This report highlights the recent developments in several related research areas, including program optimisation, large language models, symbolic regression, and personalized recommendations. A common theme among these areas is the focus on improving expressiveness, efficiency, and personalization.

In program optimisation, researchers are exploring new frameworks and methods to improve the analysis and verification of complex programs. Notable advancements include the introduction of e-graphs with bindings, differential logical relations, and trace formula implication. These techniques have the potential to revolutionize the way programs are optimised and verified, enabling the development of more efficient and reliable software systems.

In the field of large language models, researchers are working towards more personalized and context-aware interactions. Recent works have proposed innovative approaches, such as graph-based models and proactive conversation assistants, to enable language models to capture user-specific concepts and reason over relations among objects. New benchmarks and datasets have also been introduced to evaluate the performance of personalized language models.

The field of symbolic regression and program analysis is moving towards developing more advanced and efficient techniques for discovering mathematical expressions and reasoning about program behaviors. Researchers are improving the scalability and performance of symbolic execution, and exploring new approaches to symbolic regression, such as using pre-training frameworks and modular representations.

Finally, the field of personalized recommendations is shifting towards the integration of large language models to improve accuracy and transparency. Recent developments focus on addressing the cold-start problem and incorporating user preferences and behaviors into language model-based recommenders. Notable advancements include the development of memory-assisted language models and the use of cognitive architectures to simulate human decision-making.

Overall, these research areas are advancing towards more sophisticated and human-like capabilities, with a focus on improving expressiveness, efficiency, and personalization. The developments in these areas have the potential to significantly impact the way we design and interact with software systems, and will likely continue to evolve in the coming years.