The field of natural language processing is moving towards more advanced and nuanced methods of logical reasoning, with a focus on improving the accuracy and reliability of language models. Researchers are exploring new approaches to address the limitations of current models, such as the use of modal logical neural networks and hypothesis-driven backward logical reasoning. These innovations aim to enhance the ability of models to reason about necessity and possibility, and to simulate human deductive thinking. Noteworthy papers in this area include: From Hypothesis to Premises: LLM-based Backward Logical Reasoning with Selective Symbolic Translation, which proposes a novel framework for integrating confidence-aware symbolic translation with hypothesis-driven backward reasoning. Modal Logical Neural Networks, which introduces a neurosymbolic framework that integrates deep learning with the formal semantics of modal logic, enabling reasoning about necessity and possibility. Addressing Logical Fallacies In Scientific Reasoning From Large Language Models, which introduces a dual-inference training framework that integrates affirmative generation with structured counterfactual denial, yielding systems that are more resilient and interpretable.