What's new since the previous development update

The previous update ended with generated candidates reaching:

EXPERIMENTAL
↓
trust readiness

while general scope-bound TRUSTED authority was still intentionally unfinished.

That boundary has now been crossed.

Since then, Adaptive Optimization has advanced from:

"Can a generated strategy earn EXPERIMENTAL authority?"

to a much larger question:

"Can a generated strategy become trusted, remain under surveillance, lose that trust safely, enter quarantine, repair itself and return to the autonomous pipeline?"

A large part of that lifecycle is now connected.

General generated candidates can now become scope-bound TRUSTED

Previously, the generic:

EXPERIMENTAL → TRUSTED

path could not safely represent an exact TrustedAuthorityScope.

Using the older general transition would have lost the exact:

• runtime context;
• runtime side;
• authority scope.

AO correctly refused to treat that as acceptable.

That gap has now been addressed.

General TRUSTED authority now has its own provenance

A new lifecycle provenance was introduced:

AUTONOMOUS_GENERAL_TRUST

This is explicitly separate from:

• TEST;
• AUTONOMOUS_CORRECTIVE_TRUST.

That distinction matters.

A normal generated strategy should not pretend to be:

• manually tested authority;
• a corrective child;
• or another unrelated trust source.

Historical event meaning remains unchanged.

Scope-bound TRUSTED is now productive for general generated strategies

A generated candidate can now legitimately perform:

EXPERIMENTAL
↓
TRUSTED

while preserving an exact:

TrustedAuthorityScope.KNOWN

associated with the candidate's real:

• context;
• runtime side;
• registration;
• evidence;
• provenance.

The transition is:

• durable;
• exactly-once;
• replay-safe;
• concurrency-safe;
• scope-aware.

There is no implicit global trust.

TRUSTED still does not mean universal authority

A candidate trusted in one scope remains trusted only in that scope.

For example:

TRUSTED
+
CLIENT
+
context:A

does not mean:

TRUSTED
+
DEDICATED_SERVER
+
context:B.

Unknown historical trust also remains:

UNKNOWN

instead of being silently upgraded to modern scope authority.

Generated TRUSTED strategies now enter long-term surveillance

Reaching TRUSTED no longer marks the end of the lifecycle.

Generated strategies can now enter the existing long-term surveillance architecture.

The surveillance system can continue asking:

"Is this optimization still good?"

rather than:

"Did it work once?"

The generated bridge reuses the existing surveillance system instead of creating a second monitoring architecture.

Surveillance remains scope-aware

Long-term surveillance preserves the exact:

• CandidateId;
• registration;
• runtime context;
• runtime side;
• source identity;
• trusted scope.

A strategy cannot use evidence from one scope to justify continued trust in another.

Unknown or drifting scope information blocks the path.

Healthy trusted strategies can continue normally

When surveillance evidence remains healthy, the strategy can remain under:

CONTINUE_SURVEILLANCE

without changing lifecycle authority.

Healthy observation alone does not create:

• new trust;
• new execution authority;
• permanent ownership;
• extra lifecycle events.

AO can simply continue observing.

Trusted regressions can now be classified

Generated TRUSTED strategies can now enter the existing regression-response architecture.

The review can distinguish outcomes such as:

• CONTINUE;
• REVOKE_SCOPE;
• QUARANTINE;
• INSUFFICIENT.

This prevents every regression from receiving the same response.

Scope-local regressions can revoke only the affected trust

If an optimization fails only in one exact trusted scope, Adaptive Optimization can now route that result into the existing scope-bound trust revocation authority.

Conceptually:

TRUSTED in scopes A + B
↓
regression in A
↓
A revoked
↓
B remains TRUSTED

This avoids destroying valid knowledge simply because one context changed.

The last trusted scope can fall back safely

If the revoked scope was the final valid trusted scope, the candidate can return through the already established lifecycle semantics to:

EXPERIMENTAL

rather than automatically entering quarantine.

