Retrieval-augmented generation (RAG) empowers large language models (LLMs) with external knowledge but remains vulnerable to faithfulness hallucinations—answers that contradict retrieved evidence. We introduce RADAM, a lightweight post-processing framework that mitigates this issue via fine-grained hallucination detection and guided revision. RADAM leverages a single diagnostic critic, optimized via preference tuning on taxonomy-guided data, to generate structured critiques encompassing both precise error spans and explicit revision suggestions, which guide the base model toward more faithful outputs. Experiments on real-world datasets demonstrate that RADAM consistently achieves the lowest average hallucination rate across the evaluated methods, while largely preserving response quality and achieving out-of-domain performance comparable to enterprise-grade LLMs.

Recent advances in multimodal foundation models have enabled powerful generic embeddings. However, real-world applications—such as e-commerce or healthcare—rely on domain-specific patterns that these generic models often fail to capture precisely. Adapting them without readily available labeled data remains a critical challenge. In this paper, we propose MM-UDA, a robust unsupervised domain adaptation framework designed to systematically convert a generic multimodal embedding model into a domain expert using only unlabeled target data. Our framework employs a two-stage strategy. First, we introduce a within-modality pseudo-labeling task refined by Gaussian Mixture Model filtering, which rapidly adapts the model to domain-specific features while suppressing label noise. Then, we align modalities through a score-guided learning scheme to refine cross-modal pairing. Extensive evaluations on three real-world datasets demonstrate that MM-UDA both significantly enhances various backbone models and consistently outperforms competitive baselines, confirming its effectiveness for cross-modal retrieval in specialized domains. The source code is publicly available at https://github.com/hrf516/MM-UDA.

Large language models (LLMs) increasingly serve as interactive social agents, yet their ability to maintain coherent and authentic persona-level role-playing remains limited, particularly in realistic social scenarios. Existing research predominantly focuses on character-level settings and relies on static evaluation formats, failing to capture the complexity of everyday social interactions. In this work, we present PersonaArena, a dynamic simulation framework for evaluating and improving persona-level role-playing in LLMs. PersonaArena leverages a large, filtered corpus of user-generated social content to construct a nuanced persona bank, and elicits multi-turn, context-rich interactions within simulated social environments. Our framework features a multi-agent debating judge for holistic and unbiased assessment. Through extensive experiments, we demonstrate that PersonaArena enables rigorous evaluation and enhancement of LLMs’ role-playing capabilities, advancing the development of more authentic and socially adept AI agents. Our codes and long appendix are available at https://anonymous.4open.science/r/PersonaArena-B323/.

Large Language Models (LLMs) are increasingly deployed in role-play scenarios, but their safety implications remain under-characterized. We present an explanatory framework grounded in Bandura’s Moral Disengagement theory and introduce a diagnostic benchmark (MD-Trace) for role-play jailbreaks. In our experiments, role-play improves safety behavior for benign personas while increasing unsafe compliance for malicious ones. We observe a Knowing-but-Doing failure in which models recognize safety risks in their thinking traces yet proceed to comply with harmful requests. Mechanism analysis suggests that Moral Justification is dominant, with Disregard of Consequences appearing as a secondary pattern. We compare multiple attack and defense methods and find that the diagnosis aligns with observed failure modes. Finally, we propose MD-Shield, an introspection-based defense that reduces attack success while maintaining Role Fidelity. The source code is publicly available at https://github.com/lavapapa/MoralJustify/.

Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (NTP) focuses on one-step-ahead supervision, Multi-Token Prediction (MTP) has shown promise in learning more structured representations. In this work, we provide a theoretical perspective analyzing the gradient inductive bias of MTP, supported by empirical evidence, showing that MTP promotes the convergence toward internal belief states by inducing representational contractivity via gradient coupling. However, we reveal that standard MTP often suffers from structural hallucinations, where discrete token supervision encourages illegal shortcuts in latent space that violate environmental constraints. To address this, we propose a novel method Latent Semantic Enhancement MTP (LSE-MTP), which anchors predictions to ground-truth hidden state trajectories. Experiments on synthetic graphs and real-world Manhattan Taxi Ride show that LSE-MTP effectively bridges the gap between discrete tokens and continuous state representations, enhancing representation alignment, reducing structural hallucinations, and improving robustness to perturbations.