DNAlign: Dynamic Null-Space Safe Alignment for LLMs
Read the original on arXiv AI →DNAlign is a lightweight alignment framework that uses control‑theoretic optimization and null‑space projection to steer large language models toward safe behavior while preserving core knowledge and response quality. By treating the LLM as a dynamic system, it introduces controllable perturbations that are restricted to a harmful‑related subspace derived from neutral hidden states, and a value function trained on human preference data adaptively optimizes these control signals. Extensive evaluations across multiple LLM backbones show that DNAlign consistently reduces harmful outputs while maintaining fluency, coherence, and factual utility, outperforming prior alignment baselines without sacrificing generation diversity.
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