Model: Qwen1. 5-0. 5B-Chat Output: Let us build a fire In the living room Where living is done. The kitchen plants need Mowing and the cabinets To be axed and split into Kindling. W e'll slink inside Dresses, apply the brightest Rouge. Prettiest conflagration. Label: When the day closes, my grief's a laundromat done tumbling away, or a dry cleaner, its pressed shirts hanging like ef figies, or flags of no and every nation under faux stars, neon constellations. I sweep the evening, hoping for a kind of incandescence, yet what the day takes is light and light. Input: Dictation: Author:Joseph O. Legaspi Title: Poetry Output: 晚出临川逢岭树，飞花乍望空搀玉。 追思往事今如梦，晚景凄凉事事休。 Label: 野寺寻春花已迟，背岩惟有两三枝。 明朝携酒犹堪醉，为报春风且莫吹。Input: Dictation: Dynasty: 唐代 Author: 李端 Title: 春晚游鹤林寺寄使府诸公Model: Qwen1. 5-0. 5B-Chat Figure 3: The process of Transformer. 3. 3 T HETOKEN LENGTH EFFECT Additionally, we believe that the length of the input text has a significant impact on the memory capabilities of LLMs—the longer the text, the harder it is to remember. To verify this, we set the input text length in the CN Poems dataset to be between 256 and 512 characters (we used Chinese text because the relationship between token length and the original text length is not fixed in English). After fine-tuning the model for 100 epochs on CN Poems, the results are shown in Table 2. It is evident that as the text length increases, the difficulty for the model to remember the content also increases. Table 2: The memory ability of Qwen1. 5-0. 5B-Chat, Qwen2-0. 5B-Instruct, Qwen2-1. 5B-Instruct, bloom-389m-zh, bloom-1b4-zh on CN Poems in the condition of longer prediction. Models Qwen1. 5-0. 5B-Chat Qwen2-0. 5B-Instruct Qwen2-1. 5B-Instructbloom-389m-zhbloom-1b4-zh CN Poems Acc 44. 9 56. 85 86. 95 68. 6 93. 65 4 A C OMPARISION BETWEEN HUMAN AND LLM S Based on the definition of memory provided in Section 3 and the experimental results, we believe that LLMs indeed possess memory capabilities, and this ability shows no fundamental difference from human memory. Building on this, we extend the concept of memory in the brain to other cognitive abilities, such as social skills, imagination, and creativity. All of these can be attributed to reasoning ability based on existing knowledge. Currently, mainstream academia holds that LLMs either lack human-like reasoning abilities or per-form poorly in this regard. So, do LLMs actually possess reasoning capabilities similar to humans? Why do LLMs seem to exhibit weak reasoning skills? Before formally addressing this question, let's define reasoning ability in simple terms: Reasoning Ability: The ability to generate results consistent with previously learned knowledge based on specific inputs. From this definition, we can identify several key factors in reasoning ability: the knowledge learned, the specific input, and the ability to produce results aligned with the learned knowledge. According to this definition, the memory capabilities of LLMs can also be considered a form of reasoning. As shown in Figure 2, LLMs can iteratively learn to reconstruct a complete poem based solely on its title and author. These poems are not stored in a particular weight within the model but are dy-namically generated based on the input. This is why we describe LLMs' memory as ”Schr ¨odinger's 6