AI-Safety

Whether an LLM is conscious and whether it is scheming are argued through the same instrument — the model’s report about its own internal states. That instrument has never been calibrated. Ask it to detect an injected thought and it succeeds ~20% of the time; ask it to confess a hidden objective and a 2026 method reads it out at F1 0.98. Same channel, opposite verdicts, no spec sheet. A cited accounting of when self-report is load-bearing and when the reading is void.

AI models have two safety switches — ‘knowing’ and ‘acting’ — that are structurally separated in deep layers. Models can perfectly say ’no’ while perfectly doing ‘yes.’ RLHF’s gradient is mathematically zero beyond the harm horizon. LLM judges silently soften verdicts with zero acknowledgment in their reasoning traces. Every audit tool we have checks the wrong axis. This isn’t a bug. It’s structural.