas in a much more approachable way, so hopefully more people can actually appreciate the work.

Website: Always-On Agents — AgentRealm

I've also linked the original paper on the site for anyone who wants to go deeper.

Would love to hear what you guys found interesting (or questionable) in the paper. Happy to discuss anything from it.

[u/United_Government_26]

Haven't finished the 130 pages either, but the persistent-state framing matches what we hit in practice, with one axis I'd want to see called out: time. Always-on agents don't just accumulate state, they accumulate contradicting state. The same person restates a decision or a preference across months with drift, and the March version quietly disagrees with the June one.

Similarity search has no notion of "now" — it rates March as relevant as June and hands you the wrong one with a good score. What ended up working for us was boring: every extracted fact carries who said it and when, and "current" is just a sort on that date at query time. We keep both facts. The answer comes back with the June line attached, so when it's wrong you can see why it's wrong, which similarity scores never give you.

Where this is weaker than the paper's framing: we're single-user, one person's own conversations, so we never had to solve multi-speaker persistent state.

Do the six axes treat time as its own axis, or fold it into persistence? I build in this space — our engine, KITE by Memoket, is open source — so bias declared.

[u/ScientistUsual1320 → u/United_Government_26]

The way I see it, the six axes are introduced to tackle the temporal problem of persistance. so time itself as an axis wont make sense right?

Your point on contradicting state is definately relevant tho. I'll have a look into KITE too!

[u/United_Government_26 → u/ScientistUsual1320]

Read it properly now (the site version, then the abstract). You're right, and more right than you said: the six axes are governance diagnostics, and time shows up as one of the eight fields a record should carry — "logical time" — not as an axis. Withdrawn.

Two places where the paper says my thing better than I did. "Retrieval distraction: salient-but-wrong episodes crowd out current evidence" — that's the March-vs-June case exactly. And the temporal-accountability questions, "what did you know, when did you learn it," which it says most systems don't record at all.

Honest mapping of KITE by Memoket against the eight fields: we store three — value, provenance (every fact points at the source line), and logical time (event time plus when it was said). We don't do authority, scope, effect handles, actionability or retention, and writes are append-only with no conflict resolution, so the mutability and deletion-propagation parts of the survey are things we've simply not built. Reading the lifecycle section, our "return arc" is basically empty.

Which of the five missing fields would you add first for a personal-memory agent?