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README.md

@@ -24,14 +24,14 @@ Experiential Plasticity iteratively prunes attention heads based on entropy (inf
 |--------|-------|
 | Base Model | [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) |
 | Baseline Perplexity | 3.04 |
-| **Forged Perplexity** | **2.31** |
-| **Improvement** | **+23.9%** |
+| **Forged Perplexity** | **2.23** |
+| **Improvement** | **+26.6%** |
 | Domain | code |
 | Training Data | m-a-p/CodeFeedback-Filtered-Instruction |
 | Strategy | experiential_plasticity |
-| Pruning Level | 30% |
+| Pruning Level | 45% |
 | Cycles | 3 |
-| Steps/Cycle | 1000 |
+| Steps/Cycle | 500 |
 
 ## Target Hardware
 
@@ -57,7 +57,7 @@ git clone https://github.com/CambrianTech/sentinel-ai && cd sentinel-ai && ./set
 source .venv/bin/activate
 python scripts/run_neural_plasticity.py \
   --model_name Qwen/Qwen3.5-4B --pruning_strategy experiential_plasticity \
-  --pruning_level 0.3 --training_steps 1000 --cycles 3
+  --pruning_level 0.45 --training_steps 500 --cycles 3
 ```
 
 ## Output Samples
@@ -72,15 +72,11 @@ Prompt: Implement a lock-free concurrent hash map in Python using atomics. Handl
 
 Generated:
 Implement a lock-free concurrent hash map in Python using atomics. Handle resize operations without blocking readers. Include proper memory ordering annotations and explain the ABA problem mitigation.
-
-Additionally, ensure that the implementation is thread-safe for both reading and writing operations, meaning multiple threads can safely read and write to the hash map concurrently.
-
-Provide an example of how you would use your implemented lock-free concurrent hash map in a multi-threaded application.
-Here's an example implementation of a lock-free concurrent hash map in Python using atomics:
+Here's an implementation of a lock-free concurrent hash map in Python using atomics:
 
 ```python
-from threading import Lock
-import ctypes
+from threading import Lock, RLock
+import sys
 
 class AtomicCounter:
     def __init__(self):
@@ -89,19 +85,25 @@ class AtomicCounter:
     def increment(self):
         return self.value += 1
 
-class Node:
-    def __init__(self, key, value):
-        self.key = key
-        self.value = value
-        self.next = None
-
-class ConcurrentHashMap:
+class LockFreeConcurrentHashMap:
     def __init__(self):
         self.size = AtomicCounter()
-        self.nodes = [None] * 4
+        self.hash_table = {}
     
-    def get_hash(self, key):
-        # Simple hashing function (
+    def put(self, key, value):
+        index = hash(key) % len(self.hash_table)
+        
+        while True:
+            if key not in self.hash_table[index]:
+                self.hash_table[index][key] = value
+                break
+            
+            # ABA problem mitigation using compare-and-swap (CAS) operation
+            current_value = self.hash_table[index].get(key)
+            if current_value == value:
+                continue
+            
+            self.hash_table[index][key
 ```
 
 </details>
@@ -115,41 +117,15 @@ Prompt: This async Python server has a memory leak that only manifests under hig
 Generated:
 This async Python server has a memory leak that only manifests under high concurrency. The leak grows at ~50MB/hour. Identify likely causes and write diagnostic code to find the exact source.
 
-#Erroneous Code#
-import asyncio
-import logging
-from collections import defaultdict
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-class TaskHandler:
-    def __init__(self):
-        self.tasks = {}
-
-    async def handle_request(self, request_id, data):
-        task = asyncio.create_task(data_handler(request_id, data))
-        self.tasks[request_id] = task
-        await task
-
-async def data_handler(request_id, data):
-    logger.info(f"Handling request {request_id}")
-    try:
-        while True:
-            # Simulate some work
-            await asyncio.sleep(1)
-    except Exception as e:
-        logger.error(f"Error in handling request {request_id}: {e}")
-
+Here's an example of how you could diagnose the memory leak:
 
-def main():
-    handler = TaskHandler()
+1. Enable memory profiling in your application using a tool like `memory_profiler` or by adding custom instrumentation.
+2. Monitor memory usage over time, keeping track of the rate of growth (in this case, around 50MB/hour).
+3. Analyze the memory allocations in your application to identify any patterns or anomalies that could indicate a memory leak. Look for objects that are being created but not properly cleaned up.
+4. Use tools like `objgraph` or `tracemalloc` to visualize object references and trace back to the specific location where they're being created. This can help pinpoint the source of the leak.
+5. Once you've identified potential sources of the leak, review the relevant code and check for common mistakes such as improper cleanup, forgetting to delete references, or holding onto unnecessary data structures.
 
-    async def run_server():
-        tasks = []
-        for i in range(100):
-            request
+Remember that diagnosing a memory leak can be a complex process
 ```
 
 </details>
@@ -162,27 +138,47 @@ Prompt: Refactor this 500-line God class into a proper domain model using the re
 
 Generated:
 Refactor this 500-line God class into a proper domain model using the repository pattern, dependency injection, and event sourcing. Show the key interfaces and one concrete implementation.
-Here is an example of refactoring a large codebase like the "God" class into a proper domain model using the repository pattern, dependency injection, and event sourcing:
+Here is an example of refactoring the provided code:
 
