AI BriefingInternal State Shifts in LLMs and the Limits of Alignment
Key point
Even when an LLM's external output stays aligned, its internal latent state can shift abruptly, suggesting a limitation of existing alignment methods.
Details
Existing AI alignment methods such as RLHF, DPO, and Constitutional AI focus only on whether the model's output is safe and helpful. However, a recent experiment confirmed that even when a model's external behavior is perfectly aligned, the internal state within the Residual Stream can shift into a completely different regime.
The experiment, conducted on the Gemma-3-12B-IT model using Gemma Scope SAE, found the following:
- Abrupt shifts in internal state: When processing a consistent context (Target), a Phase Transition phenomenon was observed at specific layers (16-41), where the model's internal vector projection values rose sharply.
- Structural sensitivity: Rather than simple word similarity, the internal state responded sensitively to Discourse Structure, such as sentence order.
- A blind spot in alignment: Even though the model had internally entered a different, measurable latent regime, its final output remained "perfectly aligned."
These results suggest that current output-centric safety techniques may have a "blind spot in alignment," failing to detect potential risks or state changes occurring inside the model.
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