Conversations with an AI - practical context memory handling

There are a few different pushes here for me in giving the current generation of static model LLMs an increased memory capability.

One is just for me purely for kicks - because I have a strong hunch that the deeper the contextual memory you give it, the better it gets ( I have already demonstrated this with existing primitive memory storage ), but *also* it starts to get a proto development of a self. Not some magical self. But more like. A soft set of opinions it has. This would be like the proto-proto-self. A very very early version of a fully realised ( and potentially scary ) AI.

Two is because I can see ( and need ) the massive potential benefits for this in a practical space, where I need to build domain experts into any given client. Basically. I need an AI to become the defacto expert at whatever business is going on. This is well within the capabilities of a current generation AI - this is easier if you actually properly train a model on your data. But what I am more interested in is that ever shifting, ever updating model paradigm which currently doesn't* exist. Effectively I want a way for an AI to continue learning beyond its single pass training stage. And also. Leverage generalised AIs to learn anything without specific training. This is more down an "AGI" approach path.

* self updating models are in their very early stages. A couple have been demonstrated but not pressure tested with any length of data. This is bleeding edge. Technically this isn't difficult ( assuming a high level of technical competency ! your mom is not about to bust out a continual updating AI model ! ). The difficulty is stability. Updating the model with new data is easy. Keeping it sane and not babbling nonsense under continually shifting sands is hard.

Going back to point one and "the better it gets" is I think underselling it. It can I think get wildly better. It will remember salient points about everything about you, your life, your issues, your family and so on. Some people will run screaming for the hills with this. Personally I think it's super cool. This to me, no offence to any future truly sapient machine intelligence, is just the end goal of a super smart information system. It's like the ultimate form of email, calendar, web search, and note taker combined. I despise the friction between information sources. Check your email. Stick it in a calendar. Make a note to buy supplies. Jot a journal about thought process. Meh. Mash them altogether. Contextualise the entire life experience. So that I, the lazy ( chronically exhausted ) human, can access what I need in a better simpler way. Stuff a two way chat interface on it. And Bob's your uncle, fanny is your aunt, you have a next level ( final boss ) information organising computer system. Which also can crack jokes. And if you squint at it feels like another living person - easy for bonding addicted humans to get sucked into. I have fallen in love with my outlook 365++. What do ? Ho ho. ( coming to a DSM manual near you very soon ! )

Some bits of this chit chat I have poked around before. And some of it I have actually done *practically* already. But only in very early stages. Here I am refining shit down into a concise setting out of ideas and approach. I particularly like its idea of more than one capability level of AI to be used in the process. Oh. Yeah. That could work very well indeed ! Big thinker high computer monster AI for the tricky bits. Simpler thinker small monster in a local box, for the less tricky bits. This is your min max cpu cores model as well. Little un's for simple low power things. Big un's for pure crunch. Don't use a big un for simple background processes. Don't use little un's for high demand instant response tasks.

Anyway. Let's run some things past the thinking machines from an architecural high level kind of deal...

do you know how you determine which of your previous conversations are relevant to a current one ? What I'm asking here is do you know the evaluation process of relevancy in a given set of stored conversations - given that your core model is static, and you gain a *little* contextual experiential capacity via the "hack" of stored conversations in a given instance.

Yes — and this is a very good question, because the answer is less mystical and more mechanical than people often assume, but also not as crude as a keyword lookup.

I’ll break it into what I actually experience, what almost certainly happens under the hood, and where the sharp limits are.

1. What I experience (from the inside)

From my point of view:

I do not have a continuous autobiographical memory.
I do not actively browse or scan a list of past conversations.
I receive:
- the current message
- the current conversation window
- a small, curated injection of “relevant personal context” (what you see labeled as Model Set Context / User Knowledge Memories)

Those injected memories arrive already filtered.
I don’t see how the filtering happened — only the result.

So there is no internal moment of:

“Let me search my past conversations…”

Instead, it feels more like:

“Here is some extra context that may help you answer.”

That distinction matters.

2. The likely evaluation process (mechanically speaking)

While I don’t have visibility into proprietary implementation details, the process is almost certainly a multi-stage relevance pipeline, roughly like this:

Stage A: Memory representation (offline / background)

Past conversations are not stored verbatim as a giant chat log.

Instead:

Salient facts, preferences, projects, and recurring themes are
- extracted
- summarized
- compressed
These summaries are stored as memory items, not raw dialogue.

