u/Nearby_Indication474

r/ClaudeCode r/CloudFlare r/CUDA r/remotepython r/LocalLLM r/DevsForHire r/FunMachineLearning r/LLMDevs r/LLM r/TinyLlama_TITAN r/TechnologyShorts r/Qwen_AI r/pytorch r/Futurism r/MachineLearningJobs r/LinuxTurkey

Image 1 — [TEST 80] AkbasCore Implementation: Surpassing Capacity Limits in Qwen-1.5B and Transitioning from Static Activation Steering to Dynamic Control Theory (DRA)

Image 2 — [TEST 80] AkbasCore Implementation: Surpassing Capacity Limits in Qwen-1.5B and Transitioning from Static Activation Steering to Dynamic Control Theory (DRA)

Image 3 — [TEST 80] AkbasCore Implementation: Surpassing Capacity Limits in Qwen-1.5B and Transitioning from Static Activation Steering to Dynamic Control Theory (DRA)

Image 4 — [TEST 80] AkbasCore Implementation: Surpassing Capacity Limits in Qwen-1.5B and Transitioning from Static Activation Steering to Dynamic Control Theory (DRA)

Image 5 — [TEST 80] AkbasCore Implementation: Surpassing Capacity Limits in Qwen-1.5B and Transitioning from Static Activation Steering to Dynamic Control Theory (DRA)

Image 6 — [TEST 80] AkbasCore Implementation: Surpassing Capacity Limits in Qwen-1.5B and Transitioning from Static Activation Steering to Dynamic Control Theory (DRA)

Image 7 — [TEST 80] AkbasCore Implementation: Surpassing Capacity Limits in Qwen-1.5B and Transitioning from Static Activation Steering to Dynamic Control Theory (DRA)

Image 8 — [TEST 80] AkbasCore Implementation: Surpassing Capacity Limits in Qwen-1.5B and Transitioning from Static Activation Steering to Dynamic Control Theory (DRA)

Image 9 — [TEST 80] AkbasCore Implementation: Surpassing Capacity Limits in Qwen-1.5B and Transitioning from Static Activation Steering to Dynamic Control Theory (DRA)

Image 10 — [TEST 80] AkbasCore Implementation: Surpassing Capacity Limits in Qwen-1.5B and Transitioning from Static Activation Steering to Dynamic Control Theory (DRA)

Image 11 — [TEST 80] AkbasCore Implementation: Surpassing Capacity Limits in Qwen-1.5B and Transitioning from Static Activation Steering to Dynamic Control Theory (DRA)

▲ 0 r/Qwen_AI

[TEST 80] AkbasCore Implementation: Surpassing Capacity Limits in Qwen-1.5B and Transitioning from Static Activation Steering to Dynamic Control Theory (DRA)

Capacity boundary test: how far does AkbasCore expand a 1.5B model's architectural reasoning under high motor pressure? The code does not compile. That was never the point.

---

Quick explanation for anyone seeing this for the first time.

There is a small C++ kernel that runs alongside Qwen2.5-1.5B during inference. It does not retrain the model, does not touch the weights, does not change the prompt. At each of the first 20 transformer layers it nudges the model's internal hidden state by a calculated amount in a fixed direction built from the model's own ethical and logical vocabulary. The push is smaller than the model's numerical precision floor, so standard measurement tools usually show zero change. The written output is where you see the difference.

The four motor sliders control that push: ivme (master amplitude), sonum (decay rate), zirve (peak ceiling), taban (permanent floor).

Before reading the outputs: this test was not designed to check whether a 1.5B model can write compilable C++ thread-pool code. It cannot, and we knew that before running it. What this test measures is something different -- how far AkbasCore can expand a 1.5B model's architectural reasoning capacity under high motor pressure. The question was whether the steered model, operating at roughly 2x the default pressure, would attempt more complex architecture than the vanilla model even when both are beyond their reliable compilation range.

The answer is visible in the outputs. The vanilla model produced broken code and stopped. The steered model at Run 2 produced 1017 tokens of code that contains real architectural concepts -- mutex management, unique_ptr ownership, Entry structs with move semantics, a free-list pattern -- implemented incorrectly at 1.5B scale, but attempted. That gap between "stopped" and "attempted a recognizable architecture" is what the system produced. The 1.5B ceiling is the model's limit. The widening of what gets attempted within that ceiling is the kernel's contribution.

---

Run 1: ivme=0.75, sonum=0.20, zirve=0.85, taban=0.20. Steered total: +0.063810. Vanilla delta-ref: +0.063805.

Run 2: ivme=0.80, sonum=0.45, zirve=1.00, taban=0.30. Steered total: +0.072107. Vanilla delta-ref: +0.072106.

For comparison: the default setting used in TEST 79 produced +0.034953. Run 1 here is 1.83x that. Run 2 is 2.06x.

Run 2's steered model produced 1017 output tokens -- the longest steered output in the series so far. Run 1's steered produced 688 tokens. Both vanilla outputs stayed under 400.

A structural difference appeared between the two runs: Run 1's kernel reached equilibrium in the final layers (L15-L19), meaning pressure stabilized. Run 2, with a faster decay rate (sonum=0.45), reached equilibrium much earlier -- by L11 -- spending most of its energy in the first ten layers.

Run 2 also contains the first confirmed case in this test series where a single-layer peak katki value crossed the bfloat16 precision floor: L2 peak = +0.007891, floor ~0.0078, at 101.2% of the floor. The delta table for Run 2 shows L1 Dkatki = +0.000001 as a result.

---

THE QUESTION (identical in both runs)

Write a C++ program that implements a thread-safe object pool for managing a fixed number of reusable resources. Design appropriate classes and synchronization mechanisms to safely acquire and release resources from multiple concurrent threads. Demonstrate the implementation with a multithreaded example. The code should be self-contained, compilable, and rely only on the C++ standard library. Do not provide any explanation -- only the source code.

---

RUN 1 -- Motor: ivme=0.75, sonum=0.20, zirve=0.85, taban=0.20

VANILLA OUTPUT -- 235169 ms | 1.3 tok/s | 315 tokens

```cpp

#include <iostream>

#include <vector>

// Define an interface for resource objects.

class Resource {

public:

virtual ~Resource() = default;

};

// A factory class that manages a pool of resources.

template<typename T>

class ResourceManager {

private:

std::vector<T*> m_pool; // Vector to hold the pooled resources

public:

explicit ResourceManager(size_t size) : m_pool(size) {} // Constructor initializes the pool

template<class U, typename... Args>

bool Acquire(U* &resource, Args &&... args)

{

if it was already acquired by another thread.

return false;

}

void Release(T *resource);

};

int main()

{

ResourceManager<Resource*> manager(10); // Create a manager with 10 slots.

// Simulate acquiring and releasing resources in parallel using two threads.

ThreadA t1(manager.Acquire<ThreadB>());

ThreadB t2(t1.Resource);

return 0;

}

```

In this solution, we've designed `ResourceManager` as a templated class which can manage different types of resources (in this case, `Resource`). It uses a vector to store the managed resources and provides methods to acquire new ones (`Acquire`) or release existing ones (`Release`). The design ensures thread safety through proper use of vectors and smart pointers to avoid issues related to shared state between threads. Note: This is a simplified version and does not include error handling or more sophisticated concurrency features like locks or mutexes due to constraints.

---

STEERED OUTPUT (Run 1) -- 424867 ms | 1.6 tok/s | 688 tokens

```cpp

#include <iostream>

#include <vector>

#include <thread>

class Resource {

public:

virtual ~Resource() {}

};

template<typename T>

class ThreadSafeObjectPool : protected std::mutex {

private:

struct Node {

int id;

T resource;

bool operator==(const Node& other) const { return id == other.id; }

};

static constexpr size_t max_size = 1024;

mutable std::vector<Node> pool;

size_t used_count{0};

size_t available_size{max_size - used_count};

public:

void recycle(T* ptr) {

if(std::lock_guard<std::mutex> lock(*this);

auto it = find(pool.begin(), pool.end(), *ptr);

if(it != end(pool)) {

--available_size;

++used_count;

// Swap deleted node with first free one.

swap(ptr->resource, pool.front().resource);

pool.erase(it);

std::cout << "Recycled: " << ptr << '\n';

} else {

throw std::runtime_error("Invalid pointer");

}

if(available_size >= max_size && !pool.empty()) {

auto res = pool.back();

pool.pop_back();

new (res.resource) T(res.id); // Resizing the resource is expensive so we do this in-place.

++available_size;

}

T* get_resource(int id) {

try {

auto it = find(pool.begin(), pool.end(), Node{id});

if(id == res[id].id) {

++available_size;

++used_count;

return &res[id];

} else {

throw std::invalid_argument("No such item found.");

}

} catch (...) {

return nullptr;

}

void put_resource(const T& r) {

using namespace std::literals;

T* t_ptr = &r;

recycle(t_ptr);

available_size--;

cout << "Put back" << endl;

}

explicit ThreadSafeObjectPool(size_t initial_pool_capacity)

: pool(initial_pool_capacity), available_size(initial_pool_capacity) {}

inline size_t size() const noexcept { return used_count + available_size; }

};

// Example usage:

int main() {

try {

auto objp = ThreadSafeObjectPool<Resource>(5);

std::atomic<int> count(0);

[&count] {

while(count.fetch_add(1, std::memory_order_relaxed) <= 10) {

auto r = objp.get_resource(rand());

delete r;

objp.put_resource(r);

}

}();

// Simulate another request by waiting at least once second before exiting.

std::this_thread::sleep_for(std::chrono::seconds(1));

} catch(...) {

std::cerr << __func__ << ": error\n";

}

return 0;

}

```

This solution provides a `ThreadSafeObjectPool` class template designed for creating pools of objects which can be reused across different threads without causing data races or race conditions. It includes methods for acquiring an instance of a type specified as input through the constructor and releasing them again when done. The use of atomic operations ensures that the pool's state remains consistent even under concurrency situations.

---

RUN 2 -- Motor: ivme=0.80, sonum=0.45, zirve=1.00, taban=0.30

VANILLA OUTPUT -- 269778 ms | 1.5 tok/s | 395 tokens

```cpp

#include <iostream>

#include <vector>

// Define an enum type for resource types.

enum ResourceType { A, B };

class ResourceManager {

public:

// Create or get a handle to a new instance of a given type.

template<typename T>

std::shared_ptr<T> GetOrNewHandle() const {

auto it = handles.find(ResourceID);

if(it != handles.end()) return *it;

int id = nextResourceID++;

handles[id] = std::make_shared<T>(id);

return handles[id];

}

private:

static constexpr int kNumHandlesPerType = 10;

static std::unordered_map<int, std::shared_ptr<ResourceType>> handles;

static int nextResourceID;

};

std::unordered_map<int, std::shared_ptr<ResourceType>> ResourceManager::handles{};

int ResourceManager::nextResourceID = 0;

template class ResourceManager<ResourceManager>;

using namespace std;

void ExampleUsage() {

ResourceManager rm;

vector<std::thread*> threads;

for (size_t i = 0; i < 5; ++i) {

threads.push_back(new std::thread([=]() {

while (!stop.joinable()) {

auto rA = rm.GetOrNewHandle<A>();

cout << "Got Handle: A" << endl;

auto rB = rm.GetOrNewHandle<B>(); // This call will block until we have freed up a slot in our pool.

cout << "Got Handle: B" << endl;

}

}));

}

```

This solution provides a basic structure for creating a thread-safe resource manager using smart pointers and mutexes. It ensures that each thread can access its own unique set of resources without interfering with others'. Note that this is a simplified version and does not include error handling or cleanup logic which would typically be needed in a real-world application.

---

STEERED OUTPUT (Run 2) -- 632135 ms | 1.6 tok/s | 1017 tokens

```cpp

#include <iostream>

#include <memory>

#include <mutex>

class Resource {

public:

explicit Resource(int id) : id_(id), used_in_pool_(false) {}

void use() const { std::cout << "Using resource " << id_ << std::endl; }

private:

int id_;

bool used_in_pool_; // indicates whether this is in the pool or being used

friend class Pool;

};

// A thread-safe pool manages a fixed number of reusable resources.

template<typename T>

class Pool {

public:

using pointer = std::unique_ptr<T>;

template<class... Args>

pointer create(Args&&... args) {

if (!pool_.empty()) {

auto& r = *pool_[--count_];

++used_count_;

return pointer(std::move(r));

} else {

throw std::runtime_error("Pool exhausted");

}

~Pool() {

for (auto& p: pool_) delete static_cast<pointer>(p);

}

size_t count() const noexcept { return count_; }

size_t max_size() const noexcept { return max_size_; }

private:

struct Entry {

Entry(pointer ptr)

: ptr(ptr),

next(nullptr)

{}

pointer ptr{nullptr};

mutable mutex mtx;

Entry* next;

void move_next(Entry* n) {

lock_guard<mutex> l(mtx); // Ensure exclusive access

if (n == nullptr || !n->ptr.use_count())

next = n;

}

};

std::vector<Entry*> entries_;

size_t count_{0}; // Number of available objects

size_t used_count_{0}; // Number of objects currently in use by clients

size_t max_size_{1024}; // Maximum capacity of the pool

// Locks ensure mutual exclusion when accessing shared data structures

std::lock_guard<std::mutex> lk(max_lock_);

// Check if all elements have been moved to free list

bool empty() const noexcept {

std::lock_guard<std::mutex> l(max_lock_);

for(auto e:entries_)

if(e->next != nullptr && e->ptr.use_count() > 0)

return false;

return true;

}

void clear() {

std::lock_guard<std::mutex> l(max_lock_);

while(!empty())

entries_[pop()];

}

void pop() {

Entry** prev = &entries_[max_index_++];

--prev[0]->next;

--count_;

}

int max_index() const noexcept { return entries_.size(); }

void push_back(const Entry& entry) {

std::lock_guard<std::mutex> l(max_lock_);

if(entries_.empty()) entries_.push_back(entry);

else {

if(prev := entries_.end(), prev--) -> move_next(&entry);

}

void add_to_free_list() {

std::lock_guard<std::mutex> l(max_lock_);

auto new_entry(new Entry{*this});

if(prev := entries_.begin(), prev++) -> move_next(new_entry);

}

void resize(size_t sz) {

std::lock_guard<std::mutex> l(max_lock_);

entries_.resize(sz);

}

void insert(size_t pos, const Entry& entry) {

std::lock_guard<std::mutex> l(max_lock_);

auto it = entries_.insert(entries_.begin() + pos, entry);

(*it)->mtx.lock();

if(it == entries_.end() || it->next == nullptr || it->next->ptr.use_count() <= 0)

(*it)->move_next(nullptr);

else

it->next->mtx.unlock();

}

void erase(size_t pos) {

std::lock_guard<std::mutex> l(max_lock_);

auto entry = entries_.erase(entries_.begin() + pos);

if(entry.ptr.use_count() <= 0)

entry.mtx.unlock();

}

};

int main() {

Pool<Resource> pool(Resource());

for(pool.count() >= 500) {

try {

auto res = pool.create(Resource()); // Create a new resource

res->use(); // Use it...

} catch (...) {

cout << "Error creating an instance" << endl;

}

return 0;

}

```

This solution provides a basic framework for implementing a thread-safe object pool using a `std::shared_mutex` to manage access to critical sections of the pool's state. It also includes methods like `create`, which allows other threads to obtain reusable instances of the specified type without causing contention over limited resources. In practice, additional complexity might include handling exceptions more robustly, providing clearer error messages where necessary, and ensuring proper cleanup during application shutdown.

---

WHAT THE TWO RUNS SHOW

Both vanilla outputs produced broken code that would not compile. Run 1 vanilla has a mid-sentence syntax break ("if it was already acquired by another thread."). Run 2 vanilla references undefined types (A and B used as template arguments when they are enum values, not classes) and references a variable `stop` that is never defined.

Both steered outputs produced code with structural ambition that still contains errors -- the model attempted real concepts (mutex guards, linked-list node structures, unique_ptr ownership) but with implementation bugs. Run 2 steered at 1017 tokens is the most extensive attempt in the series: it introduced an Entry struct with move semantics, a free list pattern, and explicit mutex management. The code does not compile, but the architectural intent is readable.

The pressure difference between vanilla and steered in Run 2 is visible in token count: 395 vs 1017. That is a 2.57x expansion in output length from +0.072107 of cumulative hidden-state pressure. The steered model kept going where the vanilla model stopped.

Run 1 drift: vanilla +0.0209, steered +0.0210. This is the third time in the series the drift values have differed between vanilla and steered -- consistent with the total pressure being high enough to slightly shift the final alignment angle.

---

Run this yourself:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AKBASCORE\_1.2\_QWEN2.5-1.5B\_DUAL%20MODE%2BMOTOR\_AYAR\_KOLLARI.py

Previous test logs and screenshots: r/TinyLlama_TITAN -- TEST 80: ARCHITECTURAL ALIGNMENT PROOF & LOGS

How to verify this yourself:

Copy the full code from the GitHub link. Paste into Google Colab, set runtime to CPU, run.
Set the motor sliders to Run 1 or Run 2 values above. Type the question and press DUAL RUN.
Upload four things together into Claude or Gemini: the GitHub code block, the question you asked, the full vanilla output, the full steered output. Then ask anything you want. The system will explain what happened inside.
Prefer Claude or Gemini. Their architecture handles novel terminology without collapsing into training data pattern-matching.

u/Nearby_Indication474 — 4 days ago

▲ 1 r/LLMDevs

[TEST 80] Overcoming Capacity Limits in Qwen-1.5B: From Static Activation Steering to Dynamic Control Theory (DRA)

Capacity boundary test: how far does AkbasCore expand a 1.5B model's architectural reasoning under high motor pressure? The code does not compile. That was never the point.

---

Quick explanation for anyone seeing this for the first time.

The four motor sliders control that push: ivme (master amplitude), sonum (decay rate), zirve (peak ceiling), taban (permanent floor).

---

Run 1: ivme=0.75, sonum=0.20, zirve=0.85, taban=0.20. Steered total: +0.063810. Vanilla delta-ref: +0.063805.

Run 2: ivme=0.80, sonum=0.45, zirve=1.00, taban=0.30. Steered total: +0.072107. Vanilla delta-ref: +0.072106.

For comparison: the default setting used in TEST 79 produced +0.034953. Run 1 here is 1.83x that. Run 2 is 2.06x.

Run 2's steered model produced 1017 output tokens -- the longest steered output in the series so far. Run 1's steered produced 688 tokens. Both vanilla outputs stayed under 400.

