The field of foundation models is moving towards addressing the challenges of disentangling physical phenomena from instrumental distortions in time series data, as well as improving the reasoning capabilities of large language models. Researchers are exploring new architectures and techniques to enhance the generalization and adaptation of foundation models, particularly in heterogeneous or multi-instrument settings. Notably, the development of causally-motivated foundation models and the use of inductive bias probes are showing promise in uncovering deeper domain understanding. However, state-of-the-art large language models are still struggling with simple reasoning tasks, highlighting the need for further research in this area. The concept of linear separability ceiling is also being investigated, revealing that the limitation is not due to poor perception, but rather failures in the language model's reasoning pathways. Noteworthy papers include:

• A paper that presents a causally-motivated foundation model that disentangles physical and instrumental factors using a dual-encoder architecture, demonstrating significant improvements in downstream prediction tasks.
• A paper that introduces the concept of inductive bias probes to evaluate foundation models, finding that these models can excel at training tasks yet fail to develop inductive biases towards the underlying world model.
• A paper that investigates the linear separability ceiling of Visual-Language Models, providing a new lens for VLM analysis and showing that robust reasoning is a matter of targeted alignment, not simply improved representation learning.