What's new since the previous development update

Adaptive Optimization has made another major step toward its long-term autonomous architecture.

The previous stage established that generated strategies could:

be created
↓
experiment
↓
earn TRUSTED authority
↓
be monitored
↓
regress
↓
enter quarantine
↓
generate a corrective descendant

The newest work extends that lifecycle much further while also beginning to connect it to real runtime monitoring, restart recovery and persistent strategy infrastructure.

Important current architecture status

Adaptive Optimization is currently still a:

hybrid system

The older legacy optimizer is still active and remains responsible for a significant part of the practical optimization currently happening during gameplay.

The modern architecture has not yet completely replaced it.

At the same time, many modern systems are already active or becoming integrated around it, including:

• causal evidence;
• Candidate Knowledge;
• lifecycle;
• promotion;
• TRUSTED authority;
• surveillance;
• rollback/recovery;
• persistence;
• benchmarking;
• self-healing.

So the current state is not:

legacy OR modern

but increasingly:

legacy optimization + modern authority/infrastructure

while the remaining migration continues.

Corrective strategies can now complete real experiments

Corrective children are no longer limited to:

ARM

They can now continue through the physical experiment lifecycle:

ARM
↓
APPLY
↓
measurement
↓
rollback
↓
recovery
↓
causal finalization

The quarantined parent remains quarantined during this process.

The repair must prove itself independently.

Corrective strategies can now earn authority of their own

After producing valid causal evidence, a corrective child can continue through:

Candidate Knowledge
↓
AVAILABLE
↓
EXPERIMENTAL
↓
TRUSTED

Trust is still:

• scope-bound;
• evidence-backed;
• context-aware;
• restart-safe;
• exactly-once.

A repair never inherits the trust of its parent.

Repairs can themselves fail and be repaired

A corrective child is not assumed to remain correct forever.

If it later regresses, it can also enter:

surveillance
↓
regression
↓
revocation / quarantine
↓
corrective re-entry

AO can then generate another corrective descendant.

The architecture now demonstrates multiple corrective generations while preserving full lineage.

Conceptually:

strategy
↓
repair #1
↓
repair #2
↓
repair #3

without erasing the history of previous failures.

TRUSTED surveillance is becoming a real runtime system

Surveillance is no longer only a read-only concept.

AO now has real infrastructure for:

• durable surveillance ownership;
• session-local live owners;
• generation fencing;
• observation windows;
• scheduling;
• bound surveillance evidence;
• live regression review;
• productive trust revocation or quarantine.

Restart preserves the durable surveillance definition.

It does not revive stale live authority.

Real-time benchmarking is now taking shape

AO now has a dedicated architecture for continuously evaluating whether an optimization remains useful during gameplay.

Runtime benchmark evidence can distinguish states such as:

• meaningful improvement;
• negligible improvement;
• regression;
• unstable behavior;
• insufficient evidence.

This begins addressing an important question:

"Is this optimization still helping?"

instead of only asking whether it worked during its original experiment.

Runtime regressions can feed self-healing

The runtime benchmark and surveillance systems are increasingly connected to the existing corrective architecture.

A future normal runtime flow can now increasingly resemble:

gameplay
↓
benchmark
↓
regression detected
↓
trust review
↓
revocation / quarantine
↓
self-healing
↓
new corrective strategy
↓
new experiment

This brings AO closer to continuous autonomous maintenance.

AO can now examine its own overhead

Adaptive Optimization is also beginning to measure the cost of running itself.

AO can reason about overhead caused by areas such as:

• observation;
• profiling;
• benchmarking;
• workers;
• persistence;
• diagnostics.

If monitoring itself becomes too expensive, AO now has architecture for bounded and reversible mitigation.

This creates an important rule:

the optimizer itself must also justify its cost.

Generated strategies now have stronger durable identity

Generated StrategySpec definitions can now exist independently in a canonical durable strategy store.

This separates:

what the optimization is

from:

what lifecycle state it currently has.

The durable strategy definition can preserve things such as:

• operators;
• parameters;
• bounds;
• constraints;
• physical bindings;
• lineage;
• provenance;
• context;
• runtime side.

while trust, lifecycle and experiment state remain separate.

Legacy data is being preserved safely

AO is not deleting or force-converting old state just to make the architecture look modern.

Legacy data remains preserved when required.

Modern records are increasingly written into canonical locations, while older formats remain readable when necessary.

Unknown historical information stays:

UNKNOWN

instead of being silently invented.

Restart recovery has expanded significantly

Restart is increasingly treated as a normal lifecycle event.

AO now has architecture for reconstructing or resuming important durable state involving:

• experiment reservations;
• strategy definitions;
• causal evidence;
• benchmark history;
• promotion;
• TRUSTED state;
• surveillance.

The guiding rule remains:

knowledge may survive restart; temporary physical authority does not.

Shared knowledge remains descriptive, not authoritative

AO is also building infrastructure for sharing optimization knowledge between installations.

Imported knowledge may eventually help selection and reasoning.

However it cannot directly grant:

• TRUSTED authority;
• execution authority;
• promotion;
• quarantine decisions.

Another installation can provide information.

It cannot remotely decide what the local AO should trust.

Current validation status

The development suite has grown to approximately:

2145 tests

with the full runs remaining functionally successful.

Occasional Windows @TempDir / DirectoryNotEmptyException cleanup failures were classified as environmental and passed when rerun independently.

Real Minecraft qualification remains separate from offline testing.

What still remains

The largest remaining work is no longer basic lifecycle architecture.

It is primarily:

Final runtime integration

Continue connecting the durable modern architecture naturally into actual gameplay so fewer optimization decisions depend on legacy paths.

Legacy → modern migration

Preserve the optimizations that already provide real performance improvements while moving their decision-making toward:

• causal evidence;
• modern lifecycle;
• runtime benchmarking;
• scope-aware authority;
• rollback;
• long-term surveillance.

More expressive generated strategies

Future generated strategies should become closer to small specialized optimization systems rather than simple combinations of existing parameters.

New tools/operators

AO should eventually be able to discover that it lacks a useful optimization capability and create a reusable internal tool/operator rather than remaining limited forever to its original primitive set.

Real Minecraft qualification

The full architecture still needs long-session validation across:

• CLIENT;
• INTEGRATED_SERVER;
• DEDICATED_SERVER;
• restart/rejoin;
• shaders ON/OFF;
• heavy modpacks;
• 1000+ mods;
• long idle sessions;
• active gameplay;
• generated strategies;
• regression;
• quarantine;
• self-healing.

Current direction

Adaptive Optimization has progressed from:

Can AO optimize Minecraft?

to:

Can AO prove that an optimization helped?

then:

Can AO create and trust its own optimizations?

then:

Can AO detect when they become wrong and repair them?

and now increasingly:

Can AO maintain that entire optimization ecosystem continuously during real gameplay?

The answer is becoming:

increasingly, yes.

Where AO stands today

Adaptive Optimization is not yet fully migrated to the modern optimizer.

The legacy optimizer still plays an important role in the performance improvements users currently experience.

But around it, AO now has increasingly mature systems for:

learning
↓
causal evidence
↓
durable knowledge
↓
promotion
↓
TRUSTED authority
↓
continuous surveillance
↓
regression detection
↓
quarantine
↓
self-healing
↓
corrective experimentation

The remaining challenge is increasingly about connecting these systems into one continuous productive runtime rather than inventing the foundations themselves.

Adaptive Optimization is moving from:

an optimizer with adaptive systems

toward:

a persistent self-observing, self-correcting optimization system.