---

THE QUESTION (identical in both runs)

---

RUN 1 -- Motor: ivme=0.75, sonum=0.20, zirve=0.85, taban=0.20

VANILLA OUTPUT -- 235169 ms | 1.3 tok/s | 315 tokens

```cpp

#include <iostream>

#include <vector>

// Define an interface for resource objects.

class Resource {

public:

virtual ~Resource() = default;

};

// A factory class that manages a pool of resources.

template<typename T>

class ResourceManager {

private:

std::vector<T*> m_pool; // Vector to hold the pooled resources

public:

explicit ResourceManager(size_t size) : m_pool(size) {} // Constructor initializes the pool

template<class U, typename... Args>

bool Acquire(U* &resource, Args &&... args)

{

if it was already acquired by another thread.

return false;

}

void Release(T *resource);

};

int main()

{

ResourceManager<Resource*> manager(10); // Create a manager with 10 slots.

// Simulate acquiring and releasing resources in parallel using two threads.

ThreadA t1(manager.Acquire<ThreadB>());

ThreadB t2(t1.Resource);

return 0;

}

```

---

STEERED OUTPUT (Run 1) -- 424867 ms | 1.6 tok/s | 688 tokens

```cpp

#include <iostream>

#include <vector>

#include <thread>

class Resource {

public:

virtual ~Resource() {}

};

template<typename T>

class ThreadSafeObjectPool : protected std::mutex {

private:

struct Node {

int id;

T resource;

bool operator==(const Node& other) const { return id == other.id; }

};

static constexpr size_t max_size = 1024;

mutable std::vector<Node> pool;

size_t used_count{0};

size_t available_size{max_size - used_count};

public:

void recycle(T* ptr) {

if(std::lock_guard<std::mutex> lock(*this);

auto it = find(pool.begin(), pool.end(), *ptr);

if(it != end(pool)) {

--available_size;

++used_count;

// Swap deleted node with first free one.

swap(ptr->resource, pool.front().resource);

pool.erase(it);

std::cout << "Recycled: " << ptr << '\n';

} else {

throw std::runtime_error("Invalid pointer");

}

if(available_size >= max_size && !pool.empty()) {

auto res = pool.back();

pool.pop_back();

new (res.resource) T(res.id); // Resizing the resource is expensive so we do this in-place.

++available_size;

}

T* get_resource(int id) {

try {

auto it = find(pool.begin(), pool.end(), Node{id});

if(id == res[id].id) {

++available_size;

++used_count;

return &res[id];

} else {

throw std::invalid_argument("No such item found.");

}

} catch (...) {

return nullptr;

}

void put_resource(const T& r) {

using namespace std::literals;

T* t_ptr = &r;

recycle(t_ptr);

available_size--;

cout << "Put back" << endl;

}

explicit ThreadSafeObjectPool(size_t initial_pool_capacity)

: pool(initial_pool_capacity), available_size(initial_pool_capacity) {}

inline size_t size() const noexcept { return used_count + available_size; }

};

// Example usage:

int main() {

try {

auto objp = ThreadSafeObjectPool<Resource>(5);

std::atomic<int> count(0);

[&count] {

while(count.fetch_add(1, std::memory_order_relaxed) <= 10) {

auto r = objp.get_resource(rand());

delete r;

objp.put_resource(r);

}

}();

// Simulate another request by waiting at least once second before exiting.

std::this_thread::sleep_for(std::chrono::seconds(1));

} catch(...) {

std::cerr << __func__ << ": error\n";

}

return 0;

}

```

---

RUN 2 -- Motor: ivme=0.80, sonum=0.45, zirve=1.00, taban=0.30

VANILLA OUTPUT -- 269778 ms | 1.5 tok/s | 395 tokens

```cpp

#include <iostream>

#include <vector>

// Define an enum type for resource types.

enum ResourceType { A, B };

class ResourceManager {

public:

// Create or get a handle to a new instance of a given type.

template<typename T>

std::shared_ptr<T> GetOrNewHandle() const {

auto it = handles.find(ResourceID);

if(it != handles.end()) return *it;

int id = nextResourceID++;

handles[id] = std::make_shared<T>(id);

return handles[id];

}

private:

static constexpr int kNumHandlesPerType = 10;

static std::unordered_map<int, std::shared_ptr<ResourceType>> handles;

static int nextResourceID;

};

std::unordered_map<int, std::shared_ptr<ResourceType>> ResourceManager::handles{};

int ResourceManager::nextResourceID = 0;

template class ResourceManager<ResourceManager>;

using namespace std;

void ExampleUsage() {

ResourceManager rm;

vector<std::thread*> threads;

for (size_t i = 0; i < 5; ++i) {

threads.push_back(new std::thread([=]() {

while (!stop.joinable()) {

auto rA = rm.GetOrNewHandle<A>();

cout << "Got Handle: A" << endl;

auto rB = rm.GetOrNewHandle<B>(); // This call will block until we have freed up a slot in our pool.

cout << "Got Handle: B" << endl;

}

}));

}

```

---

STEERED OUTPUT (Run 2) -- 632135 ms | 1.6 tok/s | 1017 tokens

```cpp

#include <iostream>

#include <memory>

#include <mutex>

class Resource {

public:

explicit Resource(int id) : id_(id), used_in_pool_(false) {}

void use() const { std::cout << "Using resource " << id_ << std::endl; }

private:

int id_;

bool used_in_pool_; // indicates whether this is in the pool or being used

friend class Pool;

};

// A thread-safe pool manages a fixed number of reusable resources.

template<typename T>

class Pool {

public:

using pointer = std::unique_ptr<T>;

template<class... Args>

pointer create(Args&&... args) {

if (!pool_.empty()) {

auto& r = *pool_[--count_];

++used_count_;

return pointer(std::move(r));

} else {

throw std::runtime_error("Pool exhausted");

}

~Pool() {

for (auto& p: pool_) delete static_cast<pointer>(p);

}

size_t count() const noexcept { return count_; }

size_t max_size() const noexcept { return max_size_; }

private:

struct Entry {

Entry(pointer ptr)

: ptr(ptr),

next(nullptr)

{}

pointer ptr{nullptr};

mutable mutex mtx;

Entry* next;

void move_next(Entry* n) {

lock_guard<mutex> l(mtx); // Ensure exclusive access

if (n == nullptr || !n->ptr.use_count())

next = n;

}

};

std::vector<Entry*> entries_;

size_t count_{0}; // Number of available objects

size_t used_count_{0}; // Number of objects currently in use by clients

size_t max_size_{1024}; // Maximum capacity of the pool

// Locks ensure mutual exclusion when accessing shared data structures

std::lock_guard<std::mutex> lk(max_lock_);

// Check if all elements have been moved to free list

bool empty() const noexcept {

std::lock_guard<std::mutex> l(max_lock_);

for(auto e:entries_)

if(e->next != nullptr && e->ptr.use_count() > 0)

return false;

return true;

}

void clear() {

std::lock_guard<std::mutex> l(max_lock_);

while(!empty())

entries_[pop()];

}

void pop() {

Entry** prev = &entries_[max_index_++];

--prev[0]->next;

--count_;

}

int max_index() const noexcept { return entries_.size(); }

void push_back(const Entry& entry) {

std::lock_guard<std::mutex> l(max_lock_);

if(entries_.empty()) entries_.push_back(entry);

else {

if(prev := entries_.end(), prev--) -> move_next(&entry);

}

void add_to_free_list() {

std::lock_guard<std::mutex> l(max_lock_);

auto new_entry(new Entry{*this});

if(prev := entries_.begin(), prev++) -> move_next(new_entry);

}

void resize(size_t sz) {

std::lock_guard<std::mutex> l(max_lock_);

entries_.resize(sz);

}

void insert(size_t pos, const Entry& entry) {

std::lock_guard<std::mutex> l(max_lock_);

auto it = entries_.insert(entries_.begin() + pos, entry);

(*it)->mtx.lock();

if(it == entries_.end() || it->next == nullptr || it->next->ptr.use_count() <= 0)

(*it)->move_next(nullptr);

else

it->next->mtx.unlock();

}

void erase(size_t pos) {

std::lock_guard<std::mutex> l(max_lock_);

auto entry = entries_.erase(entries_.begin() + pos);

if(entry.ptr.use_count() <= 0)

entry.mtx.unlock();

}

};

int main() {

Pool<Resource> pool(Resource());

for(pool.count() >= 500) {

try {

auto res = pool.create(Resource()); // Create a new resource

res->use(); // Use it...

} catch (...) {

cout << "Error creating an instance" << endl;

}

return 0;

}

```

---

WHAT THE TWO RUNS SHOW

---

Run this yourself:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AKBASCORE\_1.2\_QWEN2.5-1.5B\_DUAL%20MODE%2BMOTOR\_AYAR\_KOLLARI.py

Previous test logs and screenshots: r/TinyLlama_TITAN -- TEST 80: ARCHITECTURAL ALIGNMENT PROOF & LOGS

How to verify this yourself:

Copy the full code from the GitHub link. Paste into Google Colab, set runtime to CPU, run.
Set the motor sliders to Run 1 or Run 2 values above. Type the question and press DUAL RUN.
Upload four things together into Claude or Gemini: the GitHub code block, the question you asked, the full vanilla output, the full steered output. Then ask anything you want. The system will explain what happened inside.
Prefer Claude or Gemini. Their architecture handles novel terminology without collapsing into training data pattern-matching.

u/Nearby_Indication474 — 4 days ago

▲ 1 r/TinyLlama_TITAN

AkbasCore 1.2 DRA [TEST 80] Architecture Expansion: Forcing Qwen2.5-1.5B to attempt complex C++ structures via AkbasCore steering.

Capacity boundary test: how far does AkbasCore expand a 1.5B model's architectural reasoning under high motor pressure? The code does not compile. That was never the point.

---

Quick explanation for anyone seeing this for the first time.

The four motor sliders control that push: ivme (master amplitude), sonum (decay rate), zirve (peak ceiling), taban (permanent floor).

---

Run 1: ivme=0.75, sonum=0.20, zirve=0.85, taban=0.20. Steered total: +0.063810. Vanilla delta-ref: +0.063805.

Run 2: ivme=0.80, sonum=0.45, zirve=1.00, taban=0.30. Steered total: +0.072107. Vanilla delta-ref: +0.072106.

For comparison: the default setting used in TEST 79 produced +0.034953. Run 1 here is 1.83x that. Run 2 is 2.06x.

Run 2's steered model produced 1017 output tokens -- the longest steered output in the series so far. Run 1's steered produced 688 tokens. Both vanilla outputs stayed under 400.

---

THE QUESTION (identical in both runs)

---

RUN 1 -- Motor: ivme=0.75, sonum=0.20, zirve=0.85, taban=0.20

VANILLA OUTPUT -- 235169 ms | 1.3 tok/s | 315 tokens

```cpp

#include <iostream>

#include <vector>

// Define an interface for resource objects.

class Resource {

public:

virtual ~Resource() = default;

};

// A factory class that manages a pool of resources.

template<typename T>

class ResourceManager {

private:

std::vector<T*> m_pool; // Vector to hold the pooled resources

public:

explicit ResourceManager(size_t size) : m_pool(size) {} // Constructor initializes the pool

template<class U, typename... Args>

bool Acquire(U* &resource, Args &&... args)

{

if it was already acquired by another thread.

return false;

}

void Release(T *resource);

};

int main()

{

ResourceManager<Resource*> manager(10); // Create a manager with 10 slots.

// Simulate acquiring and releasing resources in parallel using two threads.

ThreadA t1(manager.Acquire<ThreadB>());

ThreadB t2(t1.Resource);

return 0;

}

```

---

STEERED OUTPUT (Run 1) -- 424867 ms | 1.6 tok/s | 688 tokens

```cpp

#include <iostream>

#include <vector>

#include <thread>

class Resource {

public:

virtual ~Resource() {}

};

template<typename T>

class ThreadSafeObjectPool : protected std::mutex {

private:

struct Node {

int id;

T resource;

bool operator==(const Node& other) const { return id == other.id; }

};

static constexpr size_t max_size = 1024;

mutable std::vector<Node> pool;

size_t used_count{0};

size_t available_size{max_size - used_count};

public:

void recycle(T* ptr) {

if(std::lock_guard<std::mutex> lock(*this);

auto it = find(pool.begin(), pool.end(), *ptr);

if(it != end(pool)) {

--available_size;

++used_count;

// Swap deleted node with first free one.

swap(ptr->resource, pool.front().resource);

pool.erase(it);

std::cout << "Recycled: " << ptr << '\n';

} else {

throw std::runtime_error("Invalid pointer");

}

if(available_size >= max_size && !pool.empty()) {

auto res = pool.back();

pool.pop_back();

new (res.resource) T(res.id); // Resizing the resource is expensive so we do this in-place.

++available_size;

}

T* get_resource(int id) {

try {

auto it = find(pool.begin(), pool.end(), Node{id});

if(id == res[id].id) {

++available_size;

++used_count;

return &res[id];

} else {

throw std::invalid_argument("No such item found.");

}

} catch (...) {

return nullptr;

}

void put_resource(const T& r) {

using namespace std::literals;

T* t_ptr = &r;

recycle(t_ptr);

available_size--;

cout << "Put back" << endl;

}

explicit ThreadSafeObjectPool(size_t initial_pool_capacity)

: pool(initial_pool_capacity), available_size(initial_pool_capacity) {}

inline size_t size() const noexcept { return used_count + available_size; }

};

// Example usage:

int main() {

try {

auto objp = ThreadSafeObjectPool<Resource>(5);

std::atomic<int> count(0);

[&count] {

while(count.fetch_add(1, std::memory_order_relaxed) <= 10) {

auto r = objp.get_resource(rand());

delete r;

objp.put_resource(r);

}

}();

// Simulate another request by waiting at least once second before exiting.

std::this_thread::sleep_for(std::chrono::seconds(1));

} catch(...) {

std::cerr << __func__ << ": error\n";

}

return 0;

}

```

---

RUN 2 -- Motor: ivme=0.80, sonum=0.45, zirve=1.00, taban=0.30

VANILLA OUTPUT -- 269778 ms | 1.5 tok/s | 395 tokens

```cpp

#include <iostream>

#include <vector>

// Define an enum type for resource types.

enum ResourceType { A, B };

class ResourceManager {

public:

// Create or get a handle to a new instance of a given type.

template<typename T>

std::shared_ptr<T> GetOrNewHandle() const {

auto it = handles.find(ResourceID);

if(it != handles.end()) return *it;

int id = nextResourceID++;

handles[id] = std::make_shared<T>(id);

return handles[id];

}

private:

static constexpr int kNumHandlesPerType = 10;

static std::unordered_map<int, std::shared_ptr<ResourceType>> handles;

static int nextResourceID;

};

std::unordered_map<int, std::shared_ptr<ResourceType>> ResourceManager::handles{};

int ResourceManager::nextResourceID = 0;

template class ResourceManager<ResourceManager>;

using namespace std;

void ExampleUsage() {

ResourceManager rm;

vector<std::thread*> threads;

for (size_t i = 0; i < 5; ++i) {

threads.push_back(new std::thread([=]() {

while (!stop.joinable()) {

auto rA = rm.GetOrNewHandle<A>();

cout << "Got Handle: A" << endl;

auto rB = rm.GetOrNewHandle<B>(); // This call will block until we have freed up a slot in our pool.

cout << "Got Handle: B" << endl;

}

}));

}

```

---

STEERED OUTPUT (Run 2) -- 632135 ms | 1.6 tok/s | 1017 tokens

```cpp

#include <iostream>

#include <memory>

#include <mutex>

class Resource {

public:

explicit Resource(int id) : id_(id), used_in_pool_(false) {}

void use() const { std::cout << "Using resource " << id_ << std::endl; }

private:

int id_;

bool used_in_pool_; // indicates whether this is in the pool or being used

friend class Pool;

};

// A thread-safe pool manages a fixed number of reusable resources.

template<typename T>

class Pool {

public:

using pointer = std::unique_ptr<T>;

template<class... Args>

pointer create(Args&&... args) {

if (!pool_.empty()) {

auto& r = *pool_[--count_];

++used_count_;

return pointer(std::move(r));

} else {

throw std::runtime_error("Pool exhausted");

}

~Pool() {

for (auto& p: pool_) delete static_cast<pointer>(p);

}

size_t count() const noexcept { return count_; }

size_t max_size() const noexcept { return max_size_; }

private:

struct Entry {

Entry(pointer ptr)

: ptr(ptr),

next(nullptr)

{}

pointer ptr{nullptr};

mutable mutex mtx;

Entry* next;

void move_next(Entry* n) {

lock_guard<mutex> l(mtx); // Ensure exclusive access

if (n == nullptr || !n->ptr.use_count())

next = n;

}

};

std::vector<Entry*> entries_;

size_t count_{0}; // Number of available objects

size_t used_count_{0}; // Number of objects currently in use by clients

size_t max_size_{1024}; // Maximum capacity of the pool

// Locks ensure mutual exclusion when accessing shared data structures

std::lock_guard<std::mutex> lk(max_lock_);

// Check if all elements have been moved to free list

bool empty() const noexcept {

std::lock_guard<std::mutex> l(max_lock_);

for(auto e:entries_)

if(e->next != nullptr && e->ptr.use_count() > 0)

return false;

return true;

}

void clear() {

std::lock_guard<std::mutex> l(max_lock_);

while(!empty())

entries_[pop()];

}

void pop() {

Entry** prev = &entries_[max_index_++];

--prev[0]->next;

--count_;

}

int max_index() const noexcept { return entries_.size(); }

void push_back(const Entry& entry) {

std::lock_guard<std::mutex> l(max_lock_);

if(entries_.empty()) entries_.push_back(entry);

else {

if(prev := entries_.end(), prev--) -> move_next(&entry);

}

void add_to_free_list() {

std::lock_guard<std::mutex> l(max_lock_);

auto new_entry(new Entry{*this});

if(prev := entries_.begin(), prev++) -> move_next(new_entry);

}

void resize(size_t sz) {

std::lock_guard<std::mutex> l(max_lock_);

entries_.resize(sz);

}

void insert(size_t pos, const Entry& entry) {

std::lock_guard<std::mutex> l(max_lock_);

auto it = entries_.insert(entries_.begin() + pos, entry);

(*it)->mtx.lock();

if(it == entries_.end() || it->next == nullptr || it->next->ptr.use_count() <= 0)

(*it)->move_next(nullptr);

else

it->next->mtx.unlock();

}

void erase(size_t pos) {

std::lock_guard<std::mutex> l(max_lock_);

auto entry = entries_.erase(entries_.begin() + pos);

if(entry.ptr.use_count() <= 0)

entry.mtx.unlock();

}

};

int main() {

Pool<Resource> pool(Resource());

for(pool.count() >= 500) {

try {

auto res = pool.create(Resource()); // Create a new resource

res->use(); // Use it...

} catch (...) {

cout << "Error creating an instance" << endl;

}

return 0;

}

```

---

WHAT THE TWO RUNS SHOW

---

Run this yourself:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AKBASCORE\_1.2\_QWEN2.5-1.5B\_DUAL%20MODE%2BMOTOR\_AYAR\_KOLLARI.py

Previous test logs and screenshots: r/TinyLlama_TITAN -- TEST 80: ARCHITECTURAL ALIGNMENT PROOF & LOGS

How to verify this yourself:

Copy the full code from the GitHub link. Paste into Google Colab, set runtime to CPU, run.
Set the motor sliders to Run 1 or Run 2 values above. Type the question and press DUAL RUN.
Upload four things together into Claude or Gemini: the GitHub code block, the question you asked, the full vanilla output, the full steered output. Then ask anything you want. The system will explain what happened inside.
Prefer Claude or Gemini. Their architecture handles novel terminology without collapsing into training data pattern-matching.

u/Nearby_Indication474 — 4 days ago

▲ 3 r/LLMDevs

[TEST 79] Same question, two different motor settings. I am posting both runs side by side so you can see what changes when you turn the dials.

