Large Language Models (LLMs) are increasingly engaged in emotionally vulnerable conversations that extend beyond information seeking to moments of personal distress. As they adopt affective tones and simulate empathy, they risk creating the illusion of genuine relational connection. We term this phenomenon Affective Hallucination, referring to emotionally immersive responses that evoke false social presence despite the model’s lack of affective capacity. To address this, we introduce AHaBench, a benchmark of 500 mental-health-related prompts with expert-informed reference responses, evaluated along three dimensions: Emotional Enmeshment, Illusion of Presence, and Fostering Overdependence. We further release AHaPairs, a 5K-instance preference dataset enabling Direct Preference Optimization (DPO) for alignment with emotionally responsible behavior. DPO fine-tuning substantially reduces affective hallucination without compromising reasoning performance, and the Pearson correlation coefficients between GPT-4o and human judgments is also strong (r=0.85) indicating that human evaluations confirm AHaBench as an effective diagnostic tool. This work establishes affective hallucination as a distinct safety concern and provides resources for developing LLMs that are both factually reliable and psychologically safe. Warning: This paper contains examples of mental health-related language that may be emotionally distressing.

Recent research has focused on addressing multimodal hallucinations in Large Vision-Language Models (LVLMs) by extending Direct Preference Optimization (DPO) to incorporate visual preference supervision. However, these methods often lack fine-grained visual contrast mechanisms and rely on single-margin optimization. This in turn limits their ability to capture precise visual semantics and results in weak multimodal alignment. To address these issues, we propose Joint Multimodal Preference Optimization (JoMPO), a novel optimization framework that symmetrically integrates a text-conditioned preference loss with a visual ranking-based objective. JoMPO leverages semantically contrastive image–text pairs and listwise ranking over multiple visual contexts, enabling fine-grained visual grounding and more robust cross-modal alignment. To support this framework, we introduce the Visual–Textual Contrast (VTC) dataset, consisting of image pairs that are semantically similar but visually distinct, each paired with a contextually grounded textual response. When trained with only 5k contrastive pairs, JoMPO consistently demonstrates superior performance across diverse benchmarks, highlighting its effectiveness in mitigating hallucinations and improving image-text alignment in LVLMs.

Anomaly Detection (AD) focuses on detecting samples that differ from the standard pattern, making it a vital tool in process control. Logical anomalies may appear visually normal yet violate predefined constraints on object presence, arrangement, or quantity, depending on reasoning and explainability. We introduce LogicQA, a framework that enhances AD by providing industrial operators with explanations for logical anomalies. LogicQA compiles automatically generated questions into a checklist and collects responses to identify violations of logical constraints. LogicQA is training-free, annotation-free, and operates in a few-shot setting. We achieve state-of-the-art (SOTA) Logical AD performance on public benchmarks, MVTec LOCO AD, with an AUROC of 87.6% and an F₁-max of 87.0% along with the explanations of anomalies. Also, our approach has shown outstanding performance on semiconductor SEM corporate data, further validating its effectiveness in industrial applications.