The field of artificial intelligence is witnessing significant advancements in language understanding and reasoning, with a focus on developing more robust and interpretable models. Researchers are exploring new architectures and approaches that combine the strengths of large language models (LLMs) with symbolic reasoning and logic-based systems. This integration enables models to decompose complex queries into verifiable sub-tasks, orchestrate reliable solutions, and mitigate common failure modes. The development of open-source systems that support full speech-to-speech, multi-turn dialogue with integrated tool use and agentic reasoning is also a notable trend. Furthermore, hierarchical decision-making frameworks and reinforcement learning-based reasoning models are being studied to improve the efficiency and effectiveness of language understanding and generation. Noteworthy papers include AURA, which introduces an open-source, speech-native assistant capable of completing complex tasks through dynamic tool invocation and multi-turn conversation, and Do LLMs Dream of Discrete Algorithms?, which proposes a neurosymbolic approach that augments LLMs with logic-based reasoning modules. Additionally, Agent-as-Tool presents a hierarchical framework that detaches the tool calling process and the reasoning process, enabling the model to focus on verbally reasoning while handling tool calling processes separately.