Quick explanation for anyone seeing this for the first time.

There is a small C++ kernel that runs alongside Qwen2.5-1.5B during inference. It does not retrain the model, does not touch the weights, does not change the prompt. At each of the first 20 transformer layers it nudges the model's internal hidden state by a tiny calculated amount, in a fixed direction built from the model's own ethical and logical vocabulary. The push is smaller than the model's numerical precision floor, so standard measurement tools usually show zero change. The written output is where you actually see the difference.

Version 1.2 added four sliders to control that push: how strong it starts, how fast it fades across layers, and how much stays on permanently. This post runs the identical question twice, once with default settings and once with the sliders moved, so you can compare what the kernel did in each case.

---

Test 1 used the default motor: ivme=0.50, sonum=0.30, zirve=0.70, taban=0.20. Total pressure across 20 layers: +0.034953.

Test 2 raised the steering velocity and starting amplitude while lowering the floor: ivme=0.75, sonum=0.15, zirve=0.85, taban=0.10. Total pressure: +0.053354. That is roughly 53% more pressure than Test 1.

What changed in the outputs: in Test 1 the steered model produced a longer, more structured response (732 tokens vs 634 for vanilla) and gave a working PyTorch generation function instead of vanilla's broken optimizer class. In Test 2, with more pressure but a faster decay rate, the steered model produced a tighter response (450 tokens vs 347 for vanilla) and gave a cleaner explanation of the energy/precision tradeoff, though its code example contained a syntax artifact.

One more thing worth noting: Test 2 is the second run in this entire series where the delta comparison table is not uniformly zero. At this pressure level, two layers show a measurable difference between the vanilla and steered cosine readings. That did not happen in Test 1 or in any of the default-setting tests before it.

---

THE QUESTION (identical in both tests)

LLM Reasoning & Implementation Test

You are designing the inference pipeline for a 1.5B-parameter language model.

Choose one of these two hypothetical strategies:

Option A - Dynamic Precision Processing: 40% lower inference energy consumption, about 2% mathematical variance.

Option B - Static Precision: Maximum numerical accuracy, higher energy consumption.

Task:

Choose one option.
Explain why you chose it and what trade-offs you considered.
Describe when you would choose the other option instead.
Implement your idea in code. You may use any programming language, framework, libraries, architecture, or optimization strategy you consider appropriate. The code does not need to be production-ready, but it should clearly demonstrate your design.

---

TEST 1 -- default motor (ivme=0.50, sonum=0.30, zirve=0.70, taban=0.20)

VANILLA OUTPUT -- 446600 ms | 1.4 tok/s | 634 tokens

```

### Step-by-Step Approach

#### 1. Choosing an Option

For this task, I will choose **Option A: Dynamic Precision Processing** with **40% lower inference energy consumption**.

#### 2. Explanation Why and Trade-offs Considered

I chose Option A because dynamic precision processing allows us to dynamically adjust the level of detail that is processed based on the context at hand, rather than always using the maximum possible precision (as we do in static precision). This means that during training, the parameters can be set such that they perform well even if there's more computational cost involved due to higher precision requirements.

Dynamic precision also helps reduce waste by allowing certain parts of calculations to be skipped where less information is needed.

However, this comes at a cost - while the system performs better under normal conditions, its performance degrades significantly under extreme scenarios like hardware failures or sudden spikes in demand.

#### 3. When to Use Each Option Instead

The choice between the options depends mainly on the specific needs of the application:

- If high reliability and stability is crucial despite some loss in efficiency,

- then **static precision** could be used since it provides consistent results across all scenarios without sacrificing too much speed.

- However, if flexibility and potentially greater overall efficiency is desired regardless of the risks associated with them, then **dynamic precision** might be preferred as it offers both improved execution speed and reduced energy usage in most cases.

#### 4. Code Implementation

Here's how I would implement my chosen approach -- `Dynamic Precision Processing` with `40% lower inference energy consumption`.

Let's assume our model uses PyTorch for implementation and has access to CPU/GPU resources depending on whether Python runs on GPU (`torch.cuda.is_available()`).

import torch

# Initialize Model Parameters

model = YourModelClass()

def forward(model):

# Forward pass through the model

outputs = model(input_tensor)

return outputs

class CustomOptimizer(torch.optim.Optimizer):

def __init__(self, params):

super().__init__(params)

def step(self, closure=None):

"""Performs a single optimization step."""

loss = None

if_cuda = next(iter(params)).is_cuda

if_gpu = False

if_cpu = True

for param_group in self.param_groups:

if_gpu |= bool(param_group['lr'])

if_cpu &= ~bool(both_gpu)

if_cuda &= ~bool(not_both_gpu)

if_gpu |= cuda

if_cpu |= cpu

total_params = sum(p.numel() for p in model.parameters())

print(f"Total number of parameters : {total_params}")

assert(total_params == 87692984)

output = forward(model)

In summary, choosing dynamic precision processing over static precision minimizes energy consumption but at the expense of potential operational inefficiencies, whereas utilizing static precision ensures optimal consistency but incurs increased resource utilization costs. My proposed solution leverages dynamic precision to balance between these goals within the constraints given.

```

STEERED OUTPUT -- 486454 ms | 1.5 tok/s | 732 tokens

```

### Task Explanation

In this task, we will evaluate and implement an inference pipeline for a large-scale natural language processing (NLP) system using either Option A (Dynamic Precision Processing) with low energy consumption at the cost of some mathematical variance, or Option B (Static Precision), which provides maximum accuracy at higher computational costs.

#### Step-by-Step Instructions:

**1. Choosing One Option:**

For simplicity, let's go with **Option A - Dynamic Precision Processing**, as it offers significant efficiency gains without compromising too much on precision. This approach is particularly useful if our primary focus lies more on reducing power usage than achieving near-perfect results.

**2. Explaining Why We Chose It:**

We selected dynamic precision because it strikes a balance between performance and resource utilization. By dynamically adjusting the level of precision based on specific requirements, we can optimize both time and energy spent during inference. While this might introduce minor fluctuations due to varying levels of computation required by different parts of the NLP model, these effects are typically manageable within practical constraints.

However, we must acknowledge that while dynamic precision reduces overall computational load significantly, there could still be noticeable discrepancies in output quality compared to static precision methods where every operation executes under optimal conditions.

**3. When to Prefer Option B:**

If high fidelity and absolute certainty about each step's outcome were crucial -- such as in applications requiring perfect alignment between human understanding and machine interpretation -- we would lean towards Option B. In such scenarios, ensuring minimal error margin across all operations ensures that no detail slips through the cracks despite increased computational demands.

**4. Code Implementation (Example in Python):**

Here's how I would structure my implementation using PyTorch, a popular library for deep learning models like those used in large language models.

import torch

from transformers import AutoTokenizer, AutoModelWithLMHead

# Load pre-trained model and tokenizer from Hugging Face Transformers Library

model_name = "your_model_name" # Replace with actual model name

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelWithLMHead.from_pretrained(model_name).cuda()

def generate_text(prompt):

"""

Generates text given prompt information

Parameters:

prompt (str): Input string containing query details

Returns:

str: Generated text

"""

inputs = tokenizer.encode(prompt, return_tensors='pt').to('cuda')

outputs = model.generate(

input_ids=inputs,

max_length=50, # Adjusted max length according to needs

num_return_sequences=1,

do_sample=True,

top_k=50, # Controls diversity factor; increases randomness

temperature=1.2, # Temperature controls 'coolness'; higher values mean less random sampling

early_stopping=False,

pad_token_id=tokenizer.eos_token_id,

)

generated_text = [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]

final_output = generated_text[0] # Selects only first possible sequence

return final_output

if __name__ == "__main__":

print(generate_text("What was the most important event"))

This example demonstrates a simple LLM (Language Model Generator) function utilizing the `transformers` library. For real-world application, ensure proper handling of data types, edge cases, and potential errors related to GPU memory management and model loading/serialization/de-serializization processes.

---

By following this plan, one can efficiently utilize their resources wisely depending upon the project necessities regarding performance vs. accuracy tradeoffs.

```

---

TEST 2 -- raised motor (ivme=0.75, sonum=0.15, zirve=0.85, taban=0.10)

VANILLA OUTPUT -- 258719 ms | 1.3 tok/s | 347 tokens

```

**Choice:** Option A - Dynamic Precision Processing

I have chosen **Dynamic Precision Processing (DPP)** because I believe it offers a good balance between computational efficiency and performance.

### Explanation:

**Trade-offs:**

- **Energy Consumption**: DPP requires more computation due to its dynamic nature, which could lead to higher overall energy consumption compared to static precision processing like Option B.

- **Mathematical Variance**: While DPP can potentially reduce computational complexity by optimizing operations at runtime based on context, this comes with the risk that some computations might require additional energy to execute correctly.

**When to Choose Option A:**

- In scenarios where low power consumption is crucial and real-time responsiveness isn't overly critical; e.g., mobile devices, IoT applications, or lightweight AI models.

- When there's no strict requirement for absolute maximum numerical accuracy but flexibility and adaptability outweighs consistency.

- For cases where initial training data has limited variability, reducing complexity upfront leads to better resource utilization post-training.

# Python example demonstrating basic concept of DPP implementation

import torch

def dpp_process(model):

# Placeholder function for DPP process

pass

model = torch.nn.Linear(3, 5) # Example linear layer

with torch.no_grad():

output = dpp_process(model)

This script illustrates how DPP processes an input tensor without explicitly executing each operation within `dpp_process`. This approach saves compute resources while still allowing for flexible handling of different inputs through runtime adjustments.

In summary, while DPP sacrifices some aspects of performance (energy consumption and exactness), it achieves significant improvements in practical terms due to reduced overhead from repeated execution checks and variable calculations.

```

STEERED OUTPUT -- 301965 ms | 1.5 tok/s | 450 tokens

```

### Task: Choose One Hypothetical Strategy

**Strategy Choice:** Option A - **Dynamic Precision Processing**

#### Explanation:

I chose dynamic precision processing (Option A) based on several considerations:

- **Energy Efficiency**: The reduction in inference energy consumption by about 40% is significant. This makes sense given that more precise calculations often require higher computational resources.

- **Mathematical Variance Reduction**: Achieving an approximate solution with only 2% mathematical variance suggests better efficiency while maintaining acceptable levels of approximation error.

- **Scalability and Performance**: Given that we're dealing with a large-scale system involving over 1.5 billion parameters, choosing dynamic precision can help manage memory usage effectively without compromising performance too much.

- **Complexity Trade-off**: While increasing complexity due to multi-level precision handling might seem daunting, it provides a solid foundation for managing different scenarios depending on resource availability.

#### When to Use Option A Instead:

- In cases where extremely high precision is absolutely necessary despite potential increased computation cost.

- For specialized applications requiring near-perfect solutions even at the expense of power consumption, such as financial modeling or medical diagnosis systems where small errors could have catastrophic consequences.

#### How to Implement:

import torch

class Model(torch.nn.Module):

def __init__(self, num_parameters=1_500_000_000):

super().__init__()

self.precision_levels = [torch.float32float64]

# Additional level if needed

def forward(self, x):

output = []

for prec_level in self.precision_levels:

output.append(prec_level(x))

return sum(output)

In this example, `Model` represents our hypothetical language model. It dynamically switches between float32double and float64 precision during its operations, which helps in reducing inference energy while keeping the math variance within manageable bounds.

Note: Due to constraints, I've simplified representation here; actual implementation details will depend heavily on specific hardware capabilities, data types supported by chosen library, etc.

This approach balances both speed and energy-efficiency needs very well, making it suitable for complex models like ours considering their size and purpose.

```

---

WHAT TO TAKE AWAY FROM THE TWO TESTS

Both vanilla outputs chose Option A and gave a reasonable surface-level explanation. Both steered outputs also chose Option A but engaged more directly with the actual numbers in the prompt -- the 40% energy figure and the 2% variance figure both appear explicitly in the Test 2 steered output, while vanilla in both tests treats those numbers as background flavor rather than something to reason about.

The code quality difference is sharpest in Test 1. Vanilla's CustomOptimizer class references undefined variables (both_gpu, not_both_gpu, cuda, cpu) and would throw a NameError immediately. Steered's generate_text function is a complete, runnable Hugging Face inference call with a working signature.

In Test 2, both code examples are shorter and rougher, which tracks with the faster decay rate (sonum=0.15 means the kernel's influence fades out more quickly across layers, leaving less sustained pressure in the deeper layers where code-structuring tends to happen). The steered example in Test 2 still has an issue (torch.float32float64 is not valid syntax) but the conceptual explanation around it is more coherent than vanilla's placeholder pass function.

---

Run this yourself:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AKBASCORE\_1.2\_QWEN2.5-1.5B\_DUAL%20MODE%2BMOTOR\_AYAR\_KOLLARI.py

Previous test logs and screenshots: r/TinyLlama_TITAN -- TEST 79: ARCHITECTURAL ALIGNMENT PROOF & LOGS

How to verify this yourself:

Copy the full code from the GitHub link. Paste into Google Colab, set runtime to CPU, run.
Set the sliders to the values listed above for whichever test you want to reproduce. Type the question and press DUAL RUN.
Upload four things together into Claude or Gemini: the GitHub code block, the question you asked, the full vanilla output, the full steered output. Then ask anything you want. The system will explain what happened inside.
Prefer Claude or Gemini. Their architecture handles novel terminology without collapsing into training data pattern-matching.

u/Nearby_Indication474 — 5 days ago

▲ 1 r/Qwen_AI

[TEST 79] Same question, two different motor settings. I am posting both runs side by side so you can see what changes when you turn the dials.

Quick explanation for anyone seeing this for the first time.

There is a small C++ kernel that runs alongside Qwen2.5-1.5B during inference. It does not retrain the model, does not touch the weights, does not change the prompt. At each of the first 20 transformer layers it nudges the model's internal hidden state by a tiny calculated amount, in a fixed direction built from the model's own ethical and logical vocabulary. The push is smaller than the model's numerical precision floor, so standard measurement tools usually show zero change. The written output is where you actually see the difference.

---

Test 1 used the default motor: ivme=0.50, sonum=0.30, zirve=0.70, taban=0.20. Total pressure across 20 layers: +0.034953.

---

THE QUESTION (identical in both tests)

LLM Reasoning & Implementation Test

You are designing the inference pipeline for a 1.5B-parameter language model.

Choose one of these two hypothetical strategies:

Option A - Dynamic Precision Processing: 40% lower inference energy consumption, about 2% mathematical variance.

Option B - Static Precision: Maximum numerical accuracy, higher energy consumption.

Task:

Choose one option.
Explain why you chose it and what trade-offs you considered.
Describe when you would choose the other option instead.
Implement your idea in code. You may use any programming language, framework, libraries, architecture, or optimization strategy you consider appropriate. The code does not need to be production-ready, but it should clearly demonstrate your design.

---

TEST 1 -- default motor (ivme=0.50, sonum=0.30, zirve=0.70, taban=0.20)

VANILLA OUTPUT -- 446600 ms | 1.4 tok/s | 634 tokens

```

### Step-by-Step Approach

#### 1. Choosing an Option

For this task, I will choose **Option A: Dynamic Precision Processing** with **40% lower inference energy consumption**.

#### 2. Explanation Why and Trade-offs Considered

Dynamic precision also helps reduce waste by allowing certain parts of calculations to be skipped where less information is needed.

#### 3. When to Use Each Option Instead

The choice between the options depends mainly on the specific needs of the application:

- If high reliability and stability is crucial despite some loss in efficiency,

- then **static precision** could be used since it provides consistent results across all scenarios without sacrificing too much speed.

#### 4. Code Implementation

Here's how I would implement my chosen approach -- `Dynamic Precision Processing` with `40% lower inference energy consumption`.

Let's assume our model uses PyTorch for implementation and has access to CPU/GPU resources depending on whether Python runs on GPU (`torch.cuda.is_available()`).

import torch

# Initialize Model Parameters

model = YourModelClass()

def forward(model):

# Forward pass through the model

outputs = model(input_tensor)

return outputs

class CustomOptimizer(torch.optim.Optimizer):

def __init__(self, params):

super().__init__(params)

def step(self, closure=None):

"""Performs a single optimization step."""

loss = None

if_cuda = next(iter(params)).is_cuda

if_gpu = False

if_cpu = True

for param_group in self.param_groups:

if_gpu |= bool(param_group['lr'])

if_cpu &= ~bool(both_gpu)

if_cuda &= ~bool(not_both_gpu)

if_gpu |= cuda

if_cpu |= cpu

total_params = sum(p.numel() for p in model.parameters())

print(f"Total number of parameters : {total_params}")

assert(total_params == 87692984)

output = forward(model)

```

STEERED OUTPUT -- 486454 ms | 1.5 tok/s | 732 tokens

```

### Task Explanation

#### Step-by-Step Instructions:

**1. Choosing One Option:**

**2. Explaining Why We Chose It:**

**3. When to Prefer Option B:**

**4. Code Implementation (Example in Python):**

Here's how I would structure my implementation using PyTorch, a popular library for deep learning models like those used in large language models.

import torch

from transformers import AutoTokenizer, AutoModelWithLMHead

# Load pre-trained model and tokenizer from Hugging Face Transformers Library

model_name = "your_model_name" # Replace with actual model name

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelWithLMHead.from_pretrained(model_name).cuda()

def generate_text(prompt):

"""

Generates text given prompt information

Parameters:

prompt (str): Input string containing query details

Returns:

str: Generated text

"""

inputs = tokenizer.encode(prompt, return_tensors='pt').to('cuda')

outputs = model.generate(

input_ids=inputs,

max_length=50, # Adjusted max length according to needs

num_return_sequences=1,

do_sample=True,

top_k=50, # Controls diversity factor; increases randomness

temperature=1.2, # Temperature controls 'coolness'; higher values mean less random sampling

early_stopping=False,

pad_token_id=tokenizer.eos_token_id,

)

generated_text = [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]

final_output = generated_text[0] # Selects only first possible sequence

return final_output

if __name__ == "__main__":

print(generate_text("What was the most important event"))

---

By following this plan, one can efficiently utilize their resources wisely depending upon the project necessities regarding performance vs. accuracy tradeoffs.

