Evaluating semantic drift is essential for understanding dynamical discourse evolution and opinion formation in online discussions. However, sparse and uneven distributions of event-specific keywords prevent traditional models from capturing post-level semantic drift. Thus, to address this issue, we propose an LLM-embedding Semantic Adaptation Network (LLM-SAN), which is a hybrid semantic drift evaluation model with an LLM-Embedding gated recurrent unit (GRU) module, an LLM-Embedding graph convolutional network (GCN) module and a multi-expert adaptive fusion module. The GRU module is used to extract features from event related posts, and The GCN is used to extract features from temporal graphical topic posts. Then, the features are merged by the multi-expert adaptive fusion module. Finally, this module predicts the future post embedding, and the prediction error is used to evaluate and detect the semantic drift points. Extensive experiments are conducted, and the results show that LLM-SAN achieves the state-of-the-art performance on the semantic drift evaluation task, compared to the other baselines. Ablation experiments are also conducted to show the effectiveness of each module in LLM-SAN.

Safety alignment is a fundamental prerequisite for building trustworthy artificial general intelligence. Despite substantial progress in safety alignment techniques, empirical evidence shows that aligned large language models can still produce unsafe responses under minor internal perturbations, revealing a robustness gap in existing safety mechanisms at the latent representation level. In this paper, we study the robustness evaluation of safety alignment under latent-space perturbations. We introduce Activation Steering Attack (ASA), and leverage the Negative Log-Likelihood (NLL) as a diagnostic signal to probe the local sensitivity of safety behaviors in latent space. By measuring a model’s likelihood under controlled perturbations to its hidden representations, we assess the stability of its original responses. The probing signal is model-agnostic and supervision-free, enabling a general and reproducible diagnostic metric for analyzing safety robustness. Leveraging these probes, we systematically uncover a set of previously underexplored empirical findings, including (1) non-stationarity of layer vulnerabilities, revealing that the most vulnerable layer is an unstable property and even relocates after robustness training; (2) instance-level alignment with cross-layer consistency, where specific inputs remain universally vulnerable across the entire model hierarchy; (3) compositional effects of ASA, characterized by its incremental accumulation across sequential decoding steps and its potential for prompt-level jailbreak effectiveness.