Adaptive Optimization(reborn)

🧠 Adaptive Optimization — Real Adaptive Performance for Minecraft

✅ Adaptive Optimization is now officially Stable

Adaptive Optimization has officially entered its Stable development phase.

After a major internal reconstruction of its observation, profiling, persistence, workload attribution, and performance-measurement systems, the mod now has a much stronger and more reliable foundation than previous previews.

Adaptive Optimization is no longer just an experimental concept built around predefined optimization behavior.

It is becoming a real adaptive optimization platform designed to:

Observe → Identify → Diagnose → Experiment → Measure → Learn → Improve

The current stable version already provides real performance improvements in Minecraft, while future previews will continue expanding its autonomous optimization capabilities.

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⚠️ Important Development Note

My mods are developed with extensive help from AI.

I am still learning Java and Minecraft mod development, so even though Adaptive Optimization is now considered stable, this does not mean that bugs or unusual compatibility cases are impossible.

Stable means that the current foundation has reached a level where it is reliable enough for normal use and continued development without constantly rebuilding the core architecture.

I still recommend testing new versions in your own modpack before using them in an important world, especially in very large or unusual modpacks.

If you encounter:

• Performance regressions
• Compatibility issues
• Unexpected behavior
• Stuttering
• Crashes
• Or situations where Adaptive Optimization seems to make something worse

please report them.

Real-world reports are extremely useful for improving the system.

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⚙️ Current Version

Currently available for:

Forge 1.20.1

Future support is planned for additional Minecraft versions and mod loaders.

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🚀 What Adaptive Optimization Actually Does

🧠 Adaptive Runtime Optimization

Adaptive Optimization continuously observes how Minecraft behaves while you play.

Instead of relying entirely on a static list of tweaks, AO builds runtime information about the current workload and uses that information to determine where optimization opportunities may exist.

Different modpacks can behave completely differently.

AO is designed around that reality.

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🔍 Real Workload Detection

Adaptive Optimization can now distinguish many different types of workloads, including:

• Vanilla Minecraft
• Modded workloads
• Individual mods
• Classes and methods
• Client workloads
• Server workloads
• Entities
• Pathfinding
• Scheduled ticking
• Chunk-related work
• Networking
• I/O
• Rendering
• Commands
• Filesystem activity
• Lock contention
• CPU pressure
• Memory pressure
• Garbage collection
• Unknown or unclassified workloads

The mod does not need to pretend it understands something when the evidence is insufficient.

When necessary, a workload can remain UNKNOWN until AO gathers better information.

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🎯 Vanilla / Mod Attribution

AO can determine whether observed workload belongs to:

• Minecraft Vanilla
• A specific mod
• A particular subsystem
• A semantic workload group
• Or an unknown source

This gives the optimizer a much better understanding of what is actually consuming resources instead of simply reacting to global FPS or tick values.

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📊 Advanced Runtime Profiling

The profiling system has been heavily rebuilt.

Adaptive Optimization can analyze workload behavior across multiple time windows and identify whether something is:

• Emerging
• Persistent
• Disappearing
• Sporadic

It also performs stack-based attribution so related workload can be grouped together instead of being fragmented across dozens of individual methods.

This allows AO to identify real performance patterns much more accurately.

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⏱️ Real Server Tick Measurement

Adaptive Optimization now includes proper server-side tick-duration measurement.

AO observes the Forge server tick boundary while also using Minecraft's own internal tick timer as an independent reference.

These measurements remain separate so AO can detect differences between:

• Minecraft's central tick workload
• Additional Forge/mod workload surrounding it

This gives Adaptive Optimization much better visibility into:

• MSPT
• Tick instability
• Server-side workload spikes
• Modded server pressure
• Simulation slowdowns

The monitoring system was specifically designed to have extremely low overhead.

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🧪 Causal Performance Measurement

One of the biggest architectural changes is the new experimental measurement system.

Adaptive Optimization now has separate measurement phases:

PRE

Performance before an optimization.

EXPERIMENT

Performance while the candidate optimization is active.

RECOVERY

Performance after the optimization is reverted.

This architecture is being built so AO can eventually determine whether an optimization actually caused an improvement, rather than seeing performance change and assuming the optimization was responsible.

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🛡️ Workload Comparability

Minecraft performance changes constantly.

