The field of natural language processing is moving towards addressing the challenges of hallucinations and faithfulness in large language models (LLMs). Researchers are exploring innovative approaches to detect and mitigate hallucinations, which is crucial for real-world applications. A key direction is the development of novel annotation frameworks and benchmarks that can accurately evaluate the faithfulness of LLMs. Another area of focus is the creation of mechanistic detection methods that can disentangle the contributions of external context and parametric knowledge in LLMs. Additionally, there is a growing interest in applying LLMs to specific domains, such as radiology reporting and psychiatric diagnoses, where standardization and trustworthiness are essential. Noteworthy papers in this area include: The Gray Zone of Faithfulness, which proposes a novel faithfulness annotation framework, and InterpDetect, which explores a mechanistic detection approach based on external context scores and parametric knowledge scores. Process Reward Models for Sentence-Level Verification of LVLM Radiology Reports is also notable for its introduction of a sentence-level Process Reward Model adapted for vision-language tasks. Furthermore, Standardization of Psychiatric Diagnoses highlights the potential of fine-tuned LLMs and reasoning LLMs for clinical mental health diagnosis. RECAP is also noteworthy for its agentic pipeline designed to elicit and verify memorized training data from LLM outputs. A Multi-agent Large Language Model Framework demonstrates the effectiveness of an ensemble of LLM agents in assessing the performance of a clinical AI triage tool. From Prompt Optimization to Multi-Dimensional Credibility Evaluation showcases the importance of prompt optimization and credibility assessment in LLM-generated radiology reports.