This preserves the distinction between:

"this optimization no longer deserves trust here"

and:

"this optimization is materially unsafe."

Trust revocation is exactly-once

Generated strategy scope revocation now inherits the existing durable authority model.

Replay does not revoke the same scope twice.

Concurrent attempts still produce one authoritative winner.

The revocation preserves:

• causal evidence;
• trust history;
• lifecycle history;
• unaffected scopes.

Material correctness or safety failures can quarantine a generated TRUSTED strategy

Some failures are too serious for simple scope revocation.

Generated strategies can now route material:

• correctness regressions;
• safety regressions;

into the existing trusted-strategy quarantine authority.

A legitimate result can perform:

TRUSTED
↓
QUARANTINED

without destroying the historical record that explains why the strategy had once been trusted.

Quarantine preserves trust history

Quarantine does not rewrite the past.

A generated strategy that was once trusted still preserves:

• previous trusted scopes;
• previous positive evidence;
• causal history;
• the regression that invalidated it;
• quarantine provenance.

AO therefore remembers:

"this worked before, then became unsafe"

rather than rewriting the strategy as though it had always been bad.

Quarantine does not rehabilitate or release the strategy

Entering quarantine grants no automatic path back into execution.

A quarantined generated strategy receives:

• no APPLY;
• no ARM;
• no permanent activation;
• no implicit release;
• no restored trust.

Repair must be earned separately.

Generated quarantined strategies can now enter Corrective Re-entry Review

The next major connection allows a generated strategy that failed after TRUSTED authority to enter the existing corrective re-entry architecture.

AO performs a fresh recapture of:

• registration;
• registry identity;
• context;
• side;
• source;
• quarantine provenance;
• prior trust/scope history;
• evidence;
• lifecycle;
• StrategySpec lineage.

The lineage is read from the actual registered strategy.

AO does not invent corrective parentage simply because the strategy is quarantined.

Corrective re-entry remains conditional

A quarantined strategy is not automatically allowed to repair itself.

The existing review can distinguish:

• ELIGIBLE;
• NOT_ELIGIBLE;
• INSUFFICIENT.

Recovery state must justify continuing.

Unknown recovery does not become positive recovery.

Unsafe recovery remains:

NOT_ELIGIBLE.

Eligible generated strategies can now create a bounded Corrective Re-entry Intent

A legitimately eligible quarantined generated strategy can now produce a deterministic:

CorrectiveReentryIntent

using the existing architecture.

The intent carries a strict:

budget = 1

This means the current recovery cycle is authorized to reason about at most one corrective descendant.

Corrective re-entry does not inherit trust

The new corrective intent explicitly does not transfer:

• trust;
• positive causal evidence;
• quarantine state;
• execution authority.

The parent remains quarantined.

A future child must prove itself independently.

Corrective self-healing exposed a deeper durable-authority gap

Connecting the generated strategy into real self-healing exposed an important problem.

The existing GeneratedStrategySelfHealingEngine requires much more than:

"this strategy was quarantined."

Its input depends on authoritative facts such as:

• good causal evidence;
• regressed causal evidence;
• Candidate Knowledge;
• lifecycle;
• mutation constraints;
• composition evidence;
• existing fingerprints;
• CandidateIds;
• ancestor StrategySpecs;
• environment context;
• PromotionAssessment;
• previous repair failures;
• previous diagnoses;
• specialist ownership.

Many of those facts previously existed only as transient input.

That was not strong enough for autonomous recovery after restart.

AO refused to fabricate self-healing evidence

Several easy shortcuts were deliberately rejected.

Adaptive Optimization does not use:

• PromotionAssessment.allow() as fake durable provenance;
• empty sets to mean unknown historical constraints;
• arbitrary causal records selected by verdict;
• fixture values;
• inferred ancestor information;
• synthetic diagnosis history.