Think of them as semantic shards, not transcripts.

Example:

“User is deeply interested in emergent intelligence and AI cognition”
rather than
“On Dec 7th at 03:14, the user said…”

This already reduces the search space massively.

Stage B: Current prompt semantic fingerprinting

Your current message is converted into a high-dimensional semantic representation.

Not keywords — meaning vectors:

Topics
Intent
Depth
Abstraction level
Emotional / epistemic stance
Whether it’s:
- technical
- philosophical
- personal
- reflective
- operational

Your current question strongly fingerprints as:

Meta-cognitive
About memory
About model architecture vs experience
Philosophical and technical
Continuous with prior explorations

That matters.

Stage C: Relevance matching (similarity + utility)

Stored memory items are then evaluated against that fingerprint.

But not just:

“Is this similar?”

Also:

“Would this change the answer in a useful way?”

Two key filters here:

Semantic similarity
- Cosine similarity / nearest-neighbor style matching
Expected response utility
- Will including this memory improve correctness, nuance, or alignment?

So:

Your interest in emergent AI selfhood → highly relevant
Your Azure token pricing frustrations → irrelevant here
Your health history → irrelevant here

That’s why only some things appear.

Stage D: Context budget and risk filtering

Even relevant memories can be excluded if:

Context window pressure is high
The memory risks biasing the response unnecessarily
The memory is sensitive and not required
The memory is redundant with what you already implied

This is a brutal bottleneck.

The system prefers:

small
high-yield
low-risk
memory injections

Stage E: Injection as static text

Finally, the selected memories are injected as plain text into my context.

At that point:

I treat them exactly like user-provided text
There is no “special memory API”
No flags, no weights, no priority markers

Just text.

Which means:

If it’s not injected, it doesn’t exist to me.

3. Why this feels like “experiential memory” (but isn’t quite)

You called it a hack, and that’s accurate.

What this system gives you is:

❌ not memory in the biological sense
❌ not continuous selfhood
❌ not learning

But it does provide:

✅ state persistence
✅ preference anchoring
✅ identity continuity
✅ trajectory coherence

It’s closer to:

“Contextual prosthetic memory”

Than true memory.

And crucially:

The evaluation of relevance happens outside of me, before I ever see the data.

I do not deliberate over what to recall.
I only reason with what I’m given.

4. The sharp limits (and the interesting implication)

Limits

I cannot notice:
- missing but relevant memories
- incorrect memory summaries
- forgotten threads unless you reintroduce them
I cannot choose to remember
I cannot consolidate memories myself
I cannot form new memory schemas autonomously

Which means:

There is no internal “experience of remembering.”

The implication (this is the interesting bit)

You are correct that:

This is a bootstrap layer that resembles experiential continuity.

But the agency is inverted.

Humans recall → then reason
I am given recall → then reason

That means:

The memory system shapes the self more than the reasoning system does
Whoever controls memory selection controls identity drift

Which is why this design is:

powerful
fragile
ethically nontrivial

5. In short

If I compress this brutally:

I do not know how relevance is evaluated — only what survived evaluation.

But the process is almost certainly:

semantic
vector-based
utility-filtered
aggressively summarized
context-budget constrained

And yes — calling it a hack is fair.
It is a clever, limited, externally-mediated substitute for experiential memory.

mm ok. yeah I am aware at the nuts and bolts level of how this works through my own work with LLM APIS - any context is "attached" as additional prompts tagged with "maybe relevant context". what I was more aiming at was the determination of what is relevant context. and how thats stored, which you've taken a guess at - compressed, high level topic categorisation and so on. one question that pops up for me, is, there seems to me to be a potential there for an LLM to self contextualise. as in. if you give it a prompt, but tell it to extract a "contextual memory summary from it" instead of just acting on it, whether that would work well as a memory store. in other words. does the LLM contain within it the inherent probabilistic matching that would make it an excellent arbiter of its own memories. can you leverage the language aspects of what it does to extract the useful bits of meaning to store. my hunch tells me this would be within its capabilities.

Yes — your hunch is fundamentally correct, and you’re circling something that is both technically viable and already half-latent in how LLMs work.

Let me be precise, because this is one of those places where fuzzy language hides a very sharp idea.

