The field of neurosymbolic AI is moving towards the development of more reliable and verifiable knowledge graphs. This is being achieved through the use of hybrid approaches that combine the strengths of symbolic and neural network-based methods. One of the key challenges in this area is the automation of knowledge graph construction, which is being addressed through the use of large language models and verifiable contracts. Another important aspect is the development of new architectures and frameworks that can handle the complexities of real-world data and ensure data consistency and compliance with regulations such as GDPR. Noteworthy papers in this area include: GOFAI meets Generative AI, which presents a transparent hybrid solution that combines the recall capacity of large language models with the precision of symbolic systems. Beyond DNS: Unlocking the Internet of AI Agents via the NANDA Index and Verified AgentFacts, which describes a new architecture for discoverability, identifiability and authentication in the internet of AI agents. HyDRA: A Hybrid-Driven Reasoning Architecture for Verifiable Knowledge Graphs, which proposes a hybrid-driven reasoning architecture designed for verifiable KG automation.