memory”—we can only determine if an LLM remembers something after posing a question and examining the output. Otherwise, it is impossible to know. Humans operate similarly: we can only verify our memory by answering specific questions; otherwise, we cannot assess it. For example, if you ask someone how many poems they remember, they might not be able to give a precise answer, but if you ask them if they remember a specific poem, they can usually respond. As shown in Figure 3, although some predictions are incorrect, the results still align with linguistic conventions and match the poem's title to some degree. This could be seen as a form of creativity in the absence of exact memory, which is also a form of reasoning. Thus, we propose that the brain operates like a model that dynamically fits inputs. In some sense, the mathematical model of the human brain may be similar to, or even a more advanced version of, the dynamic UAT model based on Transformers. However, we believe their underlying mechanisms are the same: they both dynamically fit corresponding outputs based on inputs. So why do LLMs seem to perform poorly in reasoning tasks? We believe there are three main factors: model size, data quality and quantity, and model architecture. Model Size: In practice, larger LLMs tend to be more powerful. Theoretically, as shown in the UAT2LLMs Wang & Li (2024b) and UAT2Parallel Wang & Li (2024a) models, the more layers a model has, the stronger its dynamic fitting ability becomes, resulting in better performance. Data Quality and Quantity: In practice, current LLMs have improved greatly due to train-ing on vast amounts of data. The larger and higher quality the dataset, the stronger the model's performance. From the perspective of human learning, humans spend decades in education, from primary school through university. In terms of language exposure, humans are immersed in language from birth, and without this exposure, we wouldn't be able to develop strong language skills. Model Architecture: Current models often consolidate all tasks into a single, sequential network, which can result in limited functionality or task complexity. Research suggests that the human brain is modular, with different regions responsible for different tasks. Sim-ilarly, we could design multiple large models specialized for different tasks, and then use the UAT2Parallel framework to enable parallel processing among these models, managed by a central control unit. Since we propose that both LLMs and the brain function as dynamic models that fit inputs, why build such dynamic models? What are the advantages of this approach? We believe that this dynamic fit-ting capability gives the brain infinite possibilities. The brain doesn't need to remember everything; it only needs to focus on what's important. Imagine if a newborn's brain were pre-loaded with the weights of its parents; there would be no need to fit the world because most of the external environ-ment remains constant. In such a scenario, the brain's weights would hardly ever be updated, and the person would lose creativity. Dynamic fitting, however, bestows the brain with creativity. Since not everything in the brain is correct, continuous interaction with the outside world updates these weights. One of these updates might bring us closer to the truth, allowing us to explore new ideas and eventually achieve innovation. A great example of dynamic fitting in the brain is Henry Molaison Scoville & Milner (1957); Victor et al. (1961); Milner & Klein (2015). After his hippocampus Bliss & Collingridge (1993); Squire (1992); Erickson et al. (2011); Eckardt (1980) was damaged, he could no longer form new long-term memories, though his existing memories remained intact. We believe that the hippocampus acts as a switch controlling whether the weights responsible for long-term memory in the brain can be updated. Once the hippocampus is damaged, the brain's weight parameters can no longer change, meaning that while past inputs (before the hippocampal damage) can still produce corresponding outputs (i. e., recalling past events), the inability to update weights prevents the formation of new memories. 5 C ONCLUSION In this paper, we demonstrate that LLMs possess memory capabilities, which are enabled by their Transformer-based architecture. This architecture functions as a dynamic fitting UAT model, with a 7