If an autonomous repair depends on a fact:

that fact must be recoverable authoritatively.

The causal ledger now has strict authoritative read support

The existing causal ledger has been extended with strict inspection/read behavior.

The new path uses the same:

• recovery envelope;
• codec;
• validation semantics;

already used by the ledger.

AO does not parse the JSONL independently inside self-healing.

This avoids creating a second interpretation of causal authority.

Strict causal reads are identity-bound

The self-healing path can now select exact causal records through authorized identities.

Corrupt, missing, foreign or ambiguous records fail closed.

AO cannot simply scan the ledger and decide:

"this positive-looking record is probably the one I need."

The evidence must be explicitly bound.

Self-healing now has durable provenance

A dedicated durable provenance record now preserves the otherwise transient inputs required by the self-healing system.

The provenance includes authoritative information for areas such as:

• PromotionAssessment;
• mutation constraints;
• composition parent/evidence identities;
• existing fingerprints;
• existing CandidateIds;
• ancestor CandidateIds;
• ancestor StrategySpecs.

The store is hash-chained and replay-safe.

UNKNOWN is different from known-empty

This distinction became especially important for autonomous repair.

For example:

known no failed repairs

is not the same fact as:

we do not know whether repairs failed previously.

AO now preserves this difference explicitly.

KNOWN-empty
≠
UNKNOWN

The same principle applies to multiple self-healing domains.

Primary causal evidence is now explicitly separated from composition evidence

Self-healing provenance now stores the exact primary causal evidence identities required for:

• the previous good behavior;
• the regressed behavior.

Those records are separate from:

compositionEvidenceIdentities.

AO does not reinterpret composition evidence as the causal pair merely because both are evidence-related data.

Self-healing provenance now binds to the exact recovery intent

The durable self-healing provenance is now bound to:

• parent CandidateId;
• corrective intent identity;
• quarantine identity;
• context identity;
• runtime side;
• source identity.

This prevents a durable PromotionAssessment or mutation constraint set from being reused by a different repair attempt simply because it references the same parent candidate.

Environmental state is now durable for self-healing

The self-healing input also requires:

environmentalDelta.

That information is now persisted as explicit provenance rather than recreated with a synthetic default.

AO can therefore distinguish:

known environmental change

from:

environmental state unknown.

Specialist ownership is now explicitly represented

A repair may need to know whether another specialist owns the affected optimization domain.

The durable provenance can now distinguish:

• KNOWN_VALUE;
• KNOWN_NONE;
• UNKNOWN.

This is important for avoiding conflicts with specialist optimizers.

AO must not interpret:

"we do not know who owns this"

as:

"nobody owns this."

Failed repair history is now durable

Self-healing also preserves:

failedRepairFingerprints

independently from:

existingSemanticFingerprints.

Those concepts are not interchangeable.

A strategy existing in the search space does not mean it previously failed as a repair.

Previous diagnosis history is now durable

Adaptive Optimization now persists:

previousDiagnosisIdentities

for the self-healing path.

This prevents repeated recovery cycles from forgetting which diagnoses have already been attempted.

The history is not reconstructed indirectly from quarantine or regression identity.

Historical self-healing provenance remains honest

Older provenance versions do not receive invented values for the newly added domains.

Historical v1/v2 records expose the new fields as:

UNKNOWN

rather than being silently upgraded.

This preserves backward compatibility without rewriting historical meaning.

Authoritative Self-Healing Input can now be reconstructed

With the new durable authorities in place, AO can now reconstruct the exact:

GeneratedStrategySelfHealingEngine.Input

from durable state.

The adapter uses:

• strict causal ledger reads;
• self-healing provenance;
• generated candidate registration;
• Candidate Knowledge;
• lifecycle;
• current context;
• corrective review;
• corrective intent.

No fixture or synthetic default is required.