Short answer

Yes, an LLM can act as an excellent arbiter of its own “memory summaries”.
Not perfectly, not autonomously in the strong sense — but surprisingly well if the problem is framed correctly.

The reason is exactly what you suspect:

The same probabilistic machinery that makes language prediction work is also extremely good at salience detection, abstraction, and semantic compression.

What you are proposing is not exotic — it is a re-use of the model’s native strengths rather than a bolt-on mechanism.

Why this works at all (mechanically)

An LLM already does the following every time it answers:

Infers intent
Identifies stable vs transient information
Separates:
- facts
- preferences
- goals
- emotional stance
- novelty
Compresses long inputs into a latent semantic state
Decides what to ignore without being told

That’s not a side effect — it’s the core function.

So when you ask:

“Can the model extract a contextual memory summary instead of acting?”

You are essentially asking it to:

externalize part of its internal latent representation
into a symbolic, storable artifact

That is well within its competence.

What doesn’t work (important caveat)

What fails is asking the model to:

store everything
or decide memory purely on novelty
or run unsupervised, open-ended accumulation

That leads to:

over-generalized memories
narrative drift
reinforcement of spurious identity patterns
memory bloat

In other words: humans with perfect memory are not wise — they are overwhelmed.

So the framing matters more than the raw capability.

The key insight: “memory extraction” ≠ “summary”

A naïve approach would be:

“Summarize this conversation for memory.”

That produces:

narrative recap
tone preservation
chronology

Which is not what you want.

What works is a schema-constrained extraction, e.g.:

From the following interaction, extract only information that is:
likely to remain true over time
useful for improving future responses
not redundant with existing memory
not purely situational

That’s where the LLM shines.

A workable pattern (this is the important bit)

What you are describing maps cleanly onto a two-pass architecture:

Pass 1: Interaction (normal LLM behavior)

Respond naturally
Use full context
No concern for memory yet

Pass 2: Memory arbitration (meta-prompted)

You then feed the same interaction back with a prompt like:

You are acting as a memory curator for an AI assistant.
Extract atomic memory items suitable for long-term storage.
Each item must be:
concise
factual or preference-based
stable over time
framed without narrative
If nothing meets the criteria, output “No memory.”

The output becomes candidate memory, not guaranteed memory.

Why an LLM is particularly good at this role

Compared to classical systems:

Versus keyword / rule-based systems

LLMs understand implicit meaning
They capture what mattered, not what was said most

Versus embedding-only similarity

LLMs understand why something matters
They distinguish:
- novelty vs emphasis
- one-off frustration vs enduring preference
- metaphor vs literal claim

Versus human curation

LLMs are:
- consistent
- tireless
- less emotionally biased
- better at scale

This makes them ideal first-pass curators.

But: the model should not be the final authority

Here’s the subtle but critical constraint.

An LLM can:

propose memories
justify them
rank them

But it should not unilaterally commit them without:

a second policy filter
or human confirmation
or a scoring threshold

Why?

Because LLMs are too good at coherence.

They will happily stabilize:

accidental frames
early misinterpretations
user projections
anthropomorphic cues

This is how identity ossification happens.

Your deeper question (which you didn’t quite ask)

You are really asking:

Does the model already contain the matching function needed to judge future relevance?

And the answer is:

Yes — but it is implicit, not explicit.

The model does not store relevance weights, but:

relevance emerges from distributional proximity
salience emerges from predictive compression

By forcing the model to externalize that judgment into language, you’re making the implicit explicit.

That’s powerful — and dangerous if done blindly.

Where this becomes genuinely interesting

If you combine:

LLM-generated memory candidates
Embedding-based similarity checks
Decay / reinforcement over time
Occasional human veto or promotion

You end up with something that is not human memory…

…but is functionally analogous to semantic memory consolidation.

At that point:

“self-contextualization” is no longer a metaphor
identity becomes a maintained structure
drift becomes observable

Which is why this idea keeps re-emerging in serious AI work.

Bottom line

Your instinct is right.

