The field of cyber threat detection and intelligence is rapidly evolving, with a focus on developing innovative approaches to identify and mitigate emerging threats. Recent research has explored the application of machine learning and large language models to improve threat detection, intelligence gathering, and incident response. Notably, the use of contrastive learning, hybrid CNN-LSTM models, and multimodal frameworks has shown promise in detecting threat behaviors, identifying security events, and verifying the credibility of cyber threat intelligence. Additionally, the development of comprehensive benchmarks, such as AICrypto, has enabled the evaluation of large language models' cryptographic capabilities, highlighting their potential for advancing the field. Furthermore, research has emphasized the importance of integrating human expertise and explainability into automated threat detection and analysis systems, ensuring that these systems are transparent, reliable, and effective in real-world threat environments. Noteworthy papers include: CLIProv, which introduces a novel approach for detecting threat behaviors in a host system by aligning the semantics of provenance logs with threat intelligence. AICrypto, a comprehensive benchmark for evaluating the cryptographic capabilities of large language models, reveals that state-of-the-art models match or surpass human experts in certain tasks. EventHunter, an unsupervised framework that automatically detects, clusters, and prioritizes security events discussed across hacker forum posts, demonstrates the potential for transforming disparate discussions into structured, actionable intelligence.