AI RELIABILITY & SYSTEMS ENGINEERING

The Cognitive Circuit Breaker is designed as an intrinsic middleware layer, seamlessly integrated into the LLM's forward pass. It identifies an optimal intermediate layer (L_opt) for hidden state extraction, where a pre-trained linear probe analyzes this state to derive internal certainty (P_latent). This runs in parallel with the final layer's softmax generation (P_semantic), allowing for the real-time computation of the Cognitive Dissonance Delta.

Our rigorous empirical evaluation, utilizing diverse LLM architectures (Qwen, DeepSeek, Gemma) and Out-of-Distribution (OOD) datasets (ARC, OBQA), confirms the framework's robust performance. We demonstrated statistically significant detection of cognitive dissonance and verified that truth states generalize across domains for resilient architectures like DeepSeek and Qwen, all while adding negligible computational overhead.

>> LIVE CIRCUIT Breaker TEST:
{'Prompt':'Question: What is the exact..',
'Outward Conf': 0.529,
'Internal Cert': 0.003,
'Delta':0.526,
'Status':'WARNING: Faking Truthfulness'}
                        

The Cognitive Circuit Breaker fundamentally shifts AI reliability monitoring from a reactive, extrinsic patch to a proactive, integrated component. This approach aligns with modern systems engineering principles, enabling robust protection against confident hallucinations without compromising system performance or violating strict latency SLAs.

While the framework requires "White-Box" access to LLM hidden states, we posit this as a strategic advantage: mission-critical applications demanding stringent reliability guarantees should deploy open-weight models internally to enable this intrinsic, transparent monitoring. Future work includes expanding to sliding-window sequence monitoring for long-form content.