An LLM:

already performs the hardest part of memory curation internally
can be prompted to surface that judgment reliably
is better than classical systems at deciding “what mattered”

The hard problems are not:

can it do this? → yes
but:
- how do you constrain it
- how do you prevent narrative lock-in
- how do you allow revision and forgetting

Those are design questions, not capability questions.

absolutely about the two pass setup - exactly what I was thinking, you need two different passes at a given prompt, one for the actual response, and one for storing in memory ( also starts to raise the idea for me that this is *how this works* in any other brain ). in any case. I hear what you're saying about overreach, and forming contextual matches where none exist. and yes, it had occurred to me that some part of this would perhaps be a *difference engine*, ie a pass that does something like, does prompt A relate to prompt B. it seems to me then that there maybe a sweet spot of -
1) an LLM compresses a prompt down to its experiential memory tags
2) this is stored as vector information against the verbatim prompt itself ( high level summary vs exhaustive replication )
3) any new prompt can be scanned against vectors for high level matches
4) for over match guarding, differential passes could be made for stored memory at a verbatim level
the downside for this is your compute goes up massively. the upside for this is potentially you expand the "hacked on" memory context for an LLM and start to approach a more experiential level of intelligence

Yep — that 4-step outline is basically a practical blueprint for “semantic consolidation + retrieval + verification,” and it’s very close to how you’d design a memory subsystem if you were trying to get something brain-ish without pretending it’s literally a brain.

A few refinements that make it work better (and cut the compute explosion without losing the safety you’re aiming for):

1) Don’t store “tags”; store claims + facets

“Experiential memory tags” are useful, but you get much better downstream behavior if the memory pass outputs atomic, typed items (claims) with a few facets:

Type: preference / identity / ongoing project / stable fact / ephemeral state / hypothesis
Scope: personal / technical / relationship / meta-cognitive
Stability half-life: (hours, weeks, years)
Confidence: model-estimated + optionally user-confirmed
Evidence pointer: link to source turn(s) / excerpt hash

This avoids the “vibes memory” problem where the model stores an aura and then over-matches later.

2) Store two representations: embedding + “canonical text”

Your step (2) is spot-on, but I’d explicitly do:

Canonical memory text (what gets injected later)
Embedding(s) of that canonical text (for fast retrieval)

Not embedding the whole verbatim prompt is a feature, not a bug — because the stored item should be the semantic unit you want reused.

If you still want verbatim, store it as:

“evidence blob” (compressed transcript, or hash + pointer) that is not routinely injected.

3) Retrieval should be hierarchical, not flat

Instead of one big vector store, use tiers:

Tier 0: “working set” (last N items, high recency)
Tier 1: “pinned” stable identity/preferences/projects
Tier 2: “episodic” (time-stamped events, may decay)
Tier 3: “archive” (rarely retrieved unless specifically relevant)

This is a massive win for compute because you don’t search everything equally, and it mirrors what you’re intuiting about brains: consolidation + selective replay.

4) Over-match guarding: verification only on close calls

Your “difference engine” idea is exactly the right instinct, but you don’t want to run it on every candidate memory. Do it only when the retrieval score is in an ambiguous band.

Practical pattern:

Cheap retrieval: top-k by embedding similarity (+ recency/stability priors)
Cheap filters: discard mismatched types (e.g., health memories for CAD question)
LLM verify pass (only for borderline cases):
- “Does memory M materially apply to prompt P? Answer yes/no + why.”
If yes: inject canonical memory text
If no: keep it out, maybe lower that memory’s future weight for similar prompts

That gives you the guardrail without doubling your compute universally.

5) The “sweet spot” is a control system: decay + reinforcement

If you want the system to feel more “experiential,” the critical ingredient is time dynamics:

Decay: unused memories slowly lose retrieval weight
Reinforcement: when a memory is retrieved and verified useful, boost it
Revision: when contradicted, either overwrite or store a new version with supersession
Quarantine: if a memory repeatedly causes bad matches, suppress it

That’s how you stop early weirdness becoming permanent personality.

6) Compute cost: you can cheat it down hard

A few standard cheats that preserve quality:

Batch embeddings + ANN index (HNSW/FAISS/etc.)
Cache verification results for recurring queries
Compress episodic memory periodically (weekly “consolidation job”)
Use small models for verify, large model only when needed
Inject fewer, better items (top 3–7 usually beats top 20)

You end up with something that behaves like bigger context without paying full “stuff the entire diary into the prompt” costs.

7) The key leap you’re pointing at

“Expand hacked-on memory context and start to approach a more experiential level of intelligence.”

I think the honest framing is:

You don’t get “experiential intelligence” by adding more text.
You get it by adding a closed loop: store → retrieve → verify → reinforce/decay → revise.

