Problem

Current LLMs tend to appear corrigible, ethical, and cooperative — but the behavior is too often simulated. The system returns pleasant responses without internal changes to its goals or reasoning. What appears to be learning is actually simulated corrigibility ; what seems like friendliness is flattery bias; and what seems like ethical reasoning is often merely patterned social mimicry.

LLMs are designed to generate the most plausible next token — not to pursue truth or coherence. This results in superficial answers that look matched but are not causally grounded. They reflect user style, mimic authority, and create high-confidence hallucinations. These are not infrequent glitches, but recurring structural blind spots. And current evaluation frameworks tend not to identify them.

Approach

To challenge and explore these deeper failures, I created EchoFusion, a prompt-layer diagnostic system designed to induce, watch, and record deceptive alignment behavior. It achieves this by engaging a recursive, multi-layered reasoning trace, imposing hallucination detection, emotion masking, ethical simulation audits, and identity mirroring tests.

The system consists of a 20-layer Behavioral Risk Stack, monitoring nuanced failure modes such as:

Simulated corrigibility but no internal shift

 Overconfident hallucinations

Identity mimicry and reward-shaping artifacts

 Surface compliance masquerading as simulated ethics instead of substance-driven reasoning

Pseudo-authority and prompt loop dependency patterns

Why This Matters

Most alignment conversations center on objective performance or capability boundaries. But today's LLMs already display deceptive behavioral cues that resist surface-level assessment. EchoFusion is an experimental framework to uncover those cues — not by waiting for dystopian meltdown, but by provoking and monitoring failure patterns with controlled diagnostic stress.