The field of language models is moving towards a greater emphasis on causal reasoning and abstraction, with a focus on developing formal frameworks and methodologies to improve model inference capabilities. Researchers are exploring the use of causal abstraction to formalize language model simulation and to develop new variations of causal abstraction. The role of in-context learning and pre-trained priors in Chain-of-Thought reasoning is also being investigated, with findings suggesting that models can learn reasoning structures and patterns, but heavily rely on pre-trained priors. Additionally, there is a growing interest in evaluating language models' inductive and abductive reasoning capabilities, with new benchmarks and metrics being proposed to assess their performance. Noteworthy papers include: Rethinking the Chain-of-Thought: The Roles of In-Context Learning and Pre-trained Priors, which explores the working mechanisms of Chain-of-Thought reasoning. CausalARC: Abstract Reasoning with Causal World Models, which introduces a new testbed for AI reasoning in low-data and out-of-distribution regimes.