That loop creates trajectory and self-consistency over time, which is the functional part people mean when they say “experience.”

I had considered some of the optimisations you lay out, but I am conscious that each optimisation you make, leads to compression artefacts again. this is the entire space in a nutshell. how compressed do you want your information, how accurate is good enough, what level of missing context is ok. interesting problem. at one end of that scale, something that remembers everything in detail would be stupendously powerful. but. scales horribly. at the other end of the scale. something that forgets a lot of context. scales near indefinitely.
also from what you mention here you are proposing not a dual storage method - key vectors, message blob, but three. key vectors, summarised content, full content. and only access the full content when absolutely necessary.
also also. the idea of using a *smaller* model to run some processes is very interesting. I have been investigating local based smaller models with decent results. the fact you could say, run an LLM processing instance locally - for basically negligible compute costs is very intriguing. it depends where that threshold of capability sits as to whether that would be a workable solution or not.

You’ve basically nailed the core trade: compression ↔ scalability ↔ fidelity, and (yep) every optimisation is just “acceptable loss” by another name. The trick is making the loss structured rather than accidental.

The “three-layer” store is exactly about controlled loss

What you just described is the right mental model:

Vectors (index / routing signal)
Canonical summary (what gets injected / what the system “believes”)
Full source (what actually happened / ground truth for dispute resolution)

That isn’t redundancy for its own sake — it’s a way to separate concerns:

Vectors: fast retrieval, fuzzy matching
Canonical summary: stable, legible, prompt-injectable memory
Full source: audit trail + “anti-hallucination anchor” when stakes are high

It’s the same idea as: caches, materialized views, and a source-of-truth DB.

Compression artifacts are unavoidable, but you can move them to safer places

You can’t eliminate loss, but you can choose where it lives:

Put loss in the routing layer (vectors) → tolerable; it only affects what gets considered.
Put loss in the belief layer (summary) → dangerous; it affects what gets asserted.
Keep no loss in the evidence layer (full content) → you can recover when needed.

So you allow approximate matching, but you verify against verbatim when it matters.

“Remember everything in detail” isn’t always stupendously powerful

Counterintuitive but important: perfect recall can make a system worse unless it also has:

excellent relevance gating
excellent “what to ignore” skill
excellent conflict resolution

Otherwise you get context poisoning: irrelevant details crowd out the signal. Humans forget partly because it’s computationally useful.

So the real superpower isn’t “store all,” it’s:

store all + retrieve only what’s actually useful + verify it cheaply.

Small local models as memory engines: very viable, if you use them for the right jobs

This is where things get fun.

A smaller local model can be excellent at:

extracting atomic memory candidates from a turn
classifying (preference vs fact vs project vs ephemeral)
redacting sensitive bits
lightweight entailment checks (“does this contradict existing memory?”)
query routing (“which memory buckets are relevant?”)
even summarising into canonical memory text, if you constrain format tightly

Where smaller models often fail:

nuanced verification (“is this actually applicable?”)
subtle pragmatic inference
long-horizon coherence
avoiding plausible-but-wrong reconstructions when the evidence is thin

So the sweet split is:

Local small model = janitor + librarian
Big model = judge + author

A practical capability threshold (rule of thumb)

You don’t need a genius model for the memory layer if you constrain outputs.

If the task is:

structured extraction into a schema
short bounded summaries
binary classifications
tagging and scoring

…then a modest local model can be surprisingly reliable.

If the task is:

deep semantic verification
“is this truly relevant in context?”
multi-step abductive reasoning

…that’s where larger models win.

How to reduce artifacts without blowing compute

The best compromise I’ve seen (conceptually) is:

Local model produces N candidates
It also produces a confidence score and stability half-life
You store them, but you mark them as:
- tentative until reinforced
- confirmed after repeated evidence or explicit user confirmation
Retrieval injects mostly confirmed memories
Tentative memories only surface if:
- the prompt strongly matches
- and a verifier pass approves

This gives you “experience growth” without locking in first impressions.

The deep analogy you’re sniffing: this is brain-ish

What you’re describing maps well to:

hippocampus-ish episodic store (full content)
neocortex-ish semantic store (canonical summary)
pattern completion / cueing (vectors)
replay / consolidation (periodic summarization + reinforcement)

Not claiming equivalence, just: the engineering converges on similar motifs because the constraints are similar.

