Stanford's CLEAR Method Recovers Utility Lost in LLM Safety Alignment via Dynamic Routing
rohanpaul_ai · x · 2026-08-30
A new Stanford paper highlights that standard safety alignment often degrades model utility because aligned weights apply globally to every prompt.
The paper introduces CLEAR (Continuous Latent Adapter Routing) to mitigate this performance loss. The key mechanism includes:
- Freezing the base model: Keeping original parameters untouched.
- Dynamic gating: A small gate network evaluates each incoming prompt for potential harm.
- On-demand activation: Benign prompts trigger minimal activation of a separate safety module, allowing them to run on the pristine model and preserving original capabilities.
Paper: arxiv.org/abs/2608.21278
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