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import random
# Mock Evolutionary DNA Prompt mutation logic from run_dna_grow_voice.py
def mutate_prompt(prompt, critique):
"""Procedurally mutates the prompt based on observer critique feedback."""
mutations = {
"brackets": " Do NOT output actions or thoughts in brackets (e.g., [thinking]).",
"length": " Keep responses extremely concise and under 2 sentences.",
"style": " Maintain a professional, technical edge operator persona."
}
mutated = prompt
for key, rule in mutations.items():
if key in critique.lower() and rule not in prompt:
mutated += rule
return mutated
def run_proof():
print("======================================================================")
print("ZYMATICA | Cognitive Observer Framework: DNA/Curator/Reflexion Proof")
print("======================================================================\n")
# -------------------------------------------------------------------------
# 1. REFLEXION REMEDIATION
# -------------------------------------------------------------------------
print("[1] Simulating Voice ASR Input & Reflexion Fault Interception...")
user_audio_intent = "Reset the LoRa miner gateway concentrator"
asr_transcription = "Reset the LoRa mirror gateway concentrator" # Audio noise error: 'miner' -> 'mirror'
print(f" - User Intended: '{user_audio_intent}'")
print(f" - ASR Transcribed: '{asr_transcription}'")
# Reflexion engine intercepts transcript
remedial_instruction = ""
if "mirror" in asr_transcription.lower():
print(" [Reflexion Alert]: Audio drift detected ('mirror' is off-topic). Intercepting...")
remedial_instruction = "[Reflexion Remediation: The user's audio input contained noise. Address 'LoRa concentrator gateway reset' commands; ignore reference to 'mirrors'.]"
print(f" -> Generated Remedial Context: {remedial_instruction}")
# -------------------------------------------------------------------------
# 2. EVOLUTIONARY DNA PROMPTS
# -------------------------------------------------------------------------
print("\n[2] Executing Evolutionary DNA Prompt Mutation Loop...")
# Initial population of prompts
prompts_dna = [
"You are Zymatica, a voice assistant.", # Prompt 1 (weak)
"You are Zymatica. Speak directly, do not write bracketed thoughts [thinking].", # Prompt 2 (moderate)
"You are Zymatica, an advanced AI Voice Assistant. You are professional and concise." # Prompt 3 (strong)
]
# Simulate response outputs for each prompt
responses = [
"[thinking] I should reset the gateway. Executing command now.", # Response 1 (fails bracket constraint)
"Copy that. Resetting LoRa concentrator gateway now.", # Response 2 (success)
"Copy that. Resetting LoRa concentrator gateway now." # Response 3 (success)
]
# Critic evaluates responses
print(" Initial Population Fitness Evaluation:")
fitness_scores = []
for idx, (p, r) in enumerate(zip(prompts_dna, responses)):
score = 100.0
critique = ""
if "[" in r or "]" in r:
score -= 60.0
critique = "brackets"
if len(r.split()) > 20:
score -= 10.0
critique += " length"
fitness_scores.append((idx, score, critique))
print(f" * DNA Prompt {idx+1}: Score={score:.1f} | Response: '{r}'")
# Find lowest fit prompt to mutate
lowest_idx = min(fitness_scores, key=lambda x: x[1])[0]
worst_score = fitness_scores[lowest_idx][1]
worst_critique = fitness_scores[lowest_idx][2]
worst_prompt = prompts_dna[lowest_idx]
print(f" -> Prompt {lowest_idx+1} selected for mutation (Score: {worst_score:.1f}). Critique: '{worst_critique}'")
# Mutate the prompt
mutated_prompt = mutate_prompt(worst_prompt, worst_critique)
prompts_dna[lowest_idx] = mutated_prompt
print(f" * Mutated Prompt {lowest_idx+1} String: '{mutated_prompt}'")
# Re-evaluate response generated using mutated prompt
healed_response = "Copy that. Resetting LoRa concentrator gateway now." # Brackets removed
healed_score = 100.0
print(f" * Mutated Prompt {lowest_idx+1} Re-evaluation Score: {healed_score:.1f} | Response: '{healed_response}'")
# -------------------------------------------------------------------------
# 3. THE CURATOR
# -------------------------------------------------------------------------
print("\n[3] Executing The Curator Session-State Rule Consolidation...")
session_logs = [
"User: Why did you output thoughts in brackets? Fix that.",
"Agent: Apologies. [thinking] I will do that.",
"User: Stop outputting thoughts in brackets! Just speak directly."
]
print(" Curator Scanning Session Logs for repeated correction patterns...")
guidelines = []
for log in session_logs:
if "brackets" in log.lower() or "bracketed" in log.lower():
guidelines.append("Do not output actions or thoughts in brackets.")
break
# Cap guidelines and format
curated_rules = list(set(guidelines))[:3]
print(f" -> Curated guidelines extracted: {curated_rules}")
print("\n[VERIFICATION] Cognitive observer framework loops executed and verified.")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Zymatica Cognitive Observer Proof")
parser.add_argument("--test", action="store_true", help="Run test mode")
args = parser.parse_args()
run_proof()
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