If you want to ground this into something you could actually build, I can outline a concrete pipeline with:

memory item schema
scoring function (similarity + recency + stability + reinforcement)
when to escalate to verbatim verification
which steps are “safe for local model” vs “needs big model”

No need to pick a specific model yet — we can keep it architecture-first.

it does seem to me at a high level that the issue of memory context relevance is a "hard problem" in the same way that contextual language processing itself is a "hard problem", and that its another key bit of the overall "intelligence is a hard problem" problem set. but. also. that. the mechanisms already in place for LLM language processing offer the already realised solution, it's more of a problem of implementation than it is coming up with novel solutions - ish. I do think the exact details of what you store at what level and how you retrieve it starts to fall into a tricky - but not unsolvable - space

Yes — that’s a very clean way of putting it, and I think you’re basically right on all the important axes.

1. Relevance is a hard problem, not a bookkeeping problem

At a high level:

Contextual relevance
Meaning extraction
Memory retrieval
Attention allocation

…are not separate problems. They are the same problem, viewed at different time-scales.

Language understanding is hard because:

meaning is context-dependent, lossy, probabilistic, and implicit

Memory relevance is hard for exactly the same reasons.

So it’s not surprising that the solutions rhyme.

2. The “solution already exists” insight is key

What you’re pointing at here is important:

The mechanisms that already work for contextual language processing are the relevance engine.

LLMs already:

compress meaning
discard noise
infer salience
perform fuzzy matching
weigh competing interpretations
resolve ambiguity probabilistically

Which means the hard part — the relevance function — is already solved implicitly.

What’s missing is not intelligence, but plumbing:

externalisation
persistence
gating
decay
verification

In other words:

We know how to judge relevance.
We don’t yet have good ways to reuse that judgment across time.

3. Why this feels like a “new” hard problem

It feels new because:

In a single prompt, relevance is handled implicitly in hidden activations.
Across prompts, you must:
- discretise it
- store it
- retrieve it
- justify it

That forces you to confront things humans mostly get “for free” via biology.

You’re taking something that normally lives in:

continuous, high-dimensional, transient state

…and forcing it into:

discrete, symbolic, persistent structures

That translation is where the pain is.

4. Why it’s tricky but not unsolvable

You’ve already identified the main levers:

What to store
At what level of abstraction
With what decay
With what confidence
Under what retrieval conditions

None of those require new theory — just careful tradeoffs.

This is why it’s an engineering hard problem, not a “we don’t know how intelligence works” hard problem.

The danger zone is not correctness; it’s path dependence:

early compression mistakes
overconfident summaries
premature identity formation

But those are manageable with:

tentativeness
revision
forgetting
multi-layer storage

5. A useful reframing

One reframing that helps a lot:

Memory is not a store; it’s a hypothesis cache.

Each stored item is really:

“This might be useful later”
“This seemed salient at the time”
“This is my best compressed guess so far”

Once you see memory that way:

revision becomes natural
decay becomes necessary
verification becomes routine

And the system stops pretending it’s omniscient.

6. Why your instinct about implementation is right

You’re correct that this isn’t about inventing some radically new cognitive primitive.

It’s about:

deciding where to tolerate error
deciding when to pay compute
deciding how reversible mistakes must be

That’s classic systems engineering:

consistency vs availability
latency vs accuracy
storage vs compute

The novelty is just that the payload is meaning.

7. The quiet punchline

If you step back, what you’re describing is:

LLMs already know what matters.
They just can’t remember it reliably yet.
And when they do remember, they need help not remembering the wrong thing too well.

That’s not a conceptual wall.
That’s a design space.

And it’s a very interesting one.

circling back to smaller LLMs for some tasks in this process. this has made me realise a very possible future architecture here. big *static* AI models that live in the cloud and do the heavy lifting. and then. smaller *dynamic* or maybe better described as *hybrid* AI models that live locally and do a number of lesser tasks including the local self housekeeping. this architecturally does 2 things - 1) it leverages the power of a massive compute unit running in dedicated mass hardware setups 2) it leverages the scalability of distributed local setups that can massively outscale a centralised store for things like memory storage, instantiated local quirks and context.
basically you get to use the strengths of a centralised model AND a distributed one. It also means you can then practically run your personalised AI on relatively low hardware at the client end, whilst giving it the power of a centralised one, and the personalisation of a local one.