Moving into another dimension, generating terrain, garbage collection, another mod becoming active, or the profiler losing samples can make two performance measurements impossible to compare fairly.

Adaptive Optimization now detects many of these situations.

Examples include:

• Workload changes
• Target disappearance
• World changes
• Dimension changes
• Resource reloads
• Lifecycle changes
• CPU-pressure shifts
• Garbage collection
• Heap changes
• Lock contention
• Network bursts
• Sampling degradation
• Missing observations

If an experiment cannot be trusted, AO can mark it:

INCONCLUSIVE

instead of learning incorrect information.

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🧬 Adaptive and Evolution-Based Optimization

Adaptive Optimization contains systems designed to generate, evaluate, remember, and improve optimization strategies over time.

The long-term goal is not simply:

Enable optimization X.

Instead, AO is being developed toward:

Determine what the current game needs, test possible solutions, measure them, reject bad solutions, preserve good ones, and improve future decisions.

Existing adaptive systems are being progressively connected to the newer and much more reliable observation and causal-evaluation architecture.

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💾 Persistent Learning

Adaptive Optimization can preserve useful information between sessions.

Its persistence system has also been rebuilt and corrected to provide much safer long-term state handling.

The goal is for AO to eventually remember things such as:

• Strategies that worked
• Strategies that failed
• Which workloads they affected
• Which mods were involved
• Which environments produced good results
• Which candidates should not be attempted again

This allows optimization knowledge to become more useful over time instead of restarting from zero every session.

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🎮 Focus on Real Gameplay Stability

Adaptive Optimization is not designed only to chase the highest possible FPS number.

Its larger goal is improving the overall stability of Minecraft.

That includes:

• Frametime consistency
• Tick stability
• Reduced workload spikes
• Less stutter
• Better responsiveness
• Better behavior under large modpacks
• More consistent long gameplay sessions

A Minecraft instance that averages 200 FPS but constantly freezes is not truly optimized.

AO focuses on the complete performance experience.

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🧩 Designed for Modded Minecraft

Adaptive Optimization is being developed specifically with complex modded environments in mind.

Instead of assuming every workload is Vanilla, AO can observe mod ownership and identify modded behavior.

This becomes increasingly important as modpacks grow larger and multiple systems begin interacting with each other.

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🤝 Adaptive Optimization and My Other Optimization Mods

Adaptive Optimization is not intended to make my other optimization projects obsolete.

Instead, the long-term goal is to create an optimization ecosystem.

Projects such as:

Sync Fix

Specialized optimization for tick/synchronization-related systems and other workloads within its domain.

Chunks Optimization

Deep optimization of chunk loading, generation, consistency, and large-scale chunk workloads.

Logs Fixer

Reduces unnecessary logging overhead and prevents excessive log spam from affecting performance.

Mods Fixer

Detects and corrects problems whose root cause exists inside third-party mods, including compatibility problems and inefficient or broken mod behavior.

Future Render Optimization

Specialized optimization of rendering workloads.

Adaptive Optimization can still optimize additional parts of these same areas when it discovers a complementary optimization opportunity.

The idea is not to artificially restrict AO.

Instead:

Specialized mods deeply optimize their subsystem, while Adaptive Optimization provides an additional adaptive and cross-system layer.

The systems should work together instead of blindly performing the same optimization twice.

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🔬 Safe Optimization Philosophy

Adaptive Optimization is not intended to gain performance by simply disabling features or aggressively skipping game logic.

The project is being developed around safer optimization techniques such as:

• Caching
• Memoization
• Deduplication
• Batching
• Coalescing
• Object reuse
• Better scheduling
• Deferring appropriate work
• Correct invalidation
• Fast paths
• Safe interception
• Controlled rewrites

Any future autonomous optimization system must also be capable of rejecting candidates when they are unsafe or ineffective.

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🛡️ Performance Without Breaking Gameplay

An optimization is useless if it breaks the game.

The new architecture separates several questions:

Did performance improve?

Was the optimization safe?

Did the game continue behaving correctly?

Future autonomous experiments will evaluate these independently before allowing an optimization to become trusted.

Possible experiment results include:

• ACCEPT
• REJECT
• INCONCLUSIVE
• ABORTED

This allows AO to be much more conservative when evidence is uncertain.

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🔮 What Comes Next?

Stable does not mean development is finished.

