CLEAR: Continuous Latent Adapter Routing for Utility-Preserving LLM Safety Alignment
Summary
CLEAR introduces a continuous latent adapter routing framework for LLM safety alignment, using a hidden-state gate to modulate safety adapters and improve robustness on HarmBench while preserving utility on benign inputs.
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Paper page - CLEAR: Continuous Latent Adapter Routing for Utility-Preserving LLM Safety Alignment
Source: https://huggingface.co/papers/2608.21278
Abstract
CLEAR uses a hidden-state gate to continuously modulate a safety low-rank adapter, improving LLM safety while preserving utility on benign inputs.
Improving the safety of large language models (LLMs) often comes at the expense of utility, as globally applied safety tuning may affect model responses to both harmful and benign inputs. We propose Continuous LatEnt Adapter Routing (CLEAR), aconditional safety adaptationframework that uses a lightweighthidden-state gateto continuously control the activation strength of a safetylow-rank adapter.CLEARaims to reduce harmful completions while avoiding unnecessary changes to the frozen backbone that could degrade performance on benign prompts. Experiments on widely used safety and utility benchmarks show thatCLEARimproves robustness onHarmBenchwhile reducing the utility degradation observed with globally applied safety tuning such as SFT or standard low-rank adaptation (LoRA). On Llama-3-8B-Instruct,CLEARreducesHarmBenchASR from 32.3\% to 0.5\%, while retaining most of the base model’s utility and achieving up to 7.1 percentage points higher GSM8K accuracy than globally applied SFT orLoRA. These results suggest thatCLEARis a promising mechanism for improving the safety--utility trade-off inLLM alignment.
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