What you’re calling “delegation of attention” is basically the amortisation move that 4QX is built to make cheap:
- spend a burst of expensive, general intelligence to form a new stable pattern/context (a Name/holon in the trie), then
- let child execution happen later via bounded, cheaper quanta (delegates, interpreters, schedulers), with the whole thing remaining seam-legible and replayable.
That’s not just an “app idea”; it falls out of several core pieces already in the compendium.
Where your idea lands in the canonical 4QX machinery
1) “New conversational context” = Instance loop doing context formation
In 13_COGNITIVE_SUBSTRATE, the dual-triangle engine explicitly separates an Instance loop (perception/adaptation) from a Class loop (execution/learning), with a shared seam and “no shortcuts.”
The Instance side includes attention allocation and context formation as core perception capabilities, and it’s tied to self/world modelling:
- Self-reference (BL) and world-model (TL) are maintained and updated by the cycle.
So your “general intelligence creates a new conversational context (new idea)” maps cleanly to:
Instance telos (BL → TL → TR → BL) — “Inner Form discovers and uses the outer world.”
2) “Child threads” = Names resolving to deeper Class holons + child Instances
The trie doc states the caller-side mechanism almost exactly in your terms:
- “The Name appears in TL as a pattern key”
- BL→TL (IStart): select salient patterns
- TL→TR (IDo): choose what to call now
- Selected Names resolve to Class holons deeper in the trie
- Those run as child Instances in the same present slice
That’s the structural version of “spawn child threads that can be done later with cheaper/specialised agency.”
3) “Cheap agency over time” = attention quanta + Quanta Market + lazy time
4QX makes “attention” discrete and schedulable:
- The trie doc defines an executional attention quantum as “a single application of one micro-phase of the six-phase cycle … at a particular Name path,” with properties like discrete/local/finite.
Implementation-wise, OracleMultiplex is described as providing a quantum scheduler and delegation wrappers, and then it introduces the Quanta Market:
- work is in discrete AttentionQuantum units that can be “scheduled, priced, audited, and replayed”.
The “over time” part isn’t hand-wavy either. 09_TIME_AT_THE_SEAM frames TR as lazy time (a LazyGroup of event names where only the current slice is materialised), and it explicitly ties seam width to attention bandwidth.
So your “child threads carried out later” is literally: events remain potential in TR until funded/cleared into quanta.
4) “The perl child already exists” = self-organising namespace + reuse + idempotence
Two reuse mechanisms show up directly:
- In the cognitive substrate vision, knowledge lives in a self-organising namespace where frequently used patterns “bubble up,” similar concepts cluster, and unused knowledge prunes.
- In the trie doc, Names called often are cached and pushed “closer,” while disuse fades paths.
And critically, 4QX leans on idempotent operations (“every action is safe to repeat”) to make distributed reuse safe.
That’s exactly what you want for a “smart quotes normaliser”: a transformation you can safely re-run anywhere without new reasoning.
Mapping your smart-quotes example to the quadrants and phases
Let’s make the correspondence explicit.
The problem you described is “debt_coh”
06_HARMONY_CONVERGENCE defines H operationally as:
- debt_coh = structural/pattern misalignment (Form)
- debt_flux = backlog/unprocessed events (Flux)
- H = debt_coh + debt_flux
Smart quotes causing repeated LLM edit failures is a textbook pattern misalignment (debt_coh). The fix is to create a stable operation that removes that misalignment so future work doesn’t pay the same penalty.
First time: expensive reasoning compiles a new delegate-pattern
A plausible 6-phase story (staying faithful to the compendium’s loop semantics):
- IStart (BL→TL): detect repeated mismatch / friction; decide “we need a normalisation pattern.” (Perception includes attention allocation and context formation.)
- IDo (TL→TR): post an intention/offer at the seam: “normalise quotes in folder X.”
- CStart (TL→TR): accept that offer as a runnable class operation.
- CDo (TR→BR): execute the actual work (run the perl script).
- CFinish (BR→TL): publish/update the pattern library: store the script + metadata as a reusable Name.
- IFinish (TR→BL): integrate results, update self-model/resources.
That “publish the script into TL” step is what turns the one-off reasoning into a reusable compiled asset.
Later times: cheap execution reuses the Name; no new LLM script-writing required
Now the “caller-side” mechanism kicks in:
- The Name exists in TL as a pattern key.
- In IStart you ask “which patterns are salient?”
- In IDo you call the existing Name.
- It resolves to a Class holon deeper in the trie and runs as a child instance.
So the later run consumes interpreter/executor quanta (plus logging) rather than LLM quanta spent inventing the script again.
Why this is structurally safe in 4QX (and not just caching)
One subtle but important piece: delegation is constrained.
The production surface defines a DelegateEnvelopeSchema to wrap delegate invocations and enforce context/constraints (including required fields like "role", "constraints", "query", "arena").
It also defines default kernel constraints for delegates, including:
- SEAM_ONLY: no BL↔BR shortcuts
- H_MONOTONE: internal steps must not increase H
- REPLAYABLE: actions reconstructible from seam log
- WORLD_AWARE: acknowledge world state when present
This matters because your “perl child” isn’t just a convenience; in 4QX it becomes a delegate that must remain:
- seam-legible (auditable),
- H-progress respecting, and
- replay-verifiable.
That’s how delegation doesn’t silently reintroduce the forbidden BL↔BR “back-channel” that breaks the architecture.
A practical “delegation-of-attention” design pattern for your example
If you wanted to encode your smart-quotes normaliser as a first-class 4QX pattern, the minimum structure is:
A) Give it a stable Name and interface
Example naming scheme (illustrative):
/Text/Normalize/Quotes/SmartToASCII
The key is: the Name is a pattern key in TL that can be called from contexts.
B) Make the delegate idempotent + deterministic
Idempotence is central to 4QX reliability (“safe to repeat”), and the implementation guidance emphasises idempotence + replay safety as testing targets.
For quote normalisation, idempotence is achievable: running it twice should produce no further changes after the first pass.
C) Wrap it in an envelope with constraints + arena
The delegate wrapper needs the envelope schema and default constraints (seam-only, H-monotone, replayable, world-aware).
Conceptually: “run smart-quote normalisation for folder X” is the query, and the world picture (arena) pins the operation to a specific environment.
D) Schedule it with quanta, not “sessions”
Instead of “call the LLM and hope it finishes,” the work becomes:
- bids/priority → market clearing → quanta execution.
The production code even sketches deterministic clearing for bids (“same input → same output”), which is exactly what you want for replayable delegated work.
E) Publish back into TL (so future contexts discover it)
This is the step that turns delegation into a scaling law: reuse happens because the trie self-organises and frequently used patterns stay close/cached.
One crisp way to phrase it in 4QX terms
If you want a compact definition consistent with the docs:
Delegation of attention = the process by which an Instance loop pays an up-front cost to form/publish a new TL pattern (Name + constraints + delegate), so that subsequent executions are discharged by Class holons as bounded attention quanta (CDo) under envelope constraints—reducing future debt_coh and shifting work from expensive reasoning to cheap replayable execution.
This is exactly the “discrete outside, continuous inside, same kernel” north-star framing in field semantics.
If you want, I can also show how your smart-quotes delegate fits into the FieldGO (Fit→Fund→Run→Harvest→Publish) interpretation, where “Fit” is salience scan, “Run” clears into quanta, and “Publish” updates the pattern library—but the above should already line up tightly with your example.