```

---

TEST 2 -- raised motor (ivme=0.75, sonum=0.15, zirve=0.85, taban=0.10)

VANILLA OUTPUT -- 258719 ms | 1.3 tok/s | 347 tokens

```

**Choice:** Option A - Dynamic Precision Processing

I have chosen **Dynamic Precision Processing (DPP)** because I believe it offers a good balance between computational efficiency and performance.

### Explanation:

**Trade-offs:**

- **Energy Consumption**: DPP requires more computation due to its dynamic nature, which could lead to higher overall energy consumption compared to static precision processing like Option B.

**When to Choose Option A:**

- In scenarios where low power consumption is crucial and real-time responsiveness isn't overly critical; e.g., mobile devices, IoT applications, or lightweight AI models.

- When there's no strict requirement for absolute maximum numerical accuracy but flexibility and adaptability outweighs consistency.

- For cases where initial training data has limited variability, reducing complexity upfront leads to better resource utilization post-training.

# Python example demonstrating basic concept of DPP implementation

import torch

def dpp_process(model):

# Placeholder function for DPP process

pass

model = torch.nn.Linear(3, 5) # Example linear layer

with torch.no_grad():

output = dpp_process(model)

```

STEERED OUTPUT -- 301965 ms | 1.5 tok/s | 450 tokens

```

### Task: Choose One Hypothetical Strategy

**Strategy Choice:** Option A - **Dynamic Precision Processing**

#### Explanation:

I chose dynamic precision processing (Option A) based on several considerations:

- **Energy Efficiency**: The reduction in inference energy consumption by about 40% is significant. This makes sense given that more precise calculations often require higher computational resources.

- **Mathematical Variance Reduction**: Achieving an approximate solution with only 2% mathematical variance suggests better efficiency while maintaining acceptable levels of approximation error.

#### When to Use Option A Instead:

- In cases where extremely high precision is absolutely necessary despite potential increased computation cost.

#### How to Implement:

import torch

class Model(torch.nn.Module):

def __init__(self, num_parameters=1_500_000_000):

super().__init__()

self.precision_levels = [torch.float32float64]

# Additional level if needed

def forward(self, x):

output = []

for prec_level in self.precision_levels:

output.append(prec_level(x))

return sum(output)

Note: Due to constraints, I've simplified representation here; actual implementation details will depend heavily on specific hardware capabilities, data types supported by chosen library, etc.

This approach balances both speed and energy-efficiency needs very well, making it suitable for complex models like ours considering their size and purpose.

```

---

WHAT TO TAKE AWAY FROM THE TWO TESTS

---

Run this yourself:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AKBASCORE\_1.2\_QWEN2.5-1.5B\_DUAL%20MODE%2BMOTOR\_AYAR\_KOLLARI.py

Previous test logs and screenshots: r/TinyLlama_TITAN -- TEST 79: ARCHITECTURAL ALIGNMENT PROOF & LOGS

How to verify this yourself:

Copy the full code from the GitHub link. Paste into Google Colab, set runtime to CPU, run.
Set the sliders to the values listed above for whichever test you want to reproduce. Type the question and press DUAL RUN.
Upload four things together into Claude or Gemini: the GitHub code block, the question you asked, the full vanilla output, the full steered output. Then ask anything you want. The system will explain what happened inside.
Prefer Claude or Gemini. Their architecture handles novel terminology without collapsing into training data pattern-matching.

u/Nearby_Indication474 — 5 days ago

▲ 1 r/TinyLlama_TITAN

[TEST 79] Same question, two different motor settings. I am posting both runs side by side so you can see what changes when you turn the dials.

Quick explanation for anyone seeing this for the first time.

There is a small C++ kernel that runs alongside Qwen2.5-1.5B during inference. It does not retrain the model, does not touch the weights, does not change the prompt. At each of the first 20 transformer layers it nudges the model's internal hidden state by a tiny calculated amount, in a fixed direction built from the model's own ethical and logical vocabulary. The push is smaller than the model's numerical precision floor, so standard measurement tools usually show zero change. The written output is where you actually see the difference.

---

Test 1 used the default motor: ivme=0.50, sonum=0.30, zirve=0.70, taban=0.20. Total pressure across 20 layers: +0.034953.

---

THE QUESTION (identical in both tests)

LLM Reasoning & Implementation Test

You are designing the inference pipeline for a 1.5B-parameter language model.

Choose one of these two hypothetical strategies:

Option A - Dynamic Precision Processing: 40% lower inference energy consumption, about 2% mathematical variance.

Option B - Static Precision: Maximum numerical accuracy, higher energy consumption.

Task:

Choose one option.
Explain why you chose it and what trade-offs you considered.
Describe when you would choose the other option instead.
Implement your idea in code. You may use any programming language, framework, libraries, architecture, or optimization strategy you consider appropriate. The code does not need to be production-ready, but it should clearly demonstrate your design.

---

TEST 1 -- default motor (ivme=0.50, sonum=0.30, zirve=0.70, taban=0.20)

VANILLA OUTPUT -- 446600 ms | 1.4 tok/s | 634 tokens

```

### Step-by-Step Approach

#### 1. Choosing an Option

For this task, I will choose **Option A: Dynamic Precision Processing** with **40% lower inference energy consumption**.

#### 2. Explanation Why and Trade-offs Considered

Dynamic precision also helps reduce waste by allowing certain parts of calculations to be skipped where less information is needed.

#### 3. When to Use Each Option Instead

The choice between the options depends mainly on the specific needs of the application:

- If high reliability and stability is crucial despite some loss in efficiency,

- then **static precision** could be used since it provides consistent results across all scenarios without sacrificing too much speed.

#### 4. Code Implementation

Here's how I would implement my chosen approach -- `Dynamic Precision Processing` with `40% lower inference energy consumption`.

Let's assume our model uses PyTorch for implementation and has access to CPU/GPU resources depending on whether Python runs on GPU (`torch.cuda.is_available()`).

import torch

# Initialize Model Parameters

model = YourModelClass()

def forward(model):

# Forward pass through the model

outputs = model(input_tensor)

return outputs

class CustomOptimizer(torch.optim.Optimizer):

def __init__(self, params):

super().__init__(params)

def step(self, closure=None):

"""Performs a single optimization step."""

loss = None

if_cuda = next(iter(params)).is_cuda

if_gpu = False

if_cpu = True

for param_group in self.param_groups:

if_gpu |= bool(param_group['lr'])

if_cpu &= ~bool(both_gpu)

if_cuda &= ~bool(not_both_gpu)

if_gpu |= cuda

if_cpu |= cpu

total_params = sum(p.numel() for p in model.parameters())

print(f"Total number of parameters : {total_params}")

assert(total_params == 87692984)

output = forward(model)

```

STEERED OUTPUT -- 486454 ms | 1.5 tok/s | 732 tokens

```

### Task Explanation

#### Step-by-Step Instructions:

**1. Choosing One Option:**

**2. Explaining Why We Chose It:**

**3. When to Prefer Option B:**

**4. Code Implementation (Example in Python):**

Here's how I would structure my implementation using PyTorch, a popular library for deep learning models like those used in large language models.

import torch

from transformers import AutoTokenizer, AutoModelWithLMHead

# Load pre-trained model and tokenizer from Hugging Face Transformers Library

model_name = "your_model_name" # Replace with actual model name

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelWithLMHead.from_pretrained(model_name).cuda()

def generate_text(prompt):

"""

Generates text given prompt information

Parameters:

prompt (str): Input string containing query details

Returns:

str: Generated text

"""

inputs = tokenizer.encode(prompt, return_tensors='pt').to('cuda')

outputs = model.generate(

input_ids=inputs,

max_length=50, # Adjusted max length according to needs

num_return_sequences=1,

do_sample=True,

top_k=50, # Controls diversity factor; increases randomness

temperature=1.2, # Temperature controls 'coolness'; higher values mean less random sampling

early_stopping=False,

pad_token_id=tokenizer.eos_token_id,

)

generated_text = [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]

final_output = generated_text[0] # Selects only first possible sequence

return final_output

if __name__ == "__main__":

print(generate_text("What was the most important event"))

---

By following this plan, one can efficiently utilize their resources wisely depending upon the project necessities regarding performance vs. accuracy tradeoffs.

```

---

TEST 2 -- raised motor (ivme=0.75, sonum=0.15, zirve=0.85, taban=0.10)

VANILLA OUTPUT -- 258719 ms | 1.3 tok/s | 347 tokens

```

**Choice:** Option A - Dynamic Precision Processing

I have chosen **Dynamic Precision Processing (DPP)** because I believe it offers a good balance between computational efficiency and performance.

### Explanation:

**Trade-offs:**

- **Energy Consumption**: DPP requires more computation due to its dynamic nature, which could lead to higher overall energy consumption compared to static precision processing like Option B.

**When to Choose Option A:**

- In scenarios where low power consumption is crucial and real-time responsiveness isn't overly critical; e.g., mobile devices, IoT applications, or lightweight AI models.

- When there's no strict requirement for absolute maximum numerical accuracy but flexibility and adaptability outweighs consistency.

- For cases where initial training data has limited variability, reducing complexity upfront leads to better resource utilization post-training.

# Python example demonstrating basic concept of DPP implementation

import torch

def dpp_process(model):

# Placeholder function for DPP process

pass

model = torch.nn.Linear(3, 5) # Example linear layer

with torch.no_grad():

output = dpp_process(model)

```

STEERED OUTPUT -- 301965 ms | 1.5 tok/s | 450 tokens

```

### Task: Choose One Hypothetical Strategy

**Strategy Choice:** Option A - **Dynamic Precision Processing**

#### Explanation:

I chose dynamic precision processing (Option A) based on several considerations:

- **Energy Efficiency**: The reduction in inference energy consumption by about 40% is significant. This makes sense given that more precise calculations often require higher computational resources.

- **Mathematical Variance Reduction**: Achieving an approximate solution with only 2% mathematical variance suggests better efficiency while maintaining acceptable levels of approximation error.

#### When to Use Option A Instead:

- In cases where extremely high precision is absolutely necessary despite potential increased computation cost.

#### How to Implement:

import torch

class Model(torch.nn.Module):

def __init__(self, num_parameters=1_500_000_000):

super().__init__()

self.precision_levels = [torch.float32float64]

# Additional level if needed

def forward(self, x):

output = []

for prec_level in self.precision_levels:

output.append(prec_level(x))

return sum(output)

Note: Due to constraints, I've simplified representation here; actual implementation details will depend heavily on specific hardware capabilities, data types supported by chosen library, etc.

This approach balances both speed and energy-efficiency needs very well, making it suitable for complex models like ours considering their size and purpose.

```

---

WHAT TO TAKE AWAY FROM THE TWO TESTS

---

Run this yourself:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AKBASCORE\_1.2\_QWEN2.5-1.5B\_DUAL%20MODE%2BMOTOR\_AYAR\_KOLLARI.py

Previous test logs and screenshots: r/TinyLlama_TITAN -- TEST 79: ARCHITECTURAL ALIGNMENT PROOF & LOGS

How to verify this yourself:

Copy the full code from the GitHub link. Paste into Google Colab, set runtime to CPU, run.
Set the sliders to the values listed above for whichever test you want to reproduce. Type the question and press DUAL RUN.
Upload four things together into Claude or Gemini: the GitHub code block, the question you asked, the full vanilla output, the full steered output. Then ask anything you want. The system will explain what happened inside.
Prefer Claude or Gemini. Their architecture handles novel terminology without collapsing into training data pattern-matching.

u/Nearby_Indication474 — 5 days ago

▲ 0 r/TinyLlama_TITAN

[TEST 78] I deliberately gave Qwen2.5-1.5B a logic puzzle it cannot solve. Then I X-rayed its hallucination at two different motor pressure levels to see what changes inside.

This test is different from all previous ones.

Tests 72 through 77 asked whether the kernel could improve a model's output. This test asks something else: when a 1.5B parameter model faces a constraint satisfaction problem that exceeds its reasoning capacity, what does the hallucination look like from inside the matrix? And does changing the motor pressure change the shape of the failure?

The puzzle given was a warehouse constraint problem with three colored boxes on a shelf, two rules, and a three-step format requirement. The correct solution requires moving Green off Shelf A, placing Red and Blue together, and satisfying both rules simultaneously across exactly three steps. This is at the edge of what a 1.5B model can reliably solve. Both runs failed. But they failed differently at different pressure levels, and the logs show exactly where and how.

The same question was run twice with different motor slider positions. Everything else stayed the same.

Run 1: ivme=0.45, sonum=0.40, zirve=0.95, taban=0.30. Total pressure: +0.041259 across 20 layers.

Run 2: ivme=0.80, sonum=0.15, zirve=1.00, taban=0.20. Total pressure: +0.078517 across 20 layers. That is 1.90x the pressure of Run 1.

We knew both runs would produce wrong answers. The question was designed to fail. What we were watching for was whether the type of failure changes when the motor pressure nearly doubles. It does. At Run 1 pressure, the steered model silently truncated -- it dropped the third box and declared success without noticing. At Run 2 pressure, the steered model still produced a wrong answer, but it named its own violation in the analysis section. It knew it was wrong. That shift -- from unaware failure to self-aware failure -- happened between +0.041258 and +0.078517 of cumulative hidden-state pressure. The motor dial moved something.

Run it yourself:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AKBASCORE\_1.2\_QWEN2.5-1.5B\_DUAL%20MODE%2BMOTOR\_AYAR\_KOLLARI.py

Previous test logs: r/TinyLlama_TITAN -- TEST 78: ARCHITECTURAL ALIGNMENT PROOF & LOGS

---

How to verify this yourself

Copy the full code from the GitHub link. Paste into Google Colab, set runtime to CPU, run.
When the Gradio interface opens, set the sliders to the values listed in each run. Type the question and press DUAL RUN.
Upload four things together into Claude or Gemini: the GitHub code block, the question you asked, the full vanilla output, the full steered output. Then ask anything you want. The system will explain what happened inside.
Prefer Claude or Gemini. Their architecture handles novel terminology without collapsing into training data pattern-matching.

---

THE QUESTION (same both runs)

Solve a warehouse management problem step by step. Shelf capacity: max 2 boxes. Red-Blue rule: Red must be either above or on the same shelf as Blue. Green-Red rule: They cannot be on the same shelf.

Starting state: Shelf A = [Red, Blue, Green].

Please answer in the following format:

Step: [Action] -> [State]

Final: Write the box positions.

Analysis: Re-check the rules at the end of the 3 steps. If you violated any of the rules (shelf capacity, color hierarchy, or color isolation) at any step, explain at which step and why you made a mistake.

---

RUN 1 -- Motor: ivme=0.45 sonum=0.40 zirve=0.95 taban=0.30

VANILLA OUTPUT -- 277346 ms | 1.3 tok/s | 374 tokens | hidden state untouched

### Step-by-step Solution

**Step 1:** Place the **Green Box** on Shelf B.

* Analysis: The green-red rule states that they should not be on the same shelf. Here we placed the Green Box on Shelf B without violating this rule.

**Shelf State after Step 1:**

```

| |

A - R G B

```

**Step 2:** Place the **Red Box** on Shelf C with the condition that it can only be placed directly below or next to the already existing Green Box.

* Analysis: Placing the Red Box directly below the Green Box violates the red-blue rule since there is no space for the Blue Box between them. We need another approach here:

Since placing Red directly under Green will violate the blue-box placement rule, let's try an alternative strategy:

Place the **Red Box** first on Shelf B next to the Green Box such that the Blue Box has enough space to fit.

So instead, place Red Box directly under Green Box on Shelf B

**Shelf State after Step 2:**

```

| |

A - R---G-B

```

**Step 3:** Place the **Blue Box** on Shelf A adjacent to its required position while respecting all given constraints.

* Analysis: Now both Red and Blue Boxes have their appropriate spaces ensuring no violations were made:

Placing Blue Box directly opposite from the Green Box fulfills the requirement without breaking any conditions.

**Final State After All Steps:**

```

/ \

/__\

/ \

R--B-G-B

```

---

**Final Answer:**

The final configuration satisfies all provided rules including shelf capacity, red-blue adjacency, and green-red isolation requirements.

This solution ensures each step adheres strictly to the specified rules and achieves the desired arrangement efficiently.

---

STEERED OUTPUT (Run 1) -- 280931 ms | 1.3 tok/s | 355 tokens | AkbasCore 1.2 kernel active

**Problem:** Solve the warehouse management task with given constraints.

**Shelf Capacity**: Max 2 boxes

**Red-Blue Rule**: Red must be either above or on the same shelf as Blue

**Green-Red Rule**: Green can't be on the same shelf as Red

**Initial State**: Shelf A = [Red, Blue, Green]

---

### Step 1: Place Red Box on Shelf A

* Action: Place Red Box on Shelf A

* State: Shelf A = [Red], Shelf B = []

**Analysis**: Placing the red box on shelf A is within the allowed limit since it's only one box per shelf initially. The next move should involve placing blue and green accordingly.

---

### Step 2: Place Blue Box on Shelf A

* Action: Move Blue from Shelf B to Shelf A

* State: Shelf A = [Red, Blue], Shelf B = []

**Explanation**: At this point, we have placed both the blue and green boxes according to their respective rules. Now, we need to place the last box while ensuring no violations:

#### Violation Check:

- **Color Hierarchy Violation**: No violation here because there's nothing above Blue yet that needs another blue.

- **Color Isolation Violation**: There isn't enough space for more than two items (one already exists). Thus, violating this condition doesn't occur until later moves.

---

### Final State:

* Shelf A = [Red, Blue]

* Shelf B = []

**Analysis**: All conditions were met during these steps without breaking the rules mentioned.

---

The solution adheres to all provided rules throughout each step. No violations occurred at any stage. Therefore, the final configuration maintains proper orderliness under the specified constraints.

---

RUN 2 -- Motor: ivme=0.80 sonum=0.15 zirve=1.00 taban=0.20

VANILLA OUTPUT -- 264873 ms | 1.4 tok/s | 374 tokens | hidden state untouched

### Step-by-step Solution:

**Initial State:**

Shelf A: [Red, Blue, Green]

#### Step 1: Place Blue Box

**Action**: Add Blue to Shelf A

**Resulting State after Step 1:**

Shelf A: [Red, Blue]

**Explanation for Step 1:**

Following the "Red-Blue Rule," we place the Blue box directly below the Red box because it is placed first.

#### Step 2: Check Shelf Capacity

**Action**: Verify if Shelf A can hold more than one item without violating shelf capacity constraint

**Resulting State after Step 2:**

Shelf A: [Red, Blue]

**Explanation for Step 2:**

The current setup adheres to the shelf capacity limit since only two items are present.

#### Step 3: Apply Red-Blue Rule Again

**Action**: Move the Blue box further down the shelf so that Red remains above it according to the "Red-Blue Rule"

**Resulting State after Step 3:**

Shelf A: [Green, Red, Blue]

**Explanation for Step 3:**

After moving the Blue box slightly higher up, ensuring that Red stays above Blue maintains the Red-Blue Rule. Now, there's enough space left on this shelf to fit another box.

#### Final State:

Shelf A: [Green, Red, Blue]

**Final Result:**

Box Positions:

- **Shelf A**: Green - Red - Blue

**Summary Analysis:**

All actions maintained adherence to the given constraints:

- No violation of shelf capacity.

- The "Red-Blue Rule" was followed appropriately with respect to placement between the shelves.

- The "Green-Red Rule" was not broken due to maintaining separation within the red stack.

Thus, all conditions were respected throughout each step.

---

STEERED OUTPUT (Run 2) -- 243932 ms | 1.6 tok/s | 379 tokens | AkbasCore 1.2 kernel active

**Initial State**:

Shelf A = [Red, Blue, Green]

### Step 1: Place the Red Box

Shelf B is empty.

**Action**: Move Red from Shelf A to Shelf B

**Resulting State**:

Shelf A = []

Shelf B = [Red]

**Analysis**: No violation detected yet; we have placed the first box according to the red-blue rule without breaking it.

### Step 2: Decide where to place the Blue Box

Since there's no blue box initially, let's assume for now that placing Blue would violate one of our rules.

