Hugging Face Trending Papers August 20, 2026

Enforcing LLM Safety through DMD-based Classification of Prompt-Response Embedding Dynamics

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The paper extends a dynamical systems approach to classify unsafe outputs from large language models (LLMs) by projecting prompts and responses into high‑dimensional embeddings and fitting separate Koopman-based predictive models for safe and unsafe regimes. A differential residual score compares prediction errors from these models to classify new outputs. Experiments on three safety benchmarks show that including prompt embeddings improves detection of interaction‑dependent violations, especially with causal decoders like Llama‑3, while response‑only violations benefit more from dense semantic embeddings.

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Enforcing LLM Safety through DMD-based Classification of Prompt-Response Embedding Dynamics

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