The field of code understanding and generation is rapidly advancing, with a focus on developing more efficient and effective models. Researchers are exploring alternative architectures, such as state-space models, to improve performance and reduce training data requirements. Another area of innovation is in the representation of gene sequences, where mixed granularities of coding units are being used to enhance genomic representations. Benchmarking is also becoming increasingly important, with new benchmarks being proposed for long context code generation and evaluations of code large language models. Additionally, there is a growing awareness of the environmental impact of using large language models in software development, with studies highlighting the need to minimize carbon footprint. Noteworthy papers include:

• BiGSCoder, which presents a novel encoder-only bidirectional state-space model for code understanding that outperforms traditional transformer models.
• DNAZEN, which proposes an enhanced genomic representation framework that learns from various granularities in gene sequences.
• YABLoCo, which contributes a new benchmark for long context code generation in C and C++.
• Comparative Analysis of Carbon Footprint, which compares the energy consumption of manual versus LLM-assisted code development and proposes strategies for minimizing carbon footprint.