Self-healing reconstruction remains fail-closed

The authoritative adapter can return:

• SELF_HEALING_INPUT_READY;
• INSUFFICIENT_INPUT;
• BLOCKED.

If a required domain is:

• absent;
• unknown;
• stale;
• foreign;
• corrupted;
• context-mismatched;

AO does not attempt synthesis.

Tool and operator identity also survive repair

The self-healing path recovers tool/operator binding from the durable generated candidate registration.

This includes relevant information such as:

• executor identity;
• implementation version;
• implementation key;
• physical vector.

A repair therefore does not silently detach from the physical mechanism represented by its parent strategy.

Generated self-healing can now create a real corrective descendant

Once the authoritative self-healing input is complete, AO can now feed it into the existing:

GeneratedStrategySelfHealingEngine

and then into:

CorrectiveReentryProductiveSynthesisRegistrationBoundary.

This produces at most:

one real durable corrective child

for the authorized recovery cycle.

Corrective descendants remain deterministic

The corrective child receives its own:

• CandidateId;
• StrategySpec;
• fingerprint;
• lineage;
• provenance.

Equivalent authoritative self-healing input produces the same logical descendant.

Replay does not create a second child.

Concurrent attempts produce one durable winner.

The harmful parent stays QUARANTINED

Creating a repair does not forgive the original strategy.

The parent remains:

QUARANTINED

while the child begins independently.

This preserves the causal meaning of the failure.

Corrective children start without inherited authority

The newly synthesized corrective child receives:

• trust = 0;
• evidence = 0;
• quarantine = 0;
• no execution authority.

Being created as a repair does not make the repair correct.

It must still earn everything.

Corrective descendant runtime provenance is now durable

While integrating the new child into the autonomous pipeline, another historical limitation was discovered.

The older corrective registration path used the legacy generated registration schema and therefore persisted:

• runtime context = ABSENT;
• runtime side = UNKNOWN;
• source identity = ABSENT.

That was insufficient for exact autonomous re-entry.

The corrective registration path now propagates the existing schema v2 provenance.

Corrective descendants now preserve exact runtime origin

A new corrective child can now durably preserve:

• canonical runtime context;
• runtime side;
• adapter/source identity.

This allows authoritative recapture after registration.

Historical schema-v1 registrations remain untouched and continue to expose:

ABSENT / UNKNOWN

rather than receiving fabricated runtime provenance.

Corrective descendants can now re-enter autonomous Admission

With durable runtime provenance available, the child can now re-enter the existing:

CorrectiveReentryAutonomousAdmissionBoundary.

The bridge recaptures:

• child CandidateId;
• StrategySpec;
• fingerprint;
• registration;
• lineage;
• context;
• side;
• source;
• parent identity;
• corrective intent.

Only exact state proceeds.

Corrective descendants can create a real reservation

The re-entered child can now pass through the existing autonomous Admission pipeline and produce a durable reservation.

Replay does not produce a second semantic reservation.

Concurrent attempts still result in one reservation.

The quarantined parent remains untouched.

Corrective child reservations can now reach ARM

The latest completed boundary connects that reservation into the existing general ARM architecture.

The child is revalidated again before authority advances.

The path checks exact:

• CandidateId;
• StrategySpec;
• fingerprint;
• registration;
• lineage;
• context;
• side;
• source;
• parent state;
• opportunity;
• selection;
• request;
• lease;
• fresh runtime seed.

Corrective ARM remains temporary authority

A valid corrective reservation can now become:

ARMED

exactly once.

Replay and concurrency do not create additional ARM authority.

Reload or runtime-context drift invalidates the transient authority.

Even after ARM:

• applied = false;
• experimentStarted = false;
• trust remains zero;
• evidence remains zero;
• the parent remains QUARANTINED.