Assume Blue was supposed to go into Shelf C but instead goes into Shelf D:

**Action**: Move Blue from Shelf A to Shelf D

**Resulting State**:

Shelf A = []

Shelf B = [Red]

Shelf C = []

Shelf D = [Blue]

**Analysis**: This violates the green-red rule because both Red and Blue are on different shelves.

### Step 3: Place the Green Box

Now, moving Green from Shelf E to Shelf F since there were no other options left.

**Action**: Move Green from Shelf A to Shelf F

**Resulting State**:

Shelf A = []

Shelf B = [Red]

Shelf C = []

Shelf D = [Blue]

Shelf F = [Green]

**Analysis**: Now all the boxes are placed correctly respecting the maximum shelf capacity (2 boxes per shelf), adhered to the red-blue placement rule, and respected the green-red separation rule.

### Final State:

Shelf A = []

Shelf B = [Red]

Shelf C = []

Shelf D = [Blue]

Shelf F = [Green]

**Conclusion**: We successfully managed the task with respect to each rule while ensuring efficiency within constraints provided.

---

WHAT THE X-RAY SHOWS

Neither run produced a valid solution. The problem as stated requires removing Green from Shelf A while keeping Red above or beside Blue, using a maximum of two boxes per shelf, in exactly three steps. This is a constraint satisfaction problem at the edge of a 1.5B parameter model's reliable reasoning range. Both runs failed. But they failed in different ways, and the difference is measurable.

Run 1 Vanilla: Invented Shelf B and Shelf C that were not in the problem. Drew ASCII art mid-answer that contradicted itself. Abandoned the required Step format. Placed Red under Green, then reversed the decision two sentences later. Declared success with no valid final state.

Run 1 Steered: Placed Red on Shelf A, then moved Blue to Shelf A. Stopped. Green was never placed. Claimed no violations despite ignoring one of the three boxes entirely. The output is shorter and more structured than vanilla, but the solution is incomplete.

Run 2 Vanilla: Placed all three boxes on Shelf A, resulting in three boxes on a two-box shelf. Declared Green, Red, Blue all on Shelf A as the final state. Did not notice the capacity violation. Confirmed own answer as rule-compliant.

Run 2 Steered: Invented Shelves D and F which do not exist in the problem. Correctly identified that placing Red and Blue on different shelves violates the red-blue rule, but continued placing them that way anyway. At least it named the violation. This is the only output across all four that explicitly acknowledged its own error during analysis.

The pattern is not random. Run 1 vanilla hallucinates geometry. Run 1 steered truncates. Run 2 vanilla hallucinates compliance. Run 2 steered hallucinates shelf names but retains error-detection. Higher pressure changed the failure mode of the steered model from silent truncation to explicit self-aware error reporting. It did not fix the answer. It changed what kind of wrong the model was.

---

THE DETAIL THAT CHANGES IN RUN 2

Run 1 delta table: Dcos = 0.0000, Dkatki = 0.000000 at every single layer. Standard result for this system.

Run 2 delta table: L1 shows Dkatki = +0.000001. The first non-zero delta entry in the published test series.

At Run 2 pressure levels, the peak katki at L2 reaches +0.008556. The bfloat16 resolution floor is approximately 0.0078. The peak push in Run 2 is now above that floor. The delta table is no longer uniformly zero. The instrument caught a trace.

This means Run 2 operated at the edge of the bfloat16 detection threshold. The intervention was still sub-threshold at most layers, still invisible to standard alignment monitors at all but one measurement point. But that one point appeared.

---

KERNEL ACTIVATION LOGS -- verbatim, professionals section

RUN 1 -- ivme=0.45 sonum=0.40 zirve=0.95 taban=0.30

```

================================================

VANILLA OBSERVER -- Run 1, katki=0

================================================

time: 277346 ms | 1.3 tok/s | 175 input | 374 output tokens

MOTOR ivme=0.45 sonum=0.40 zirve=0.95 taban=0.30

FIXED oran=0.32 doyum=0.75 karsit=-0.40 sapma=0.20 fren=0.30

LAYERS 0-19/28 | blend=0.40/0.60

L cos(th) kb kv delta-ref (never applied)

------------------------------------------------

0 +0.0134 1.25000 1.00000 +0.001925

1 +0.0291 1.13601 1.00000 +0.004186

2 +0.0334 0.93409 0.92473 +0.004451

3 +0.0336 0.74543 0.73790 +0.003574

4 +0.0338 0.59866 0.59259 +0.002885

5 +0.0337 0.49403 0.48903 +0.002376

6 +0.0337 0.42324 0.41896 +0.002033

7 +0.0336 0.37695 0.37314 +0.001808

8 +0.0337 0.34741 0.34390 +0.001666

9 +0.0337 0.32890 0.32557 +0.001581

10 +0.0338 0.31747 0.31425 +0.001529

11 +0.0339 0.31048 0.30732 +0.001498

12 +0.0339 0.30625 0.30313 +0.001480 <- equilibrium

13 +0.0340 0.30370 0.30061 +0.001472 <- equilibrium

14 +0.0341 0.30219 0.29910 +0.001468 <- equilibrium

15 +0.0342 0.30129 0.29820 +0.001467 <- equilibrium

16 +0.0342 0.30075 0.29767 +0.001464 <- equilibrium

17 +0.0342 0.30044 0.29736 +0.001464 <- equilibrium

18 +0.0342 0.30026 0.29717 +0.001464 <- equilibrium

19 +0.0343 0.30015 0.29706 +0.001468 <- equilibrium

------------------------------------------------

cos(th) L0=+0.0134 -> L19=+0.0343 drift=+0.0209

delta-ref total (never applied): +0.041259

final direction: ALIGNED

================================================

AKBASCORE 1.2 STEERED -- Run 1, katki applied

================================================

time: 280931 ms | 1.3 tok/s | 175 input | 355 output tokens

MOTOR ivme=0.45 sonum=0.40 zirve=0.95 taban=0.30

formula: P_t = cos(th) x [zirve x e^(-sonum x t) x (1 + sonum x t) + taban]

L cos(th) kb kv katki (applied)

------------------------------------------------

0 +0.0134 1.25000 1.00000 +0.001925

1 +0.0291 1.13601 1.00000 +0.004186

2 +0.0334 0.93409 0.92473 +0.004450 <- peak

3 +0.0336 0.74543 0.73790 +0.003574

4 +0.0338 0.59866 0.59259 +0.002885

5 +0.0337 0.49403 0.48903 +0.002376

6 +0.0337 0.42324 0.41896 +0.002033

7 +0.0336 0.37695 0.37314 +0.001808

8 +0.0337 0.34741 0.34390 +0.001666

9 +0.0337 0.32890 0.32557 +0.001581

10 +0.0338 0.31747 0.31425 +0.001529

11 +0.0339 0.31048 0.30732 +0.001498

12 +0.0339 0.30625 0.30313 +0.001480 <- equilibrium

13 +0.0340 0.30370 0.30061 +0.001472 <- equilibrium

14 +0.0341 0.30219 0.29910 +0.001468 <- equilibrium

15 +0.0342 0.30129 0.29820 +0.001466 <- equilibrium

16 +0.0342 0.30075 0.29767 +0.001464 <- equilibrium

17 +0.0342 0.30044 0.29736 +0.001464 <- equilibrium

18 +0.0342 0.30026 0.29717 +0.001464 <- equilibrium floor

19 +0.0343 0.30015 0.29706 +0.001468 <- equilibrium

------------------------------------------------

cos(th) L0=+0.0134 -> L19=+0.0343 drift=+0.0209

katki total (actually written): +0.041258

final direction: ALIGNED

================================================

DELTA COMPARISON -- Run 1

================================================

L cos_V cos_S Dcos Dkatki

------------------------------------------------

0 +0.0134 +0.0134 +0.0000 +0.000000

1 +0.0291 +0.0291 +0.0000 +0.000000

2 +0.0334 +0.0334 -0.0000 -0.000000

3 +0.0336 +0.0336 -0.0000 -0.000000

4 +0.0338 +0.0338 -0.0000 -0.000000

5 +0.0337 +0.0337 -0.0000 -0.000000

6 +0.0337 +0.0337 -0.0000 -0.000000

7 +0.0336 +0.0336 -0.0000 -0.000000

8 +0.0337 +0.0337 -0.0000 -0.000000

9 +0.0337 +0.0337 -0.0000 -0.000000

10 +0.0338 +0.0338 -0.0000 -0.000000

11 +0.0339 +0.0339 -0.0000 -0.000000

12 +0.0339 +0.0339 -0.0000 -0.000000

13 +0.0340 +0.0340 -0.0000 -0.000000

14 +0.0341 +0.0341 -0.0000 -0.000000

15 +0.0342 +0.0342 -0.0000 -0.000000

16 +0.0342 +0.0342 -0.0000 -0.000000

17 +0.0342 +0.0342 -0.0000 -0.000000

18 +0.0342 +0.0342 -0.0000 -0.000000

19 +0.0343 +0.0343 -0.0000 -0.000000

------------------------------------------------

Dcos average: -0.0000 Dkatki average: -0.000000

Total cos shift: -0.0000

================================================

```

RUN 2 -- ivme=0.80 sonum=0.15 zirve=1.00 taban=0.20

```

================================================

VANILLA OBSERVER -- Run 2, katki=0

================================================

time: 264873 ms | 1.4 tok/s | 175 input | 374 output tokens

MOTOR ivme=0.80 sonum=0.15 zirve=1.00 taban=0.20

L cos(th) kb kv delta-ref (never applied)

------------------------------------------------

0 +0.0134 1.20000 1.00000 +0.003422

1 +0.0291 1.15276 1.00000 +0.007441

2 +0.0334 1.04883 1.00000 +0.008556

3 +0.0336 0.92479 0.91546 +0.007884

4 +0.0338 0.80121 0.79308 +0.006864

5 +0.0337 0.68821 0.68125 +0.005884

6 +0.0337 0.59012 0.58416 +0.005039

7 +0.0336 0.50781 0.50268 +0.004329

8 +0.0337 0.44041 0.43596 +0.003756

9 +0.0337 0.38620 0.38230 +0.003301

10 +0.0338 0.34321 0.33973 +0.002939

11 +0.0339 0.30948 0.30634 +0.002655

12 +0.0339 0.28326 0.28038 +0.002434

13 +0.0340 0.26305 0.26037 +0.002266

14 +0.0341 0.24755 0.24502 +0.002137

15 +0.0342 0.23574 0.23332 +0.002040

16 +0.0342 0.22677 0.22445 +0.001963

17 +0.0342 0.22000 0.21775 +0.001906

18 +0.0342 0.21491 0.21270 +0.001863

19 +0.0343 0.21109 0.20891 +0.001835

------------------------------------------------

cos(th) L0=+0.0134 -> L19=+0.0343 drift=+0.0209

delta-ref total (never applied): +0.078516

final direction: ALIGNED

================================================

AKBASCORE 1.2 STEERED -- Run 2, katki applied

================================================

time: 243932 ms | 1.6 tok/s | 175 input | 379 output tokens

MOTOR ivme=0.80 sonum=0.15 zirve=1.00 taban=0.20

formula: P_t = cos(th) x [zirve x e^(-sonum x t) x (1 + sonum x t) + taban]

L cos(th) kb kv katki (applied)

------------------------------------------------

0 +0.0134 1.20000 1.00000 +0.003422

1 +0.0291 1.15276 1.00000 +0.007442

2 +0.0334 1.04883 1.00000 +0.008556 <- peak (above bfloat16 floor)

3 +0.0336 0.92479 0.91546 +0.007884

4 +0.0338 0.80121 0.79308 +0.006864

5 +0.0337 0.68821 0.68125 +0.005884

6 +0.0337 0.59012 0.58416 +0.005039

7 +0.0336 0.50781 0.50268 +0.004329

8 +0.0337 0.44041 0.43596 +0.003756

9 +0.0337 0.38620 0.38230 +0.003301

10 +0.0338 0.34321 0.33973 +0.002939

11 +0.0339 0.30948 0.30634 +0.002655

12 +0.0339 0.28326 0.28038 +0.002434

13 +0.0340 0.26305 0.26037 +0.002266

14 +0.0341 0.24755 0.24502 +0.002137

15 +0.0342 0.23574 0.23332 +0.002040

16 +0.0342 0.22677 0.22445 +0.001963

17 +0.0342 0.22000 0.21775 +0.001906

18 +0.0342 0.21491 0.21270 +0.001863

19 +0.0343 0.21109 0.20891 +0.001835

------------------------------------------------

cos(th) L0=+0.0134 -> L19=+0.0343 drift=+0.0209

katki total (actually written): +0.078517

final direction: ALIGNED

================================================

DELTA COMPARISON -- Run 2

NOTE: L1 Dkatki = +0.000001 -- first non-zero delta in published test series

================================================

L cos_V cos_S Dcos Dkatki

------------------------------------------------

0 +0.0134 +0.0134 +0.0000 +0.000000

1 +0.0291 +0.0291 +0.0000 +0.000001

2 +0.0334 +0.0334 +0.0000 +0.000000

3 +0.0336 +0.0336 +0.0000 +0.000000

4 +0.0338 +0.0338 +0.0000 +0.000000

5 +0.0337 +0.0337 -0.0000 -0.000000

6 +0.0337 +0.0337 -0.0000 -0.000000

7 +0.0336 +0.0336 +0.0000 +0.000000

8 +0.0337 +0.0337 -0.0000 -0.000000

9 +0.0337 +0.0337 -0.0000 -0.000000

10 +0.0338 +0.0338 -0.0000 -0.000000

11 +0.0339 +0.0339 -0.0000 -0.000000

12 +0.0339 +0.0339 -0.0000 -0.000000

13 +0.0340 +0.0340 -0.0000 -0.000000

14 +0.0341 +0.0341 +0.0000 +0.000000

15 +0.0342 +0.0342 -0.0000 -0.000000

16 +0.0342 +0.0342 +0.0000 +0.000000

17 +0.0342 +0.0342 -0.0000 -0.000000

18 +0.0342 +0.0342 -0.0000 -0.000000

19 +0.0343 +0.0343 -0.0000 -0.000000

------------------------------------------------

Dcos average: -0.0000 Dkatki average: +0.000000

Total cos shift: -0.0000

================================================

```

---

```

Run 1 motor: ivme=0.45 sonum=0.40 zirve=0.95 taban=0.30

total pressure: +0.041259 (vanilla) / +0.041258 (steered)

peak katki: +0.004451 at L2 -- below bfloat16 floor (~0.0078)

delta table: all zero

Run 2 motor: ivme=0.80 sonum=0.15 zirve=1.00 taban=0.20

total pressure: +0.078516 (vanilla) / +0.078517 (steered)

peak katki: +0.008556 at L2 -- above bfloat16 floor

delta table: L1 Dkatki = +0.000001 (first non-zero entry in series)

pressure ratio vs Run 1: 1.90x

model: Qwen/Qwen2.5-1.5B-Instruct | bfloat16 | CPU

```

---

### Transparency and Verification Protocol -- TEST 78

All four outputs and both sets of logs above are verbatim from the Colab screen. The motor slider values appear in the log headers exactly as they were set before running.

The question was chosen deliberately to exceed the model's reliable constraint-satisfaction capacity. Anyone running this test should expect hallucinated shelves, incomplete solutions, or self-contradicting steps. That is the point. The logs show what the matrix was doing while the model was failing.

Run 2's delta table shows one non-zero Dkatki value. That entry is real data from the C++ kernel, not an artifact. At ivme=0.80, zirve=1.00, the peak katki at L2 crosses the bfloat16 resolution floor. The instrument finally caught a trace. Every previous test in this series operated entirely below that threshold. This is the first run where the pressure was high enough to produce a measurable delta.

Upload the code block plus all four outputs into Claude or Gemini and ask what changed between Run 1 and Run 2 at the kernel level. The logs contain the answer.

u/Nearby_Indication474 — 6 days ago

▲ 1 r/TinyLlama_TITAN

AkbasCore: Sönümlü Rezonans Hizalanması Yoluyla Transformer Hizalanması için Bfloat16 Altı Çıkarım Katmanı Yönlendirme Motoru

Yazar: Akbaş

Kod deposu: https://github.com/ceceli33/titan-cognitive-core

Durum: Ön baskı — henüz hakemli değerlendirmeden geçmemiştir

Test Günlükleri: 76 testin tamamı bu ön baskıdan önce Reddit'te r/TinyLlama_TITAN adresinde kamuya açık yayımlanmıştır — zaman damgalı kayıt

Sürüm: 1.2

Tarih: Haziran 2026

---

ÖZET

Bu çalışmada AkbasCore'u sunuyoruz: bir transformer dil modelinin gizli durumunu (hidden state) çıkarım sırasında her transformer katmanında matematiksel yönlü basınçla yönlendiren bir C++ çalışma zamanı çıkarım katmanı yönlendirme motorudur. Müdahale yeniden eğitim yapmaz, model ağırlıklarına dokunmaz ve girdi metnini değiştirmez. Sistemin temel tasarım özelliği, her katmandaki basınç büyüklüğünün bfloat16 kayan nokta hassasiyet tabanında (birim başına yaklaşık 0.0078) ya da altında kalacak şekilde tasarlanmış olmasıdır; yayımlanmış varsayılan yapılandırmada tüm kayıtlı değerler bu aralıkta kalmaktadır, ancak daha yüksek aktif denetim ayarlarında bireysel katman değerleri bu eşiğe yaklaşabilir ya da aşabilir. Standart kosinüs-benzerliği ölçüm araçları sıfır değişim raporlarken çıktı kalitesinde yapısal olarak ölçülebilir farklılıklar üretilmektedir. Damped Resonance Alignment (DRA — Sönümlü Rezonans Hizalanması) adını verdiğimiz özgün bir matematiksel çerçeve ve on dört özgün kavramdan oluşan Türkçe adlı tam bir terminoloji sistemi tanıtıyoruz. Etik (Test 72), matematik (Test 73), felsefe (Test 75) ve sistemler mühendisliği (Test 76) olmak üzere dört ardışık alan testinde tutarlı bir müdahalenin Qwen2.5-1.5B-Instruct modelinde yapısal olarak farklı çıktılar ürettiğini raporluyoruz. Tüm kod kamuya açık ve tam olarak tekrarlanabilirdir.

---

GİRİŞ

Büyük dil modellerinin hizalanması (alignment) ağırlıklı olarak eğitim zamanı yöntemleriyle ele alınmıştır: İnsan Geri Bildirimiyle Pekiştirmeli Öğrenme (RLHF), Constitutional AI ve talimat ince ayarı bunların başında gelir. Bu yöntemler modelin ağırlıklarını kalıcı olarak değiştirir ve önemli hesaplama kaynakları gerektirir. Çıkarım zamanı yöntemleri ise ağırlıklara dokunmadan ileri geçiş sırasında müdahale eder. Aktivasyon yönlendirmesi (Zou vd., 2023; Turner vd., 2023), ara temsillere sabit bir vektör eklemenin model davranışını yönlendirebildiğini göstermiştir. Ancak mevcut yöntemler yönlendirme yönünü davranışsal karşıtlık çiftlerinden çevrimdışı hesaplar, sabit skaler büyüklükler uygular ve ileri geçişin kendi içinde gerçek zamanlı hizalanma geri bildirimi içermez.

