For AI that supports long-term dialogue, remembering past goals has clear benefits. If a learning plan, work objective, or hope about life decided months earlier can be retained, the person does not need to explain everything again from the beginning. Past judgments can also be compared with the present situation. Yet “remembering something” and “treating it as knowledge that remains valid now” need to be distinguished.
A goal differs in nature from an event that occurred. The fact that “the person made this statement on a certain day” remains a fact about the past even if the person later changes their mind. By contrast, a statement such as “this person’s goal is X” includes a claim about the present state. If that state continues to be used without being updated merely because of an old statement, it may be historically accurate yet inaccurate as knowledge representing the person now.
A stored goal therefore needs time as well as content. Instead of retaining only “I want to start a business,” the system can represent “at a certain point, under particular reasons and conditions, the person had starting a business as a goal.” It can then distinguish, in light of later actions and responses from reality, whether the goal remains valid, requires reconfirmation, or has been explicitly changed by the person.
The relationship with reasons also matters. The same goal can mean different things depending on whether the person seeks a freer way of working, wants to increase income, or wants to build a business that solves a particular problem. If the goal is stored while its reasons are lost, the system cannot know what should be revalidated when conditions change. Conversely, if a reason is fixed as an absolute motive, another problem appears when the person’s later understanding changes. Reasons too must be treated as information with a point in time and a degree of certainty.
Updating here does not mean deleting the past goal. If the earlier record is erased when the goal changes, the path explaining why the change occurred is lost. Long-term understanding benefits from a history such as: what the person wanted before, which experiences changed what, and how they think now. The important point is to retain old information without confusing it with the present state.
AI’s own inference must also be distinguished from an explicit statement by the person. Even if AI infers from behavioral changes that “interest in the goal may be declining,” that is different from the person saying, “I have decided to abandon this goal.” If the two are stored with the same certainty, an inference may later be referenced as fact and unnecessarily steer the person’s self-understanding.
Knowledge representation therefore needs distinctions such as who stated the information, when it was stated, which experiences or reasons it is connected with, whether its present validity has been confirmed, and whether it is an inference or an explicit choice. Not every detail must be preserved, but differences that could change later judgment should not be lost. Compression should not merely reduce the amount of information; it should make the information manageable while preserving necessary relationships.
In Sai＊aus’s Internal Efficiency Expression, this changes the position of the goal. The goal is not a fixed input from which reasoning begins, but an element whose state can change through human experience. By relating the reasons supporting the goal, small actions, differences returned by reality, and changes in human understanding, the system can retain the context needed to reconsider whether the current goal should remain in force.
This design is not intended to make AI change human goals frequently. Rather, it prevents past goals from continuing automatically as present purposes without handing AI the authority to change them. It can indicate that revalidation is needed, present the difference between past and present, and return final meaning to the person. That boundary allows continuity of memory and the human capacity to change to coexist.
As AI comes to know more about a human being, the accuracy of its knowledge can no longer be measured only by how little it forgets. It must preserve past dreams accurately while distinguishing whether they remain valid now; retain changed goals as a history of change rather than overwriting the past; and avoid confusing AI inference with human choice. Knowledge that understands a person through time requires a structure that treats the human being not as a fixed portrait but as an updateable state.
The question “Should past dreams be stored?” is therefore not a binary choice between saving and deleting. They can remain as history, while their validity is checked again when they are used as present purposes. Memory can then become material for understanding how a person has changed rather than a mechanism that binds them to the past. Long-term AI knowledge design must make two things true at once: not losing the past, and not fixing the present to it.
Not answers. Understanding.