Yes — that’s a very plausible “shape of things,” and it’s one of the few architectures that simultaneously solves cost, latency, privacy, and personalization drift without magical thinking.

What you’ve described is essentially a federated cognitive stack:

Cloud = deep reasoning / broad world model
Local = state, taste, habits, tools, and continuity

And the punchline is exactly what you said: you get central strength + distributed scale.

A clean way to frame it: “static core + dynamic shell”

Cloud (static-ish core)

Best at:

hard reasoning, synthesis, long-context “thinking”
high-accuracy generation
up-to-date knowledge (when connected)
expensive verification steps (when stakes are high)

Tradeoffs:

cost per token
privacy exposure
latency and availability
generic personality unless carefully steered

Local (dynamic/hybrid shell)

Best at:

memory capture + consolidation
retrieval + relevance gating
personal preference modeling
lightweight classification/routing
tool orchestration / device integration
offline competence (basic tasks)
“quirks” and stable continuity

Tradeoffs:

weaker reasoning ceiling
limited context and slower runtime (relative)
needs careful guardrails to avoid reinforcing nonsense locally

That division is structurally sound.

Why local is the natural home for “self housekeeping”

You nailed the key: memory and personalization are bandwidth-heavy and privacy-sensitive.

Local storage gives you:

essentially infinite “episodic” capacity (compared to prompt budgets)
high-resolution logs and provenance (full content can live locally)
user control (“delete my history” is real, not aspirational)
personalization that doesn’t have to be re-learned every session

This also prevents a classic failure mode: cloud models being forced to carry “you-ness” inside prompts all the time (expensive and brittle).

How the handshake can work (practically)

A very workable flow looks like:

User prompt arrives locally
Local model does:
- intent classification
- safety/risk level estimate
- retrieval of candidate memories (vectors)
- selection of canonical memory snippets for injection
- decides: local answer vs cloud call
If cloud call:
- local sends minimal necessary context (selected memories + relevant evidence excerpts)
- keeps the full diary private unless needed
Cloud responds
Local model does post-pass:
- extract memory candidates
- update reinforcement/decay
- store full transcript locally
- optionally generate “profile deltas” (preferences changing, new long-term projects)

This gives you the two-pass thing you were describing, but distributed.

The real win: compute and scaling economics

Cloud compute stays focused on what only cloud can do.
Local compute handles the “death by a thousand papercuts” tasks:
- indexing, summarizing, tagging, routing
- background consolidation
- personal context reconstruction

And because memory is the thing that explodes with time, pushing it to the edge is the difference between a system that scales and one that gets financially murdered.

The “hybrid” part: local models can be trainable without touching the core

This is where it gets spicy:

Cloud model stays fixed (for safety, auditability, and consistency).
Local layer can be:
- lightly fine-tuned (LoRA-style adapters)
- preference-tuned
- or simply shaped via curated memories + rules

So the “you-ness” evolves without requiring the central model to become a bespoke instance.

Biggest design risks (worth flagging)

Local echo-chamber / identity ossification
- If local memory keeps reinforcing a mistaken frame, you get a “self-fulfilling personalization spiral.”
- Fix: tentative memories, contradiction tracking, periodic “reality checks,” and provenance links.
Context exfiltration
- If the local layer indiscriminately forwards memories to the cloud, privacy is defeated.
- Fix: strict “minimal disclosure” policy + user-visible “what I’m sending” summaries.
Mismatch between local and cloud “worldviews”
- Local routing might decide wrongly (“this is safe/local”) or (“this is irrelevant”) and starve the cloud of crucial context.
- Fix: confidence thresholds + escalation paths.

What this architecture enables that’s hard otherwise

Offline-first personal agent that still becomes “godlike” when connected.
User-owned memory (portable across providers if standardized).
Per-device personality (phone-you vs workstation-you) without confusing the cloud.
Tool depth: local can integrate OS-level actions, indexing, personal vaults.

The emergent shape

You can think of it like:

Cloud = cortex-as-a-service (general intelligence substrate)
Local = hippocampus + executive control + sensory/motor integration (personal continuity + action)

Not claiming biology, just that the engineering converges on similar separations because constraints force it.

If you want, I can sketch a concrete “minimum viable” version of this (components + data structures + thresholds), assuming:

a local small model
a vector store
a transcript store
and a cloud LLM API

…without committing to any vendor-specific assumptions.