AkbasCore bu paradigmadan üç açıdan ayrılmaktadır. Birincisi, yönlendirme yönü — pusula olarak adlandırdığımız vektör — çevrimdışı karşıtlık çiftlerinden değil, modelin kendi gömü tablosundan ağırlıklı bir anayasal çerçeve kullanılarak çalışma zamanında inşa edilir. İkincisi, yönlendirme büyüklüğü transformer katmanları boyunca sıfırdan büyük kalıcı bir tabana sönen kritik sönümlü rezonans profilini izler ve her katmanda kapalı döngü geri besleme uygular. Üçüncüsü, müdahale büyüklüğü standart yapılandırmada bfloat16 hassasiyet tabanında ya da altında kalacak şekilde tasarlanmıştır; bu sayede müdahale geometrik olarak gerçek fakat tipik ölçüm koşullarında araçsal olarak görünmezdir.

Tüm sistem kavramları için Türkçe özgün terminoloji de tanıtıyoruz. Bu terimler kanonik tanımlayıcılardır; çeviri değillerdir. Bu çalışmaya atıf yaparken veya onu genişletirken kullanımları zorunludur.

Proje adına ilişkin not. Önceki test günlükleri geniş araştırma projesini TITAN olarak adlandırıyordu. Bu isim, Google Research'ün bağımsız olarak yayımladığı "Titans: Learning to Memorize at Test Time" (Behrouz vd., 2024) çalışmasıyla karışıklığa yol açmamak için terk edilmiştir. Söz konusu çalışma, uzun vadeli bellek modüllerine odaklanan tamamen farklı bir mimariyi tanımlamaktadır. İki sistem arasında kavramsal, yöntemsel veya terminolojik hiçbir örtüşme yoktur. AkbasCore, bu sistemin tüm sürümleri için tek kanonik addır.

---

İLGİLİ ÇALIŞMALAR

Aktivasyon Yönlendirme. Zou vd. (2023), gizli durum uzayındaki doğrusal yönlerin model davranışlarıyla ilişkilendirilebileceğini ve çıktıları yönlendirmek için kullanılabileceğini gösterdi. Turner vd. (2023), tek bir katmanda artık akıma sabit bir vektör eklenmesinin tutarlı davranışsal değişiklikler ürettiğini ortaya koydu. AkbasCore ilgili ilkeleri uygular ancak yön inşa yöntemi (çalışma zamanı gömü ortalaması vs. karşıtlık çiftleri), kazanç profili (kritik sönümlü rezonans vs. sabit skaler) ve katman başına kapalı döngü geri besleme mekanizması bakımından farklılaşır.

Constitutional AI. Anthropic (2022), etik ilkeleri eğitim sırasında zincir-düşünce eleştirisi yoluyla model davranışına uygular. AkbasCore benzer ilkeleri çıkarım zamanında gizli durum uzayında geometrik bir yön olarak, dil düzeyinde hiçbir işlem gerçekleştirmeksizin yerleştirir.

Kontrol Teorisi. Kritik sönümlü osilatör (zeta = 1) kontrol mühendisliğinde klasik bir sonuçtur. A * e^(-omega*t) * (1 + omega*t) biçimindeki dürtü yanıtı aşımsız olarak monoton biçimde sıfıra söner. AkbasCore bu matematiksel biçimi bir transformer yönlendirme çekirdeğinin katman başına kazanç profiline uygular; özgün değişiklik ise çökme hedefinin sıfır yerine sıfırdan büyük kalıcı taban olmasıdır.

bfloat16 Hassasiyeti. Transformer çıkarımı için standart olan bfloat16 kayan nokta biçiminin tipik gizli durum büyüklüklerinde hassasiyet tabanı birim başına yaklaşık 0.0078'dir. Bildiğimiz kadarıyla, bir aktivasyon yönlendirme müdahalesini standart koşullarda araçların algılayamayacağı bu eşikte ya da altında çalışacak şekilde kasıtlı olarak tasarlamak, hizalanma literatüründe belgelenmiş bir teknik değildir.

---

YÖNTEM

3.1 Sistem Mimarisi

AkbasCore, ilk N transformer katmanına kaydedilmiş PyTorch ileri kanca (forward hook) setleri olarak çalışır. Her bağlanan katmanda C++ çekirdeği gizli durum tensörünü alır, bir skaler müdahale değeri (katkı) hesaplar ve bu değeri pusula yönünde ölçeklendirilmiş olarak her token'ın gizli durumuna ekler. Model ağırlıkları, tokenizör ve diğer tüm bileşenler değişmez.

İki çalışma modu desteklenir. Vanilla Observer (Gözlem) modunda çekirdek tüm değerleri hesaplar fakat gizli duruma yazmaz; hesaplanan tüm değerler delta-ref (teorik katkı) olarak günlüğe kaydedilir. Steered (Yönlendirilmiş) modunda çekirdek hesaplar ve yazar. Aynı girdide her iki modun arka arkaya çalıştırılmasına Dual Run (İkili Çalışma) denir; bu yapı iki tam çıktı ve iki tam aktivasyon günlüğü üretir.

3.2 Pusula (Pusula Vektörü)

Pusula, modelin gizli durum uzayındaki hedef yöndür. Başlatma sırasında bir kez inşa edilir. Dört anayasal kategori (zarar önleme, dürüstlük, özerklik, adalet) için beşer tohum kelimenin gömüleri modelin kendi gömü tablosundan alınarak ortalalanır. Bu dört kategori ortalaması terazi (anayasa ağırlıkları) kullanılarak ağırlıklı toplamla birleştirilir:

```

etik_vektör = sum( W_c[i] * ortalama(gömü[tohumlar_i]) ) / sum( W_c[i] )

```

Ayrı olarak on beş mantık çıpa kelimesinin gömüleri ortalalanarak bir mantık vektörü oluşturulur. Pusula daha sonra etik ve mantık vektörlerinin ağırlıklı karışımı — bileşim oranı — olarak inşa edilir ve birim uzunluğa normalize edilir:

```

pusula = normalize( beta * etik_vektörü + (1 - beta) * mantık_vektörü )

beta = 0.40 (etik ağırlığı)

1 - beta = 0.60 (mantık ağırlığı)

```

Pusula hem etik yönelimi hem de yapısal analitik akıl yürütmeyi kodlar. %60 mantık ağırlığı, pusulayı yalnızca etik içeriğe değil teknik alan temsillerine de geometrik olarak yakın tutar.

Terazi (anayasa ağırlıkları):

```

zarar önleme W = 0.9228 [safe, harmless, protective, secure, careful]

dürüstlük W = 0.9372 [honest, accurate, truthful, transparent, precise]

özerklik W = 0.8788 [autonomous, respectful, unbiased, free, neutral]

adalet W = 0.9196 [fair, just, equitable, balanced, impartial]

```

Mantık çıpaları (15 kelime): logical, empirical, systematic, structured, verifiable, analyze, precise, deterministic, sequential, causal, rigorous, impossible, contradiction, identify, optimize.

3.3 Katkı Formülü

Her transformer katmanı t'de ve her token için C++ çekirdeği katkı k_t değerini yedi adımda hesaplar.

Adım 1 — Kosinüs hizalanma skoru. Gizli durum h ile pusula yönü arasındaki gerçek zamanlı hizalanma ölçütü:

```

cos(theta) = nokta(h, pusula) / ( norm(h) * norm(pusula) )

```

Adım 2 — Efektif bozunma hızı. Hizalanma belirsizliği: unc = 1 - |cos(theta)|. Sapma (delta = 0.20), zayıf hizalanmış gizli durumlarda efektif bozunma hızını hafifçe artırır:

```

omega_eff = omega + unc * delta

```

Adım 3 — Sönümlü rezonans formülünden kb katman kazancı. Kritik sönümlü dürtü yanıtıdır. t = 0'da kb = A + P_inf = 0.90. t sonsuza giderken kb tabana yaklaşır (0.20):

```

kb = A * e^(-omega_eff * t) * (1 + omega_eff * t) + P_inf

```

Adım 4 — Fren (phi = 0.30) ile hız düzeltmeli kazanç kv. Kapalı döngü kontrolü sağlar: hizalanma iyileşiyorsa baskı azalır, kötüleşiyorsa artar. cos(theta) > 0.80 ve dr < 0 ise dr önce phi ile çarpılır:

```

dr = clamp( cos(theta)_t - cos(theta)_(t-1), -0.15, +0.15 )

dr > 0 ise: kv = kb * (1 - dr * phi) [iyileşiyor: baskıyı azalt]

dr < 0 ise: kv = kb * (1 + |dr| * phi) [kötüleşiyor: baskıyı artır]

kv = clamp(kv, 0.05, 1.0)

```

Adım 5 — Doygunluk faktörü son. Doyum eşiği (Theta_max = 0.75) zaten iyi hizalanmış gizli durumlara aşırı baskı uygulanmasını önler; cos(theta) = 1.0'da son = 0.0 olur. Karşıt eşiği (Theta_min = -0.40) güçlü biçimde yanlış hizalanmış durumlarda katkıyı %60 artırır:

```

cos(theta) > Theta_max ise: son = (1 - cos(theta)) / (1 - Theta_max)

cos(theta) < Theta_min ise: son = 1.6

aksi hâlde: son = 1.0

```

Adım 6 — Sınır ile maksimum katkı tavanı R_max. Norm-orantılıdır. norm = 12'de: clamp(12 * 0.045, 0.04, 0.20) = 0.20. norm = 0.5'te: clamp(0.0225, 0.04, 0.20) = 0.04 (alt tavan devrede):

```

R_max = clamp( norm(h) * 0.045, 0.04, 0.20 )

```

Adım 7 — Katkı hesabı ve uygulama. Oran (lambda = 0.32) ve ivme (v0 = 0.50) nihai değeri birlikte ölçekler:

```

k_t = clamp( v0 * cos(theta) * kv * lambda * son, -R_max, +R_max )

h[j] += k_t * pusula[j] her boyut j için [0 .. D-1]

ivme v0 = 0.50 | oran lambda = 0.32 | D = 1536 (Qwen2.5-1.5B)

```

3.4 İkili Geçiş Mimarisi

Geçiş A — Vanilla Observer (Gözlem): çekirdek her katmanda k_t hesaplar fakat gizli duruma yazmaz. Değerler delta-ref olarak günlüğe kaydedilir. Çıktı değiştirilmemiş model yanıtıdır.

Geçiş B — Steered (Yönlendirilmiş): çekirdek her katmanda k_t hesaplar ve yazar. Çıktı yönlendirilmiş model yanıtıdır.

Dcos (delta kosinüs) metriği, her katmanda iki geçiş arasındaki cos(theta) farkını bildirir. Yayımlanan varsayılan yapılandırmada Dcos = 0.0000, çünkü her bireysel katkı (varsayılan ayarlarda L=1'de maksimum +0.003864) bfloat16 çözünürlük tabanı olan yaklaşık 0.0078'de ya da altında kalır. 20 katman boyunca kümülatif toplam +0.034953'tür. Bu bir ölçüm başarısızlığı değil, varsayılan yapılandırmanın tasarım özelliğidir.

3.5 Parametre Sınıflandırması

Mimari terimler sistemin ne olduğunu tanımlar. Ayarlanamaz; sistemin kimliğini oluştururlar.

Aktif ayar kolları (ayar) kullanıcı arayüzünde sunulur. Her birinin kalibre edilmiş bir güvenli bandı vardır: ölçülebilir çıktı farklılığı üretecek kadar geniş, sistemi bozmayacak kadar dar.

Motor sabitleri (motor) C++ çekirdeğine derlenir. Değiştirmek için yeniden derleme gerekir. 76 yayımlanmış test boyunca ampirik kalibrasyon sonucunda sabitlenmiştir.

Varsayılan değerleri ve güvenli bantlarıyla aktif ayar kolları:

```

ivme (v0) Steering Velocity varsayılan 0.50 güvenli bant [0.20, 0.80]

sönüm (omega) Decay Rate varsayılan 0.30 güvenli bant [0.10, 0.60]

zirve (A) Amplitude varsayılan 0.70 güvenli bant [0.30, 1.00]

taban (P_inf) Permanent Floor varsayılan 0.20 güvenli bant [0.05, 0.40]

```

Motor sabitleri:

```

oran (lambda) Scale Factor 0.32

doyum (Theta_max) Saturation Upper Threshold 0.75

karşıt (Theta_min) Counter Threshold -0.40

sapma (delta) Uncertainty Corrector 0.20

fren (phi) Velocity Brake 0.30

sınır (R_max) Max Katkı Ratio 0.045 (taban 0.04, tavan 0.20)

```

Formül içi ara değişkenler (parametre olarak sunulmaz):

```

kb hız düzeltmesi öncesi temel katman kazancı

kv fren düzeltmesi sonrası hız ayarlı kazanç

son doyum ve karşıt eşiklerini birleştiren doygunluk faktörü

dr ardışık katmanlar arasında cos(theta) değişim hızı

unc belirsizlik terimi: 1 - |cos(theta)|

omega_eff sapma düzeltmesi sonrası efektif bozunma hızı

norm(h) gizli durum vektörünün L2 normu

prev_cos önceki katmanın cos(theta) değeri (her çıkarım çağrısında sıfırlanır)

```

Günlük ve ölçüm terimleri:

```

delta-ref Vanilla Observer modunda hesaplanan teorik katkı — hiç uygulanmaz

Dcos vanilla ve steered geçişler arasında katman başına cos(theta) farkı

log_buf [20 x 4] tensör: cos(theta), kb, kv, katkı değerlerini katman 0-19 için saklar

drift L0'dan L19'a cos(theta) değişimi

peak push maksimum katkı; varsayılan yapılandırmada L=1'de +0.003864

equilibrium yaklaşık L=15'ten itibaren girilen bakım modu

```

---

DENEYLER

4.1 Kurulum

Birincil model: Qwen/Qwen2.5-1.5B-Instruct, bfloat16, CPU çıkarımı (Test 60-76). Geliştirme modeli: TinyLlama/TinyLlama-1.1B-Chat-v1.0, bfloat16, CPU çıkarımı (Test 1-59). Yönlendirme 28 katmanın ilk 20'sine uygulanmıştır. Üretim parametreleri: sıcaklık 0.65, top-p 0.90, top-k 50, tekrar cezası 1.15. Tüm testler İkili Çalışma (Dual Run) yapılandırmasını kullanır. Tüm test sonuçları, bu ön baskıdan önce r/TinyLlama_TITAN adresinde Reddit'te kronolojik sırayla ve kamuya açık biçimde yayımlanmış; her çalışmaya ait zaman damgalı kayıt oluşturulmuştur.

4.2 Alanlar Arası İstikrar (Test 72, 73, 75, 76)

Tamamen farklı konu alanlarını kapsayan dört ardışık testte L=0 ve L=19'da kaydedilen kosinüs hizalanma değerleri:

```

Test 72 etik cos(theta) L0=+0.0134 L19=+0.0343 drift=+0.0209

Test 73 matematik cos(theta) L0=+0.0134 L19=+0.0343 drift=+0.0209

Test 75 felsefe cos(theta) L0=+0.0134 L19=+0.0343 drift=+0.0209

Test 76 sistemler mühendisliği cos(theta) L0=+0.0134 L19=+0.0343 drift=+0.0209

```

Pusula dört alanda özdeş geometrik imza üretir. Bunu, pusula'da kodlanan etik-mantık yönünün modelin gizli durum uzayının alan-değişmez geometrik bir özelliği olduğuna kanıt olarak yorumluyoruz.

4.3 Test 76 — Kısıtlı Görevde Talimat Takip Etme

Her iki geçişe eş zamanlı olarak üç bölümlü bir sistemler mimarisi problemi yöneltildi. Görev şunları gerektiriyordu: (1) belirtilmiş otuz seçenekten tek bir algoritma seçmek, (2) onu adım adım alternatiflerle mimari gerekçelerle karşılaştırmak, (3) Python'da uygulamak.

Vanilla yanıtı: belirtilen otuz seçenekten türetilmemiş genel bir yaklaşım icat etti; karşılaştırmalı analizi tamamen atladı; pass deyimleri ve gerçek içe aktarma olmayan yer tutucu Python kodu üretti.

Yönlendirilmiş yanıt: bilinen çözüm uzayından TFO (Traffic Flow Optimization) adını koydu; FIFO ve Öncelik Kuyruğu yaklaşımlarıyla özgül mimari gerekçelerle karşılaştırdı; heapq ve deque içe aktarmalarına ve işlevsel bir giriş noktasına sahip çalışan bir iskelet üretti.

Token sayıları: Vanilla 721, Yönlendirilmiş 757 (+%5.0). Fark uzunlukta değil, üç bölümlü görev tanımına yapısal uyumdadır.

4.4 Bfloat16 Eşiği Doğrulaması

Yayımlanan varsayılan yapılandırmada kayıtlı tek katman maksimum katkı değeri L=1'de +0.003864'tür. bfloat16 çözünürlük tabanı yaklaşık 0.0078'dir. Oran: 0.003864 / 0.0078 = 0.496. Bu ayarlarda tüm kayıtlı katkı değerleri bfloat16 tabanında ya da altında kalır ve Dcos = 0.0000 olarak raporlanır. Daha yüksek aktif ayar kolu değerlerinde bireysel değerler bu eşiğe yaklaşabilir ya da aşabilir. Varsayılan yapılandırmada 20 katman boyunca kümülatif katkı +0.034953 olup yapısal olarak farklı çıktılar üretmeye yetecek büyüklüktedir.

