Large Language Models (LLMs) have revolutionized how we interact with machines, demonstrating impressive abilities in writing, translation, and even coding. However, one area where they consistently stumble is math. While LLMs can string together impressive chains of thought, performing calculations or truly understanding the continuous nature of numbers remains a challenge. A new research paper explores innovative ways to bridge this gap by interleaving text and number embeddings within the model's architecture. The researchers propose a method called Multimodal Decoding (MMD), building upon previous work that treated numbers as continuous entities rather than discrete tokens. Their key innovation lies in employing an MLP to provide more expressive numerical embeddings, giving each number its own distinct direction in the embedding space. Imagine the number '2' associating itself with words like 'apples' or 'pairs,' while '0.0001' might gravitate towards 'infinitesimal' or 'fraction.' This approach allows the model to learn the context of numbers more effectively. Furthermore, the researchers introduce a 'routing layer' that distinguishes between text and numerical embeddings, similar to how a switchboard directs calls. This layer helps the model differentiate between word and number distributions, enhancing its ability to perform arithmetic operations. Experiments show promising results. The MMD model outperforms traditional methods on standard mathematical tasks, especially when dealing with a wide range of magnitudes. Visualizing the results reveals a striking difference. While standard models frequently miscalculate by orders of magnitude, the MMD model's predictions closely track the correct answers. This suggests a more robust understanding of numerical relationships. One intriguing finding is the improvement achieved by using logarithmic transformations of numbers. This seems to further refine the model’s ability to differentiate text and numerical data, especially in complex scenarios involving both text and numerical answers. While this research marks a significant step forward, challenges remain. The current experiments relied on a relatively simple dataset, and future work will explore more complex mathematical reasoning. The team also plans to investigate a wider range of numerical encodings and architectures to push the boundaries of LLMs' mathematical capabilities. This research highlights a critical frontier in AI development: bridging the gap between symbolic reasoning and statistical learning. If LLMs can truly grasp the meaning and relationships between numbers, it could unlock even more powerful applications in science, engineering, and beyond. The quest to give AI true number sense continues.