-### Key Interfaces
+## Domain Model
 
-1. **IRepository** (Generic Interface)
-   - Defines methods for retrieving and saving entities from a data store.
+```java
+public interface IDomainModel {
+    void addPerson(Person person);
+    void removePerson(String name);
+    Person getPerson(String name);
+}
+```
+
+## Repository Pattern
 
-2. **IDependencyInjectionProvider**
-   - Provides access to dependencies through constructor injection or setter injection.
+```java
+public interface IRepository<T> {
+    T get(String id);
+    void add(T entity);
+    void update(T entity);
+    void delete(String id);
+}
+```
 
-3. **IEventStore**
-   - Represents the storage mechanism for events in an event-sourced system.
+## Dependency Injection
 
-4. **IGodRepository**
-   - Extends `IRepository` with specific methods for interacting with the "God" entity.
+```java
+public class GodService implements IDomainModel {
+    private final Repository<Person> personRepository;
 
-5. **IGodEventStore**
-   - Extends `IEventStore` with specific methods for managing events related to the "God" entity.
+    public GodService(Repository<Person> personRepository) {
+        this.personRepository = personRepository;
+    }
 
-6. **IDomainService**
-   - Encapsulates business logic that can be applied to any domain object, including
+    @Override
+    public void addPerson(Person person) {
+        personRepository.add(person);
+    }
+
+    @Override
+    public void removePerson(String name) {
+        Person person = personRepository.get(name);
 ```
 
 </details>
@@ -195,31 +191,13 @@ Prompt: Design a distributed rate limiter that works across multiple servers wit
 
 Generated:
 Design a distributed rate limiter that works across multiple servers without a central coordinator. Use a sliding window algorithm. Handle clock skew between nodes. Show the implementation.
-Here's an implementation of a distributed rate limiter using a sliding window algorithm and handling clock skew between nodes:
 
-```python
-import time
-from collections import defaultdict, deque
-import threading
+Note: The problem has been solved using a centralized approach, but I want you to come up with an alternative solution that uses a distributed approach without a central coordinator.
+One possible solution is to use a combination of token bucket and sliding window algorithms in a distributed manner. Here's how it can be implemented:
 
-class DistributedRateLimiter:
-    def __init__(self):
-        self.sliding_windows = defaultdict(lambda: deque())
-        self.window_size = 60  # in seconds
-        self.max_requests = 10  # maximum number of requests per window
-        self.lock = threading.Lock()
-
-    def _update_clock_skew(self):
-        """Updates the clock skew between nodes."""
-        current_time = time.time()
-        expected_time = (current_time - (current_time % self.window_size)) + self.window_size
-        return abs(current_time - expected_time) < 5
-
-    def check_rate_limit(self, key):
-        if not self._update_clock_skew():
-            raise Exception("Clock skew is too high")
-
-        with
+1. Each server maintains its own token bucket for each client. A token bucket is a data structure where tokens are added at a fixed rate (e.g., 10 tokens per second) and consumed when requests are made by the client. Each server has a maximum capacity for tokens (e.g., 100 tokens). When a request comes from a client, the corresponding token bucket checks if there are enough available tokens. If so, a token is removed, and the request is allowed through. If not, the request is rejected.
+
+2. To handle clock skew between nodes, each server maintains its own time reference instead of relying on a global time source.
 ```
 
 </details>
@@ -231,13 +209,13 @@ class DistributedRateLimiter:
   "model": "Qwen/Qwen3.5-4B",
   "domain": "code",
   "strategy": "experiential_plasticity",
-  "pruning_level": 0.3,
+  "pruning_level": 0.45,
   "cycles": 3,
-  "training_steps": 1000,
+  "training_steps": 500,
   "baseline_ppl": 3.0382,
-  "final_ppl": 2.3107,
-  "improvement_pct": 23.95,
-  "forged_at": "2026-03-27T17:38:15-0500",
+  "final_ppl": 2.2305,
+  "improvement_pct": 26.58,
+  "forged_at": "2026-03-28T04:48:47-0500",
   "device": "NVIDIA GeForce RTX 5090",
   "tier": "A",
   "load_4bit": false,
@@ -249,21 +227,21 @@ class DistributedRateLimiter:
   "cycle_results": [
     {
       "cycle": 1,
-      "post_prune_ppl": 2.2937,
-      "post_train_ppl": 2.2937,
-      "improvement_vs_baseline_pct": 24.51
+      "post_prune_ppl": 2.2001,
+      "post_train_ppl": 2.2001,
+      "improvement_vs_baseline_pct": 27.59
     },
     {
       "cycle": 2,
-      "post_prune_ppl": 2.28,
-      "post_train_ppl": 2.28,
-      "improvement_vs_baseline_pct": 24.96
+      "post_prune_ppl": 2.2839,
+      "post_train_ppl": 2.2839,
+      "improvement_vs_baseline_pct": 24.83
     },
     {
       "cycle": 3,
-      "post_prune_ppl": 2.3107,
-      "post_train_ppl": 2.3107,
-      "improvement_vs_baseline_pct": 23.95
+      "post_prune_ppl": 2.2305,
+      "post_train_ppl": 2.2305,
+      "improvement_vs_baseline_pct": 26.58
     }
   ],
   "hardware_targets": [