Adaptive Optimization still has a much larger roadmap.

The upcoming previews will focus on connecting the systems that have already been built.

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🧪 Upcoming — Autonomous Experiment Coordinator

The next major stage is the central Experiment Coordinator.

Its job will be to safely manage:

Observation
↓
Baseline measurement
↓
Candidate application
↓
Stabilization
↓
Experimental measurement
↓
Rollback
↓
Recovery measurement
↓
Evaluation

It will also protect experiments against crashes and restarts so an unfinished experiment cannot silently become permanent optimization state.

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🧠 Upcoming — Real Causal Learning

Once the experimental coordinator is proven reliable, the adaptive learning systems will begin receiving trustworthy causal results.

Instead of learning:

Performance changed after I did something.

AO will move toward learning:

This exact candidate improved this comparable workload under these conditions and passed the required safety checks.

This is one of the most important goals of the project.

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🧬 Upcoming — Autonomous Candidate Generation

Later previews will continue moving AO beyond predefined optimization lists.

The goal is for Adaptive Optimization to eventually discover workloads and generate safe optimization candidates automatically.

It should be capable of finding optimization opportunities that were not explicitly programmed for one specific mod or one specific modpack.

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🌐 Future — Shared Adaptive Technology

The adaptive architecture being developed for AO may eventually be brought into several of my other optimization mods.

That would allow each specialized optimizer to learn and adapt within its own area while Adaptive Optimization handles broader and cross-system decisions.

The long-term vision is an ecosystem of optimization systems capable of cooperating instead of working independently.

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⚠️ Important Notes

Adaptive Optimization is not an instant FPS booster.

Results will vary depending on:

• Hardware
• Minecraft settings
• Modpack
• Current workload
• World
• Shaders
• Other optimization mods

Some situations may show large improvements while others may show smaller changes.

The objective is not to fake performance improvements.

It is to make Minecraft genuinely perform better whenever a safe and measurable optimization opportunity exists.

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❤️ Credits

Thanks to TishinaCentral for testing and showcasing Adaptive Optimization:

https://youtu.be/oajlZEfVfnc

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💬 Discord

You can join the Discord server here:

https://discord.gg/9WZDxVY4m

Bug reports, testing results, compatibility reports, and feedback are very useful for improving the project.

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☕ Support the Project

Support development at:

buymeacoffee.com/col9kam

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⚙️ About the Development Environment

As of version 8.3.7, Adaptive Optimization officially migrated to IntelliJ IDEA.

Early versions of the project were prototyped using MCreator, but the current system is independently coded and has grown into a much larger Java project with dedicated systems for:

• Observation
• Profiling
• Attribution
• Persistence
• Adaptive parameters
• Optimization strategies
• Runtime measurement
• Experimental evaluation
• Safety
• Learning

This migration gave the project significantly more control over architecture, performance, compatibility, and testing.

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📖 How This Project Started

Back in 2024, I started this project without knowing how I could make the idea possible.

My original goal was to create an optimization mod capable of adapting to Minecraft instead of simply containing another list of predefined tweaks.

At the time, my development knowledge was extremely limited and AI development tools were much less capable.

The project was rejected and I was told that I needed more development experience.

Instead of abandoning the idea, I kept learning.

On October 11, 2025, I decided to try again using newer AI tools and the Java knowledge I had gained.

That eventually helped me move away from MCreator, migrate the project to IntelliJ IDEA, understand the structure of a real Forge project, and continue rebuilding Adaptive Optimization into what it is today.

I still consider myself a beginner.

AI helps enormously with development, but I also continuously test, review, redesign, and learn from what goes wrong.

Adaptive Optimization exists because I continued working on an idea that initially seemed far beyond what I knew how to build.

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🧠 In Summary

Adaptive Optimization is no longer just a prototype attempting to dynamically optimize Minecraft.

It is now officially in its Stable phase and its core optimization, observation, profiling, and measurement systems are working in real Minecraft environments.

Development is now moving toward the next goal:

making those systems increasingly autonomous.

Adaptive Optimization is being built to:

observe Minecraft, understand its workloads, discover optimization opportunities, verify whether they actually work, reject unsafe or useless changes, remember successful strategies, and become better at optimization over time.

This is not the end of Adaptive Optimization.

Stable is the foundation for what comes next.