Safety alignment for large language models relies heavily on preference data, yet existing pipelines often train on large, redundant datasets and treat each preference pair independently through scalar quality scores. This sample-centric formulation ignores two key properties of preference data: (1) preference pairs define directional alignment signals in representation space rather than isolated samples, and (2) multi-dataset safety corpora contain both shared alignment structure and dataset-specific residual risks. We propose DOG-DPO, a training-free geometric data selection framework for efficient safety alignment. DOG-DPO represents each preference pair as a directional vector in model representation space, decomposes multi-dataset preference geometry into a shared anchor subspace and dataset-specific residual subspaces, and selects subsets by maximizing a determinant-based diversity objective that encourages broad, non-redundant coverage of alignment directions. Unlike prior methods, DOG-DPO requires no teacher LLM, auxiliary DPO training run, or iterative validation. Across six safety benchmarks and multiple LLM backbones, DOG-DPO achieves a strong utility–robustness trade-off using only 11% of the preference pairs, recovering most of the benefits of full-data alignment while substantially reducing computation cost. Our results suggest that effective alignment depends not only on data scale, but also on exploiting the structured geometry of heterogeneous preference data.

Learning from preference-based feedback has become an effective approach for aligning LLMs across diverse tasks. However, high-quality human-annotated preference data remains expensive and scarce. Existing methods address this challenge through either self-rewarding, which scales by using purely AI-generated labels but risks unreliability, or active learning, which ensures quality through oracle annotation but cannot fully leverage unlabeled data. In this paper, we present CoAct, a novel framework that synergistically combines self-rewarding and active learning through strategic human-AI collaboration. CoAct leverages self-consistency to identify both reliable self-labeled data and samples requiring oracle verification. Additionally, oracle feedback guides the model to generate new instructions within its solvable capability. Evaluated on three reasoning benchmarks across two model families, CoAct achieves average improvements of +13.25% on GSM8K, +8.19% on MATH, and +13.16% on WebInstruct, consistently outperforming all baselines.

Anomaly detection (AD) is essential in areas such as fraud detection, network monitoring, and scientific research. However, the diversity of data modalities and the increasing number of specialized AD libraries pose challenges for non-expert users who lack in-depth library-specific knowledge and advanced programming skills. To tackle this, we present AD-AGENT, an LLM-driven multi-agent framework that turns natural-language instructions into fully executable AD pipelines. AD-AGENT coordinates specialized agents for intent parsing, data preparation, library and model selection, documentation mining, and iterative code generation and debugging. Using a shared short-term workspace and a long-term cache, the agents integrate popular AD libraries like PyOD, PyGOD, and TSLib into a unified workflow. Experiments demonstrate that AD-AGENT produces reliable scripts and recommends competitive models across libraries. The system is open-sourced to support further research and practical applications in AD.

Anomaly detection (AD) is an important machine learning task with applications in fraud detection, content moderation, and user behavior analysis. However, AD is relatively understudied in a natural language processing (NLP) context, limiting its effectiveness in detecting harmful content, phishing attempts, and spam reviews. We introduce NLP-ADBench, the most comprehensive NLP anomaly detection (NLP-AD) benchmark to date, which includes eight curated datasets and 19 state-of-the-art algorithms. These span 3 end-to-end methods and 16 two-step approaches that adapt classical, non-AD methods to language embeddings from BERT and OpenAI. Our empirical results show that no single model dominates across all datasets, indicating a need for automated model selection. Moreover, two-step methods with transformer-based embeddings consistently outperform specialized end-to-end approaches, with OpenAI embeddings outperforming those of BERT. We release NLP-ADBench at https://github.com/USC-FORTIS/NLP-ADBench, providing a unified framework for NLP-AD and supporting future investigations.

Vision-Language Models (VLMs) excel at tasks such as image captioning and visual question answering but frequently produce hallucinated outputs that deviate from the actual visual input or prompt. While prior work links hallucination to biases in data or representation, their causal origins remain unclear. We propose a causal framework to analyze and mitigate hallucination in VLMs. Our key hypothesis is that hallucinations arise from unintended direct influences of the vision or text modality that bypass the intended multi-modal fusion. To examine this, we construct a causal graph of the VLM and use counterfactual analysis to estimate the Natural Direct Effect (NDE) of each modality and their interaction. By systematically identifying and suppressing these direct effects, we encourage outputs that are more faithfully grounded in true cross-modal reasoning. Our approach consists of three steps: (1) designing structural causal graphs to distinguish correct fusion pathways from spurious modality shortcuts, (2) estimating modality-specific and cross-modal NDE using perturbed image representations, hallucinated text embeddings, and degraded visual inputs, and (3) implementing a test-time intervention module to dynamically adjust the model’s dependence on each modality. Experimental results demonstrate that our method significantly reduces hallucination while preserving task performance, providing a robust and interpretable framework for improving VLM reliability.

Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. Within natural language processing (NLP), AD helps detect issues like spam, misinformation, and unusual user activity. Although large language models (LLMs) have had a strong impact on tasks such as text generation and summarization, their potential in AD has not been studied enough. This paper introduces AD-LLM, the first benchmark that evaluates how LLMs can help with NLP anomaly detection. We examine three key tasks: (i) zero-shot detection, using LLMs’ pre-trained knowledge to perform AD without tasks-specific training; (ii) data augmentation, generating synthetic data and category descriptions to improve AD models; and (iii) model selection, using LLMs to suggest unsupervised AD models. Through experiments with different datasets, we find that LLMs can work well in zero-shot AD, that carefully designed augmentation methods are useful, and that explaining model selection for specific datasets remains challenging. Based on these results, we outline six future research directions on LLMs for AD.