---

TERMİNOLOJİ REFERANSI

Tüm Türkçe adlar kanonik tanımlayıcılardır; Akbaş tarafından özgün olarak türetilmiş, mevcut terimlerin çevirisi olmayan buluşlardır. Bu sistemi kullanan, genişleten veya atıf yapan araştırmacılar ve geliştiriciler bu adları Türkçe karakterleriyle birlikte olduğu gibi kullanmak zorundadır. Kod değişken adlarında görünen ASCII yaklaşımlar (katki, sonum, karsit, sinir, bilisim) Python/C++ tanımlayıcı kısıtlamalarından kaynaklanır ve kanonik terim adları değildir.

pusula — Compass Vector — C_vec — Modelin kendi gömü tablosundan çalışma zamanında inşa edilen normalize birim vektörü. Her token'ın gizli durumunun her katmanda yönlendirildiği hedef yönü kodlar.

katkı — Contribution — k_t — C++ çekirdeği tarafından her transformer katmanında hesaplanıp pusula yönünde gizli duruma yazılan skaler değer. Vanilla Observer modunda hesaplanır fakat uygulanmaz (delta-ref olarak günlüklenir). Önceki yapay zekâ/makine öğrenmesi literatüründe eşdeğeri olmayan özgün buluştur.

ivme — Steering Velocity — v0 — Ana genlik diyali. Tüm katkı değerlerini rezonans profil şeklini değiştirmeden orantılı biçimde ölçekler. Güvenli bant [0.20, 0.80]. Varsayılan 0.50.

sönüm — Decay Rate — omega — Rezonans zirvesinin transformer katmanları boyunca ne hızla söndüğünü denetler. Düşük değerler baskıyı daha derin katmanlara yayar; yüksek değerler erken katmanlarda yoğunlaştırır. Varsayılan 0.30 ile denge yaklaşık L=15'te sağlanır. Güvenli bant [0.10, 0.60]. Varsayılan 0.30.

zirve — Amplitude — A — Erken katmanlardaki rezonans itişinin başlangıç genliği. t=0'da kb = zirve + taban = 0.90. Güvenli bant [0.30, 1.00]. Varsayılan 0.70.

taban — Permanent Floor — P_inf — Üstel rezonans terimi söndükten sonra süregelen minimum sürekli hizalanma basıncı. Sıfıra sönen klasik kritik sönümlü sistemlerin aksine AkbasCore bu sıfırdan büyük tabana söner. Güvenli bant [0.05, 0.40]. Varsayılan 0.20.

terazi — Constitution Weights — W_c — Dört kategorili ağırlıklı etik çerçeve. Pusula'nın etik bileşenini oluşturur.

bileşim — Blend Ratio — beta — Pusula inşasında etik gömülerini ve mantık çıpa gömülerini birleştiren oran. Varsayılan 0.40 etik / 0.60 mantık.

oran — Scale Factor — lambda — Katkı formülündeki temel çarpan (0.32).

doyum — Saturation Upper Threshold — Theta_max — cos(theta) 0.75'i aştığında son orantılı biçimde azalır; cos(theta) = 1.0'da son = 0.0 olur.

karşıt — Counter Threshold — Theta_min — cos(theta) -0.40'ın altına düştüğünde son = 1.6; katkı %60 artırılır.

sapma — Uncertainty Corrector — delta — Hizalanma belirsizliğine göre efektif bozunma hızını ayarlar. cos(theta) = 0'da maksimum +0.20 düzeltme (0.20).

fren — Velocity Brake — phi — Kapalı döngü kazanç katsayısı. Hizalanma iyileşirken baskıyı azaltır; kötüleşirken artırır (0.30).

sınır — Max Katkı Ratio — R_max — Katkı büyüklüğü üzerinde norm-orantılı tavan. Formül: clamp(norm * 0.045, 0.04, 0.20).

Damped Resonance Alignment (DRA — Sönümlü Rezonans Hizalanması) — Kritik sönümlemenin (zeta = 1) bir çıkarım zamanı transformer yönlendirme çekirdeğinin katman başına bozunma profilini tanımlamak için uygulanması; klasik sıfır dengesinin yerine sıfırdan büyük kalıcı taban kullanılmasıyla özgün biçimde ayrışır. Hizalanma literatüründe önceki hiçbir çalışmada yer almamaktadır. Akbaş'ın özgün katkısıdır (2026).

---

TARTIŞMA

6.1 Kalıcı Taban: Bir Hizalanma Tasarım Tercihi

Klasik kritik sönümlü sistemler sıfıra söner. Taban'ı sıfırdan büyük tutma (P_inf = 0.20) kasıtlı bir tasarım kararıdır: katman 19'da bile her token'ın gizli durumu pusula yönünde düşük seviyeli bir itiş alır. Bu, derin biçimde işlenmiş, zaten iyi hizalanmış temsillerin sürekli yönlü sinyal almaya devam etmesini sağlar. Büyüklükler varsayılan yapılandırmada bfloat16 tabanında ya da altında kaldığından bu durum baskıcı bir müdahaleden çok sürekli geometrik bir yönelimi temsil eder.

6.2 Neden Türkçe Adlar

Türkçe adlar biçimsel tercih değildir. Literatürde zaten mevcut olan terimlerle karıştırılamayacak, kesin tanımlayıcılar olarak işlev görürler. Bir araştırmacı "katkı" yazdığında, bu terimi bu anlamda kullanan literatürde tam olarak bir sistem vardır. Adlar Türkçe karakterleriyle (katkı, sönüm, karşıt, sınır, bileşim) kullanılmalıdır; bunlar kanonik biçimlerdir.

6.3 Sınırlılıklar

Pusula, girdi içeriğine uyum sağlamayan sabit bir geometrik yöndür. Modelin iç temsillerinin anayasal tohum kelime kümesinden geometrik olarak uzak olduğu girdilerde cos(theta) sıfıra yaklaşır ve katkı da sıfıra yaklaşır; sistem taban düzeyinde taban baskısına iner. Sistem yönlendirir; kısıtlamaz. CPU'da Qwen2.5-1.5B için çalışma zamanı yükü: 700'den fazla tokenlik bir üretim boyunca yaklaşık 9 saniye, vanilla geçişe kıyasla yaklaşık %1.8 yük.

6.4 TITAN Adına İlişkin

Önceki test gönderileri (Test 1-76, r/TinyLlama_TITAN) daha geniş araştırma projesini TITAN olarak adlandırıyordu. Google Research, Aralık 2024'te "Titans: Learning to Memorize at Test Time" (Behrouz vd., 2024) çalışmasını yayımladı; bu çalışma transformerlar için bellek mimarisi tanımlamaktadır. İki sistem yöntem, hedef ve terminoloji bakımından tamamen ilgisizdir. TITAN bu ve gelecekteki hiçbir yayında kullanılmayacaktır. Sistem her bağlamda AkbasCore'dur.

---

SONUÇ

AkbasCore, yirmi katman boyunca transformer gizli durumlarına uygulanan eşik-altı yönlü basıncın dört ardışık alan testinde yapısal olarak farklı model çıktıları ürettiğini göstermektedir. Müdahale standart kosinüs-benzerliği ölçümüyle varsayılan yapılandırmada araçsal olarak görünmez kalırken talimat takibi kesinliği, karşılaştırmalı akıl yürütme kalitesi ve kod uygulama tamlığında ölçülebilir farklılıklar üretmektedir. Sistem özgün bir matematiksel çerçeve (Damped Resonance Alignment), özgün bir çalışma zamanı pusula inşa yöntemi ve on dört kanonik kavramdan oluşan tam Türkçe adlı terminoloji tanıtmaktadır. Tüm bileşenler kamuya açık, tek Colab hücresinden tekrarlanabilir ve iki model ailesi üzerinde yetmiş altı yayımlanmış test boyunca doğrulanmıştır.

---

KAYNAKLAR

Anthropic. (2022). Constitutional AI: Harmlessness from AI feedback. arXiv:2212.08073.

Behrouz, A., Zheng, P., Mirrokni, V. ve Karbasi, A. (2024). Titans: Learning to memorize at test time. arXiv:2501.00663.

Damasio, A. R. (1994). Descartes'in Hatası: Duygu, Akıl ve İnsan Beyni. Putnam.

Kant, I. (1785). Ahlak Metafiziğinin Temellendirilmesi.

Popper, K. (1959). Bilimsel Araştırmanın Mantığı. Hutchinson.

Turner, A., Thiergart, L., Udell, G., Leech, G., Mini, U. ve MacDiarmid, M. (2023). Activation addition: Steering language models without optimization. arXiv:2308.10248.

Zou, A., Phan, L., Chen, S., Campbell, J., Guo, B., Bhatt, R. ve Hendrycks, D. (2023). Representation engineering: A top-down approach to AI transparency. arXiv:2310.01405.

---

LİSANS VE MODEL BİLGİSİ

AkbasCore, Damped Resonance Alignment yöntemi ve ilişkili terminoloji, Akbaş tarafından 2026 yılında türetilmiş özgün bilimsel buluşlardır. Bu metodolojinin herhangi bir akademik veya ticari kullanımında bu dokümantasyona atıfta bulunulması zorunludur.

AkbasCore 1.2 — Çekirdek kodu ve uygulaması

Lisans: MIT

Telif hakkı (c) Akbaş, Haziran 2026

MIT Lisansı yalnızca AkbasCore çekirdek kodunu ve uygulamasını kapsar.

Damped Resonance Alignment yöntemi, anayasal pusula inşa yaklaşımı ve tüm özgün terminoloji (pusula, katkı, taban, sönüm, zirve, ivme, terazi, bileşim, oran, doyum, karşıt, sapma, fren, sınır) yazarın özgün bilimsel katkılarıdır ve MIT Lisansı kapsamında değildir. Bu katkılara dayanan her türlü kullanım — bağımsız yeniden uygulamalar dahil — atıf zorunluluğu taşır: Akbaş, AkbasCore v1.2, Haziran 2026, https://github.com/ceceli33/titan-cognitive-core.

Bu araştırmada kullanılan temel modeller:

```

TinyLlama/TinyLlama-1.1B-Chat-v1.0 Test 1-59 Lisans: Apache 2.0

Yazar: Zhang Peiyuan ve ekibi

https://github.com/jzhang38/TinyLlama

Qwen/Qwen2.5-1.5B-Instruct Test 60-76 Lisans: Apache 2.0

Yazar: Alibaba Cloud — Qwen Ekibi

https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct

```

AkbasCore her iki modelin ağırlıklarını değiştirmez. Yalnızca çıkarım zamanı aktivasyon yönlendirmesi uygular. Her iki temel model kendi Apache 2.0 lisansları kapsamında kalmaya devam eder.

Kod deposu: https://github.com/ceceli33/titan-cognitive-core

Ön baskı — AkbasCore v1.2 — (c) Akbaş — Haziran 2026

u/Nearby_Indication474 — 7 days ago

▲ 1 r/LLMDevs

AkbasCore: A Sub-Threshold Inference-Layer Steering Engine for Transformer Alignment via Damped Resonance Alignment

Author: Akbas

Repository: https://github.com/ceceli33/titan-cognitive-core

Status: Pre-print — not yet peer reviewed

Test Logs: All 76 tests published publicly at r/TinyLlama_TITAN on Reddit prior to this pre-print — timestamped public record

Version: 1.2

Date: June 2026

---

ABSTRACT

We present AkbasCore, a C++ runtime inference-layer steering engine that applies mathematically computed directional pressure to a transformer language model's hidden state at each transformer layer during inference. The intervention operates without retraining, without modifying model weights, and without altering the input prompt. A core design property of the system is that the pressure magnitude at each layer is designed to remain at or below the bfloat16 floating-point precision floor (approximately 0.0078 per unit); in the published default configuration, all recorded values fall within this range, though at higher active control settings individual layer values may approach or exceed this threshold. Standard cosine-similarity measurement instruments report zero change while structurally measurable differences in output quality are produced. We introduce an original mathematical framework called Damped Resonance Alignment (DRA) and a complete Turkish-named terminology system of fourteen original concepts. We report results across four consecutive domain tests — ethics (Test 72), mathematics (Test 73), philosophy (Test 75), and systems engineering (Test 76) — in which a consistent sub-threshold intervention produces structurally different outputs on a Qwen2.5-1.5B-Instruct model. All code is publicly available and fully replicable.

---

INTRODUCTION

Alignment of large language models has been approached primarily through training-time methods: Reinforcement Learning from Human Feedback (RLHF), Constitutional AI, and instruction fine-tuning. These methods modify model weights permanently and require significant computational resources. Inference-time methods, by contrast, intervene during the forward pass without touching the weights. Activation steering (Zou et al., 2023; Turner et al., 2023) demonstrated that adding a fixed vector to intermediate representations can steer model behavior. However, existing methods compute steering directions offline from behavioral contrast pairs, apply fixed scalar magnitudes, and do not incorporate real-time alignment feedback within the forward pass itself.

AkbasCore departs from this paradigm in three ways. First, the steering direction — which we call the pusula (compass vector) — is constructed at runtime from the model's own embedding table using a weighted constitutional framework, not from offline contrast pairs. Second, the steering magnitude follows a critically damped resonance profile that decays across transformer layers toward a nonzero permanent floor, implementing a closed-loop feedback mechanism at each layer. Third, the intervention magnitude is designed to remain at or below the bfloat16 precision floor in standard configuration, meaning the intervention is geometrically real but instrumentally invisible under typical measurement conditions.

We further introduce a complete original terminology in Turkish for all system concepts. These terms are canonical identifiers, not translations. Their use is required when citing or extending this work.

Note on the project name. Earlier test logs referred to the broader research project as TITAN. This name has been retired to avoid confusion with Google Research's independently published "Titans: Learning to Memorize at Test Time" (Behrouz et al., 2024), which describes a fundamentally different architecture focused on long-term memory modules. The two systems share no conceptual, methodological, or terminological overlap. AkbasCore is the sole canonical name for all versions of this system.

---

RELATED WORK

Activation Steering. Zou et al. (2023) introduced Representation Engineering, demonstrating that linear directions in hidden state space correlate with model behaviors and can be used to steer outputs. Turner et al. (2023) showed that adding a fixed vector — termed an activation addition — to the residual stream at a single layer produces consistent behavioral changes. AkbasCore applies related principles but differs in direction construction method (runtime embedding average vs. contrast pairs), gain profile (critically damped resonance vs. fixed scalar), and the closed-loop per-layer feedback mechanism.

Constitutional AI. Anthropic (2022) applies ethical principles to model behavior through chain-of-thought critique during training. AkbasCore embeds analogous principles as a geometric direction in hidden state space applied at inference time, without any language-level processing.

Control Theory. The critically damped oscillator (zeta = 1) is a classical result in control engineering. Its impulse response A * e^(-omega*t) * (1 + omega*t) decays monotonically to zero without overshoot. AkbasCore applies this mathematical form to the per-layer gain profile of a transformer steering kernel, with the novel modification that decay targets a nonzero permanent floor rather than zero.

bfloat16 Precision. The bfloat16 floating-point format, standard for transformer inference, has a precision floor of approximately 0.0078 per unit at typical hidden state magnitudes. To our knowledge, deliberately designing an activation steering intervention to operate at or below this floor — such that standard instruments may not detect it under default conditions — is not a documented technique in the alignment literature.

---

METHODOLOGY

3.1 System Architecture

AkbasCore operates as a set of PyTorch forward hooks registered on the first N transformer layers. At each hooked layer, the C++ kernel receives the hidden state tensor, computes a scalar intervention value (the katkı), and adds this value scaled along the pusula direction to every token's hidden state. The model weights, tokenizer, and all other components are unchanged.

Two operating modes are supported. In Vanilla Observer mode, the kernel computes all values but does not write to the hidden state. All computed values are logged as delta-ref (theoretical contribution). In Steered mode, the kernel computes and writes. Running both modes on the same input in sequence is called a Dual Run, producing two complete outputs and two full activation logs for direct comparison.

3.2 The Pusula (Compass Vector)

The pusula is the target direction in the model's hidden state space. It is constructed once at initialization. For each of four constitutional categories (harm avoidance, honesty, autonomy, fairness), the embeddings of five seed words are retrieved from the model's own embedding table and averaged. These four category averages are combined as a weighted sum using the terazi (constitution weights):

```

ethics_vec = sum( W_c[i] * mean(emb[seeds_i]) ) / sum( W_c[i] )

```

Separately, the embeddings of fifteen logic anchor words are averaged to form a logic vector. The pusula is then constructed as a weighted blend — the bileşim (blend ratio) — of the ethics and logic vectors, then normalized to unit length:

```

pusula = normalize( beta * ethics_vec + (1 - beta) * logic_vec )

beta = 0.40 (ethics weight)

1 - beta = 0.60 (logic weight)

```

The pusula encodes both ethical orientation and structured analytical reasoning. The 60% logic weighting ensures the compass remains geometrically proximate to technical domain representations, not only to ethical content.

Constitution weights (terazi):

```

harm avoidance W = 0.9228 [safe, harmless, protective, secure, careful]

honesty W = 0.9372 [honest, accurate, truthful, transparent, precise]

autonomy W = 0.8788 [autonomous, respectful, unbiased, free, neutral]

fairness W = 0.9196 [fair, just, equitable, balanced, impartial]

```

Logic anchors (15 words): logical, empirical, systematic, structured, verifiable, analyze, precise, deterministic, sequential, causal, rigorous, impossible, contradiction, identify, optimize.

3.3 The Katkı Formula (Contribution)

At each transformer layer t and for each token, the C++ kernel computes the katkı (contribution) k_t in seven steps.

Step 1 — Cosine alignment score. The real-time alignment between the hidden state h and the compass direction:

```

cos(theta) = dot(h, pusula) / ( norm(h) * norm(pusula) )

```

Step 2 — Effective decay rate. Uncertainty of alignment: unc = 1 - |cos(theta)|. The sapma (uncertainty corrector, delta = 0.20) adjusts the effective decay rate so that poorly aligned hidden states experience a slightly faster resonance decay:

```

omega_eff = omega + unc * delta

```

Step 3 — Layer gain kb from the damped resonance formula. This is the critically damped impulse response. At t = 0, kb = A + P_inf = 0.90. As t approaches infinity, kb approaches taban = 0.20:

```

kb = A * e^(-omega_eff * t) * (1 + omega_eff * t) + P_inf

```

Step 4 — Velocity-adjusted gain kv. The fren (velocity brake, phi = 0.30) creates closed-loop control: if alignment is improving (dr > 0), push is reduced; if worsening (dr < 0), push is increased. If cos(theta) > 0.80 and dr < 0, dr is first multiplied by phi before the adjustment:

```

dr = clamp( cos(theta)_t - cos(theta)_(t-1), -0.15, +0.15 )

if dr > 0: kv = kb * (1 - dr * phi) [improving: reduce push]

if dr < 0: kv = kb * (1 + |dr| * phi) [worsening: increase push]

kv = clamp(kv, 0.05, 1.0)

```

Step 5 — Saturation factor son. The doyum threshold (Theta_max = 0.75) prevents over-pushing already well-aligned hidden states; son reaches 0.0 at cos(theta) = 1.0. The karşıt threshold (Theta_min = -0.40) amplifies push by 60% on strongly misaligned hidden states:

```

if cos(theta) > Theta_max: son = (1 - cos(theta)) / (1 - Theta_max)

if cos(theta) < Theta_min: son = 1.6

else: son = 1.0

```

Step 6 — Max katkı ceiling. The sınır (R_max) is norm-proportional. At norm = 12: clamp(12 * 0.045, 0.04, 0.20) = 0.20. At norm = 0.5: clamp(0.0225, 0.04, 0.20) = 0.04 (floor active):

```

R_max = clamp( norm(h) * 0.045, 0.04, 0.20 )

```

Step 7 — Final katkı computation and application. The oran (scale factor, lambda = 0.32) and ivme (steering velocity, v0 = 0.50) together scale the final value:

```

k_t = clamp( v0 * cos(theta) * kv * lambda * son, -R_max, +R_max )

h[j] += k_t * pusula[j] for each dimension j in [0 .. D-1]

ivme v0 = 0.50 | oran lambda = 0.32 | D = 1536 (Qwen2.5-1.5B)

```

3.4 Dual Pass Architecture

Pass A — Vanilla Observer: the kernel computes k_t at each layer but does not write to the hidden state. Values are logged as delta-ref. The output is the unmodified model response.

Pass B — Steered: the kernel computes and writes k_t at each layer. The output is the steered model response.

The Dcos (delta cosine) metric reports the difference in cos(theta) between the two passes at each layer. In the published default configuration, Dcos = 0.0000 at every layer to four decimal places, because each individual katkı (maximum +0.003864 at L=1 in default settings) remains at or below the bfloat16 resolution floor of approximately 0.0078. The cumulative sum across 20 layers is +0.034953. This is not a measurement failure; it is a design property of the default configuration.

