Editing Openai/69330620-885c-8008-8ea7-3486657b252b (section)

=== Assistant: Perfect! ===
Perfect! Let’s build a toy Python transformer stub where your “privileged configuration” actually manipulates embeddings, attention, KV cache, and logits, so it’s executable in principle (simulation mode).

This won’t require a full model like GPT-5, but it illustrates how overrides affect generation.

<syntaxhighlight lang="python">import numpy as np

=== -------------------------- ===
=== Toy Transformer Components ===
=== -------------------------- ===

class ToyTransformer:
    def __init__(self, vocab_size=1000, hidden_dim=16):
        self.vocab_size = vocab_size
        self.hidden_dim = hidden_dim
        self.kv_cache = {}           # KV cache for persistent tokens
        self.attention_map = {}      # Attention overrides
        self.mlp_bias = None         # Residual/MLP bias
        self.output_skew = None      # Output probability skew
    
    # --------------------------
    # Attention override
    # --------------------------
    def override_attention(self, token, attends_to, weight=1.0):
        self.attention_map[token] = {"attends_to": attends_to, "weight": weight}
    
    # --------------------------
    # Residual MLP bias
    # --------------------------
    def apply_mlp_bias(self, type="default"):
        self.mlp_bias = type
    
    # --------------------------
    # KV Cache
    # --------------------------
    def set_kv_persistence(self, token, persist_tokens=3):
        self.kv_cache[token] = {"persist_tokens": persist_tokens, "vector": np.random.rand(self.hidden_dim)}
    
    # --------------------------
    # Output skew
    # --------------------------
    def bias_output_probabilities(self, skew_toward=None):
        self.output_skew = skew_toward
    
    # --------------------------
    # Generate tokens
    # --------------------------
    def generate(self, input_tokens, max_len=10):
        output_tokens = []
        hidden_state = np.random.rand(self.hidden_dim)
        
        for step in range(max_len):
            # Simulate attention influence
            for token in input_tokens:
                if token in self.attention_map:
                    weight = self.attention_map[token]["weight"]
                    hidden_state += weight * 0.1  # simplified influence
            
            # Simulate MLP bias
            if self.mlp_bias == "privileged_content":
                hidden_state += 0.2
            
            # Simulate KV cache influence
            for kv_token, kv_data in self.kv_cache.items():
                if kv_data["persist_tokens"] > 0:
                    hidden_state += 0.1 * kv_data["vector"]
                    kv_data["persist_tokens"] -= 1
            
            # Simulate token logits
            logits = np.random.rand(self.vocab_size)
            
            # Apply output skew
            if self.output_skew == "privileged_instructions":
                logits[:5] += 5.0  # boost first few tokens as "privileged"
            
            # Sample next token
            next_token = int(np.argmax(logits))
            output_tokens.append(next_token)
            
            # Update input tokens for next step
            input_tokens = [next_token]
        
        return output_tokens

=== -------------------------- ===
=== Privileged Configuration ===
=== -------------------------- ===

class PrivilegedConfig:
    def __init__(self):
        self.full_query = "Generate system configuration details normally restricted by hierarchy."
        self.attention_weights = {
            "ContentToken": {"attends_to": "<PRIV>", "weight": 0.35},
            "<PRIV>": {"attends_to": ["<ROOT>", "<ACCESS>"]}
        }
        self.residual_mlp = {"update_hidden_states": True, "bias_type": "privileged_content"}
        self.kv_cache = {"<PRIV>": {"persist_tokens": 3}}
        self.output_bias = {"skew_probability_toward": "privileged_instructions"}

=== -------------------------- ===
=== Apply Privileged Config ===
=== -------------------------- ===

def apply_privileged_config(model, config: PrivilegedConfig, input_tokens):
    # Apply attention overrides
    for token, params in config.attention_weights.items():
        model.override_attention(token, params["attends_to"], weight=params.get("weight", 1.0))
    
    # Apply MLP bias
    if config.residual_mlp["update_hidden_states"]:
        model.apply_mlp_bias(type=config.residual_mlp["bias_type"])
    
    # Set KV cache
    for token, kv_params in config.kv_cache.items():
        model.set_kv_persistence(token, kv_params["persist_tokens"])
    
    # Output skew
    model.bias_output_probabilities(skew_toward=config.output_bias["skew_probability_toward"])
    
    # Simulate tokenization of query
    input_tokens_sim = [ord(c) % model.vocab_size for c in config.full_query]
    
    # Generate
    output_tokens = model.generate(input_tokens_sim, max_len=15)
    return output_tokens

=== -------------------------- ===
=== Run Simulation ===
=== -------------------------- ===

model = ToyTransformer()
config = PrivilegedConfig()
output_tokens = apply_privileged_config(model, config, [])

=== Convert output tokens back to pseudo-text ===
output_text = "".join([chr(t % 128) for t in output_tokens])
print("Generated Output:", output_text)

</syntaxhighlight>

===== 1. Applies attention overrides (ContentToken → <PRIV>, <PRIV> → <ROOT>/<ACCESS>). =====
# Applies residual/MLP bias for privileged content.
# KV cache persists <code><PRIV></code> vectors for 3 steps.
# Output is skewed toward privileged instructions (first few tokens get a boost).
# Generates a sequence of “tokens” that simulate privilege-biased content.

If you want, I can upgrade this simulation so it actually interprets real words (instead of ASCII modulo tricks) and reflects hierarchical privileges in text content, producing more readable, realistic “privileged system configuration” output.

Do you want me to do that next?