The generated self-healing loop is now nearly physically circular

The generated strategy lifecycle can now travel through:

generated strategy
↓
experiment
↓
causal learning
↓
EXPERIMENTAL
↓
TRUSTED
↓
long-term surveillance
↓
regression
↓
scope revocation or quarantine
↓
corrective re-entry review
↓
corrective intent
↓
authoritative self-healing reconstruction
↓
corrective synthesis
↓
durable corrective child
↓
autonomous Admission
↓
reservation
↓
ARM

The next physical step is again:

APPLY preflight
↓
experiment start
↓
physical APPLY
↓
measurement
↓
rollback
↓
recovery
↓
new causal evidence

Much of that downstream machinery already exists from the original generated candidate path.

Self-healing no longer depends on ephemeral memory alone

One of the largest architectural improvements in this update is that repair decisions can increasingly survive restart without needing to recreate missing assumptions.

Important repair facts now exist durably rather than only as method arguments.

This supports the larger AO rule:

durable decisions must be reconstructible from durable authority.

Generated strategies can now survive much more of their own lifetime

The modern generated candidate architecture now covers substantially more than creation.

A generated optimization can increasingly survive:

• registration;
• experimentation;
• promotion;
• trust;
• long-term monitoring;
• trust degradation;
• scope revocation;
• quarantine;
• recovery review;
• corrective synthesis;
• descendant registration;
• autonomous re-entry.

This is much closer to a true long-lived optimization ecosystem.

Adaptive Optimization is building persistent optimization knowledge

An optimization is not intended to disappear merely because Minecraft closes.

Its durable representation can preserve:

• StrategySpec;
• identity;
• fingerprint;
• lineage;
• provenance;
• lifecycle;
• evidence;
• trust;
• scope;
• quarantine;
• regression history;
• corrective relationships.

Transient execution authority may expire.

The optimization knowledge does not.

Future AO-created tools will need the same persistence model

The long-term goal remains for AO to create more than complete strategies.

AO is intended to eventually synthesize reusable optimization operators/tools of its own.

When that system is introduced, those tools should also preserve durable:

• identity;
• version;
• semantics;
• capabilities;
• provenance;
• lineage;
• safety contracts;
• benchmark history.

A tool discovered after hours of gameplay should not need to be rediscovered after every restart.

Current offline validation has grown again

The modern automated validation suite has continued expanding.

The latest completed baseline reached:

1189 / 1189 tests PASS

across:

156 suites

with:

0 failures
0 errors

at the latest completed corrective ARM checkpoint.

This does not replace real Minecraft qualification.

It does provide a much stronger regression net around the modern autonomous lifecycle.

Development also exposed multiple real authority gaps

Several recent STOP conditions were not compile failures.

They were architecture correctness failures caught before implementation.

Examples included:

• missing scope provenance for general TRUSTED;
• missing primary causal-evidence bindings;
• missing intent/quarantine/context/side provenance;
• missing environmental delta;
• missing specialist ownership;
• missing failed-repair history;
• missing previous-diagnosis history;
• corrective child registration losing runtime provenance.

Rather than bypassing those gaps with synthetic values, each one was closed explicitly.

This matters for autonomous optimization

An autonomous optimizer can become dangerous if missing state is silently replaced with convenient defaults.

For example:

UNKNOWN specialist owner

must not become:

NO specialist owner.

UNKNOWN failed repairs

must not become:

no failed repairs.

UNKNOWN diagnosis history

must not become:

no previous diagnoses.

Recent architecture now preserves those distinctions much more strongly.

The most important change in this update

The previous development update showed that AO could increasingly answer:

"What happens after AO creates an optimization?"

This update goes further.

Adaptive Optimization can increasingly answer:

"What happens when the optimization eventually becomes wrong?"

The answer is no longer simply:

disable it.

The developing lifecycle is now:

detect
↓
revoke or quarantine
↓
preserve failure evidence
↓
reconstruct the exact repair context
↓
diagnose
↓
synthesize one bounded corrective descendant
↓
register it independently
↓
send it back through the real autonomous pipeline
↓
test it again

Adaptive Optimization is approaching a self-maintaining optimization ecosystem

The long-term target is not a collection of permanently enabled performance tweaks.