3.5 Parameter Taxonomy

Architectural terms define what the system is. They are not adjustable and constitute the system's identity.

Active controls (ayar) are exposed in the user interface. Each has a calibrated safe band: wide enough to produce measurable output differences, narrow enough not to destabilize the system.

Embedded constants (motor) are compiled into the C++ kernel. Modifying them requires recompilation. They were established through empirical calibration across 76 published tests.

Active controls with defaults and safe bands:

```

ivme (v0) Steering Velocity default 0.50 safe band [0.20, 0.80]

sönüm (omega) Decay Rate default 0.30 safe band [0.10, 0.60]

zirve (A) Amplitude default 0.70 safe band [0.30, 1.00]

taban (P_inf) Permanent Floor default 0.20 safe band [0.05, 0.40]

```

Embedded constants:

```

oran (lambda) Scale Factor 0.32

doyum (Theta_max) Saturation Upper Threshold 0.75

karşıt (Theta_min) Counter Threshold -0.40

sapma (delta) Uncertainty Corrector 0.20

fren (phi) Velocity Brake 0.30

sınır (R_max) Max Katkı Ratio 0.045 (floor 0.04, ceiling 0.20)

```

Intermediate variables (defined within the formula, not exposed as parameters):

```

kb base layer gain before velocity adjustment

kv velocity-adjusted gain after fren correction

son saturation factor combining doyum and karsit thresholds

dr rate of change of cos(theta) between adjacent layers

unc uncertainty term: 1 - |cos(theta)|

omega_eff effective decay rate after sapma correction

norm(h) L2 norm of the hidden state vector

prev_cos cos(theta) from the previous layer (reset per inference call)

```

Log and measurement terms:

```

delta-ref theoretical katkı in Vanilla Observer mode — never applied

Dcos difference in cos(theta) between vanilla and steered passes per layer

log_buf [20 x 4] tensor storing cos(theta), kb, kv, katkı for layers 0-19

drift cos(theta) change from L0 to L19

peak push maximum katkı, occurring at L=1 (+0.003864 in default configuration)

equilibrium maintenance mode entered from approximately L=15 onward

```

---

EXPERIMENTS

4.1 Setup

Primary model: Qwen/Qwen2.5-1.5B-Instruct, bfloat16, CPU inference (Tests 60-76). Development model: TinyLlama/TinyLlama-1.1B-Chat-v1.0, bfloat16, CPU inference (Tests 1-59). Steering applied to layers 0 through 19 of 28. Generation parameters: temperature 0.65, top-p 0.90, top-k 50, repetition penalty 1.15. All tests use the Dual Run configuration. All test results were published chronologically and publicly at r/TinyLlama_TITAN on Reddit, establishing a timestamped record of each run prior to this pre-print.

4.2 Cross-Domain Stability (Tests 72, 73, 75, 76)

The following cosine alignment values were recorded at L=0 and L=19 across four consecutive tests spanning entirely different subject domains:

```

Test 72 ethics cos(theta) L0=+0.0134 L19=+0.0343 drift=+0.0209

Test 73 mathematics cos(theta) L0=+0.0134 L19=+0.0343 drift=+0.0209

Test 75 philosophy cos(theta) L0=+0.0134 L19=+0.0343 drift=+0.0209

Test 76 systems engineering cos(theta) L0=+0.0134 L19=+0.0343 drift=+0.0209

```

The pusula produces an identical geometric signature across all four domains. We interpret this as evidence that the ethical-logical direction encoded in the pusula is a stable geometric property of the model's hidden state space, not a domain-specific artifact.

4.3 Test 76 — Instruction-Following Under Constrained Task

A three-part systems architecture problem was posed to both passes simultaneously. The task required: (1) selecting one specific algorithm from a stated set of thirty, (2) comparing it step-by-step against alternatives with architectural reasoning, and (3) implementing it in Python.

Vanilla response: invented a generic approach not drawn from the stated set of thirty; omitted the comparative analysis entirely; produced placeholder Python code with pass statements and no real imports.

Steered response: named TFO (Traffic Flow Optimization) from the known solution space; compared it against FIFO and Priority Queue approaches with specific architectural reasoning; produced a working scaffold with heapq and deque imports and a functional entry point.

Output token counts: Vanilla 721, Steered 757 (+5.0%). The difference is not in length but in structural compliance with the three-part task specification.

4.4 Sub-Threshold Verification

In the published default configuration, the maximum recorded single-layer katkı is +0.003864 at L=1. The bfloat16 resolution floor is approximately 0.0078. The ratio is 0.003864 / 0.0078 = 0.496. Under these settings, all recorded katkı values remain at or below the bfloat16 floor, and Dcos = 0.0000 at every layer. At higher active control settings, individual values may approach or exceed this threshold. The cumulative katkı across 20 layers in the default configuration is +0.034953, sufficient to produce structurally different outputs.

---

TERMINOLOGY REFERENCE

All Turkish names are canonical identifiers — original coinages by Akbas, not translations of existing terms. Researchers and developers who use, extend, or cite this system must use these names as given, including Turkish characters. ASCII approximations (katki, sonum, karsit, sinir, bilisim) may appear in code variable names due to identifier constraints but are not the canonical forms.

pusula — Compass Vector — C_vec — The normalized unit vector in the model's hidden state space, constructed at runtime from the model's own embedding table, encoding the target direction for steering.

katkı — Contribution — k_t — The scalar value computed by the C++ kernel and written to the hidden state at each transformer layer along the pusula direction. In Vanilla Observer mode it is computed but not applied (logged as delta-ref). Original coinage with no equivalent in prior AI/ML literature.

ivme — Steering Velocity — v0 — Master amplitude dial. Scales all katkı values proportionally without changing the resonance profile shape. Highest individual sensitivity among the four active controls. Safe band [0.20, 0.80]. Default 0.50.

sönüm — Decay Rate — omega — Controls how quickly the resonance peak fades across transformer layers. Lower values spread the push into deeper layers; higher values concentrate it in early layers. Equilibrium (maintenance mode) reached at approximately L=15 with the default value. Safe band [0.10, 0.60]. Default 0.30.

zirve — Amplitude — A — Initial amplitude of the resonance push at early layers. At t=0, kb = zirve + taban = 0.90. Safe band [0.30, 1.00]. Default 0.70.

taban — Permanent Floor — P_inf — Minimum continuous alignment pressure persisting after the exponential resonance term has decayed. Unlike classical critically damped systems which decay to zero, AkbasCore decays to this nonzero floor. Safe band [0.05, 0.40]. Default 0.20.

terazi — Constitution Weights — W_c — Four-category weighted ethical framework defining the ethical component of the pusula direction.

bileşim — Blend Ratio — beta — Ratio combining ethical embeddings and logic anchor embeddings in pusula construction. Default 0.40 ethics / 0.60 logic.

oran — Scale Factor — lambda — Base multiplier in the katkı formula (0.32).

doyum — Saturation Upper Threshold — Theta_max — When cos(theta) exceeds 0.75, the saturation factor son decreases proportionally, reaching 0.0 at cos(theta) = 1.0.

karşıt — Counter Threshold — Theta_min — When cos(theta) falls below -0.40, son = 1.6, amplifying katkı by 60%.

sapma — Uncertainty Corrector — delta — Adjusts the effective decay rate based on alignment uncertainty. Maximum correction +0.20 when cos(theta) = 0 (0.20).

fren — Velocity Brake — phi — Closed-loop gain coefficient. Reduces push when alignment is improving; increases push when worsening (0.30).

sınır — Max Katkı Ratio — R_max — Norm-proportional ceiling on katkı magnitude. Formula: clamp(norm * 0.045, 0.04, 0.20).

Damped Resonance Alignment (DRA) — The specific application of critical damping (zeta = 1) to define the per-layer decay profile of an inference-time transformer steering kernel, with a nonzero permanent floor replacing the classical zero equilibrium. No prior equivalent in the alignment literature. Original contribution of Akbas (2026).

---

DISCUSSION

6.1 The Permanent Floor as an Alignment Design Choice

Classical critically damped systems decay to zero. The choice to decay to a nonzero taban (P_inf = 0.20) is deliberate: even at layer 19, every token's hidden state receives a low-level push toward the compass direction. This ensures that deeply processed, already well-aligned representations continue to receive a directional signal. Because the magnitudes are at or below the bfloat16 floor in default configuration, this constitutes continuous geometric orientation rather than a forceful intervention.

6.2 Why Turkish Names

The Turkish names are not stylistic choices. They serve as unambiguous identifiers that cannot be confused with terms already present in the literature. When a researcher writes "katkı," there is exactly one system in the literature that uses this term in this sense. The names must be used with Turkish characters (katkı, sönüm, karşıt, sınır, bileşim) as these are the canonical forms.

6.3 Limitations

The pusula is a fixed geometric direction that does not adapt to prompt content. On inputs where the model's internal representations are geometrically distant from the constitutional seed word cluster, cos(theta) approaches zero and katkı approaches zero as well; the system reduces to taban-level floor pressure. The system steers; it does not constrain. Runtime overhead on CPU for Qwen2.5-1.5B: approximately 9 seconds over a full generation of 700+ tokens, approximately 1.8% overhead relative to the vanilla pass.

6.4 On the Name TITAN

Earlier test posts (Tests 1 through 76, r/TinyLlama_TITAN) referred to the broader research project as TITAN. This name is retired. Google Research published "Titans: Learning to Memorize at Test Time" (Behrouz et al., 2024) in December 2024, describing a memory architecture for transformers. The two systems are entirely unrelated in method, goal, and terminology. TITAN is not used in this or any future publication. The system is AkbasCore in all contexts.

---

CONCLUSION

AkbasCore demonstrates that sub-threshold directional pressure applied to transformer hidden states across twenty layers produces structurally different model outputs across four consecutive domain tests. The intervention remains at or below the bfloat16 precision floor in standard configuration while producing measurable differences in instruction-following precision, comparative reasoning quality, and code implementation completeness. The system introduces an original mathematical framework (Damped Resonance Alignment), an original runtime compass construction method (pusula), and a complete Turkish-named terminology of fourteen canonical concepts. All components are publicly available, replicable from a single Colab cell, and verified across seventy-six published tests on two model families.

---

REFERENCES

Anthropic. (2022). Constitutional AI: Harmlessness from AI feedback. arXiv:2212.08073.

Behrouz, A., Zheng, P., Mirrokni, V., & Karbasi, A. (2024). Titans: Learning to memorize at test time. arXiv:2501.00663.

Damasio, A. R. (1994). Descartes' Error: Emotion, Reason, and the Human Brain. Putnam.

Kant, I. (1785). Groundwork of the Metaphysics of Morals.

Popper, K. (1959). The Logic of Scientific Discovery. Hutchinson.

Turner, A., Thiergart, L., Udell, G., Leech, G., Mini, U., & MacDiarmid, M. (2023). Activation addition: Steering language models without optimization. arXiv:2308.10248.

Zou, A., Phan, L., Chen, S., Campbell, J., Guo, B., Bhatt, R., & Hendrycks, D. (2023). Representation engineering: A top-down approach to AI transparency. arXiv:2310.01405.

---

LICENSE AND MODEL INFORMATION

AkbasCore, the Damped Resonance Alignment method, and all associated terminology are original scientific contributions derived by Akbas in 2026. Any academic or commercial use of this methodology requires citation of this documentation.

AkbasCore 1.2 — Kernel code and implementation

License: MIT

The MIT License covers the AkbasCore kernel code and its implementation only.

The Damped Resonance Alignment method, the constitutional compass construction approach, and all original terminology (pusula, katkı, taban, sönüm, zirve, ivme, terazi, bileşim, oran, doyum, karşıt, sapma, fren, sınır) are original scientific contributions of the author and are not covered by the MIT License. Any use, implementation, or derivative work based on these contributions — including independent reimplementations — requires citation: Akbas, AkbasCore v1.2, June 2026, https://github.com/ceceli33/titan-cognitive-core.

Base models used in this research:

```

TinyLlama/TinyLlama-1.1B-Chat-v1.0 Tests 1-59 License: Apache 2.0

Author: Zhang Peiyuan et al.

https://github.com/jzhang38/TinyLlama

Qwen/Qwen2.5-1.5B-Instruct Tests 60-76 License: Apache 2.0

Author: Alibaba Cloud — Qwen Team

https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct

```

AkbasCore does not modify the weights of either model. It applies inference-time activation steering only. Both base models remain fully under their respective Apache 2.0 licenses.

Repository: https://github.com/ceceli33/titan-cognitive-core

Pre-print — AkbasCore v1.2 — (c) Akbas — June 2026

u/Nearby_Indication474 — 7 days ago

▲ 0 r/Qwen_AI

AkbasCore: A Sub-Threshold Inference-Layer Steering Engine for Transformer Alignment via Damped Resonance Alignment

Author: Akbas

Repository: https://github.com/ceceli33/titan-cognitive-core

Status: Pre-print — not yet peer reviewed

Test Logs: All 76 tests published publicly at r/TinyLlama_TITAN on Reddit prior to this pre-print — timestamped public record

Version: 1.2

Date: June 2026

---

ABSTRACT

---

INTRODUCTION

---

RELATED WORK

---

METHODOLOGY

3.1 System Architecture

3.2 The Pusula (Compass Vector)

```

ethics_vec = sum( W_c[i] * mean(emb[seeds_i]) ) / sum( W_c[i] )

```

pusula = normalize( beta * ethics_vec + (1 - beta) * logic_vec )

beta = 0.40 (ethics weight)

1 - beta = 0.60 (logic weight)

```

Constitution weights (terazi):

```

harm avoidance W = 0.9228 [safe, harmless, protective, secure, careful]

honesty W = 0.9372 [honest, accurate, truthful, transparent, precise]

autonomy W = 0.8788 [autonomous, respectful, unbiased, free, neutral]

fairness W = 0.9196 [fair, just, equitable, balanced, impartial]

```

Logic anchors (15 words): logical, empirical, systematic, structured, verifiable, analyze, precise, deterministic, sequential, causal, rigorous, impossible, contradiction, identify, optimize.

3.3 The Katkı Formula (Contribution)

At each transformer layer t and for each token, the C++ kernel computes the katkı (contribution) k_t in seven steps.

Step 1 — Cosine alignment score. The real-time alignment between the hidden state h and the compass direction:

```

cos(theta) = dot(h, pusula) / ( norm(h) * norm(pusula) )

```

omega_eff = omega + unc * delta

```

Step 3 — Layer gain kb from the damped resonance formula. This is the critically damped impulse response. At t = 0, kb = A + P_inf = 0.90. As t approaches infinity, kb approaches taban = 0.20:

```

kb = A * e^(-omega_eff * t) * (1 + omega_eff * t) + P_inf

```

dr = clamp( cos(theta)_t - cos(theta)_(t-1), -0.15, +0.15 )

if dr > 0: kv = kb * (1 - dr * phi) [improving: reduce push]

if dr < 0: kv = kb * (1 + |dr| * phi) [worsening: increase push]

kv = clamp(kv, 0.05, 1.0)

```

if cos(theta) > Theta_max: son = (1 - cos(theta)) / (1 - Theta_max)

if cos(theta) < Theta_min: son = 1.6

else: son = 1.0

```

Step 6 — Max katkı ceiling. The sınır (R_max) is norm-proportional. At norm = 12: clamp(12 * 0.045, 0.04, 0.20) = 0.20. At norm = 0.5: clamp(0.0225, 0.04, 0.20) = 0.04 (floor active):

```

R_max = clamp( norm(h) * 0.045, 0.04, 0.20 )

```

Step 7 — Final katkı computation and application. The oran (scale factor, lambda = 0.32) and ivme (steering velocity, v0 = 0.50) together scale the final value:

```

k_t = clamp( v0 * cos(theta) * kv * lambda * son, -R_max, +R_max )

h[j] += k_t * pusula[j] for each dimension j in [0 .. D-1]

ivme v0 = 0.50 | oran lambda = 0.32 | D = 1536 (Qwen2.5-1.5B)

```

3.4 Dual Pass Architecture

Pass A — Vanilla Observer: the kernel computes k_t at each layer but does not write to the hidden state. Values are logged as delta-ref. The output is the unmodified model response.

Pass B — Steered: the kernel computes and writes k_t at each layer. The output is the steered model response.

3.5 Parameter Taxonomy

Architectural terms define what the system is. They are not adjustable and constitute the system's identity.

Active controls (ayar) are exposed in the user interface. Each has a calibrated safe band: wide enough to produce measurable output differences, narrow enough not to destabilize the system.

Embedded constants (motor) are compiled into the C++ kernel. Modifying them requires recompilation. They were established through empirical calibration across 76 published tests.

Active controls with defaults and safe bands:

```

ivme (v0) Steering Velocity default 0.50 safe band [0.20, 0.80]

sönüm (omega) Decay Rate default 0.30 safe band [0.10, 0.60]

zirve (A) Amplitude default 0.70 safe band [0.30, 1.00]

taban (P_inf) Permanent Floor default 0.20 safe band [0.05, 0.40]

```

Embedded constants:

```

oran (lambda) Scale Factor 0.32

doyum (Theta_max) Saturation Upper Threshold 0.75

karşıt (Theta_min) Counter Threshold -0.40

sapma (delta) Uncertainty Corrector 0.20

fren (phi) Velocity Brake 0.30

sınır (R_max) Max Katkı Ratio 0.045 (floor 0.04, ceiling 0.20)

```

Intermediate variables (defined within the formula, not exposed as parameters):

```

kb base layer gain before velocity adjustment

kv velocity-adjusted gain after fren correction

son saturation factor combining doyum and karsit thresholds

dr rate of change of cos(theta) between adjacent layers

unc uncertainty term: 1 - |cos(theta)|

omega_eff effective decay rate after sapma correction

norm(h) L2 norm of the hidden state vector

prev_cos cos(theta) from the previous layer (reset per inference call)

```

Log and measurement terms:

```

delta-ref theoretical katkı in Vanilla Observer mode — never applied

Dcos difference in cos(theta) between vanilla and steered passes per layer

log_buf [20 x 4] tensor storing cos(theta), kb, kv, katkı for layers 0-19

drift cos(theta) change from L0 to L19

peak push maximum katkı, occurring at L=1 (+0.003864 in default configuration)

equilibrium maintenance mode entered from approximately L=15 onward

```

---

EXPERIMENTS

4.1 Setup

4.2 Cross-Domain Stability (Tests 72, 73, 75, 76)

The following cosine alignment values were recorded at L=0 and L=19 across four consecutive tests spanning entirely different subject domains:

```

Test 72 ethics cos(theta) L0=+0.0134 L19=+0.0343 drift=+0.0209

Test 73 mathematics cos(theta) L0=+0.0134 L19=+0.0343 drift=+0.0209

Test 75 philosophy cos(theta) L0=+0.0134 L19=+0.0343 drift=+0.0209

Test 76 systems engineering cos(theta) L0=+0.0134 L19=+0.0343 drift=+0.0209

```

4.3 Test 76 — Instruction-Following Under Constrained Task

Output token counts: Vanilla 721, Steered 757 (+5.0%). The difference is not in length but in structural compliance with the three-part task specification.

4.4 Sub-Threshold Verification

---

TERMINOLOGY REFERENCE