The recent developments in the research area of language models and their understanding of semantic roles and grammatical patterns indicate a shift towards more nuanced evaluations and theoretical unification. There is a growing emphasis on assessing the cognitive and functional efficiency of language models, particularly in their ability to handle thematic fit and argument roles. Innovations in evaluating these models through psycholinguistic datasets and chain-of-thought reasoning are advancing the field, revealing insights into the models' strengths and limitations. Notably, the integration of semantic encoding and agreement-based predictability into a unified information-theoretic framework is a significant theoretical advancement, suggesting that future models may need to balance semantic accuracy with processing efficiency. Additionally, the exploration of filler-gap dependencies in neural language models underscores the importance of linguistic inductive biases in language acquisition modeling. These trends collectively push the boundaries of how we understand and develop language models, moving towards more human-like processing capabilities and deeper theoretical insights.