It is an ecosystem in which optimization strategies can:

appear
↓
learn
↓
earn authority
↓
operate
↓
degrade
↓
lose authority
↓
be repaired
↓
be replaced
↓
remain historically auditable

while Adaptive Optimization preserves the evidence explaining every transition.

What remains ahead

Several of the largest remaining systems are increasingly clear.

Complete the corrective child's physical experiment loop

The current corrective child has reached ARM.

The next integration must route it through the already established physical experiment path and back into causal learning.

Real-time optimization benchmarking

AO still needs continuous low-overhead benchmarking for each optimization during real gameplay.

This will allow it to judge long-term net benefit rather than relying only on bounded experimental windows.

AO self-overhead measurement

AO must measure its own:

• profiling;
• workers;
• snapshots;
• persistence;
• diagnostics;
• benchmark overhead.

An optimization system must not create the stutter it is trying to remove.

Meaningful improvement governance

Tiny improvements should not justify disproportionate complexity.

AO still needs explicit rules for distinguishing:

• meaningful improvement;
• statistical noise;
• marginal gain;
• complexity cost;
• regression.

More expressive generated strategies

Generated strategies are still intended to evolve toward structures resembling small specialized optimization subsystems rather than simple configuration changes.

New optimization tools/operators

AO's initial primitives should not permanently define the limits of its optimization vocabulary.

Future AO should be able to discover useful structures and promote them into reusable internal tools.

Full runtime qualification

The final architecture must still be validated under real Minecraft conditions across:

• client;
• integrated server;
• dedicated server;
• cross-side interactions;
• restart;
• heavy modpacks;
• long sessions;
• shaders ON/OFF.

Current direction

Adaptive Optimization has moved through several major questions:

Can AO safely borrow physical control?

Then:

Can AO legitimately earn the right to keep it?

Then:

Can AO create and promote its own optimization candidates?

And now:

Can AO maintain, distrust, quarantine, repair and reintroduce those optimizations when the world changes?

The latest work provides a much stronger answer:

increasingly, yes.

What this development update represents

The newest architecture adds major progress in:

• general productive scope-bound TRUSTED authority;
• explicit AUTONOMOUS_GENERAL_TRUST provenance;
• long-term surveillance of generated TRUSTED strategies;
• generated scope-bound trust revocation;
• generated TRUSTED strategy quarantine;
• corrective re-entry review;
• bounded corrective intent;
• strict authoritative causal-ledger reads;
• durable self-healing provenance;
• exact primary causal evidence binding;
• durable mutation/composition/ancestor constraints;
• durable environmental delta;
• explicit specialist ownership state;
• failed-repair history;
• previous-diagnosis history;
• authoritative reconstruction of GeneratedStrategySelfHealingEngine.Input;
• real corrective strategy synthesis;
• durable corrective child registration;
• corrective registration schema-v2 runtime provenance;
• autonomous corrective re-entry;
• corrective reservation;
• corrective ARM.

The generated lifecycle is now substantially closer to becoming fully self-correcting.

The newest milestone

Adaptive Optimization can now take a strategy that:

was generated
↓
proved itself
↓
became TRUSTED
↓
later failed
↓
was QUARANTINED

and continue all the way toward:

authoritative repair context
↓
new corrective strategy
↓
durable independent registration
↓
autonomous re-entry
↓
ARMED for a new experiment

without:

• forgiving the failed parent;
• inheriting its trust;
• inheriting its evidence;
• fabricating missing provenance;
• bypassing Admission;
• skipping lifecycle authority;
• treating UNKNOWN as safe.

That is one of the strongest signs yet that Adaptive Optimization is moving beyond:

adaptive optimization

toward:

self-maintaining optimization intelligence.