Time Heals

"Time heals," as we say in human terms, and that's what made me realize that Cershe can't exist in one emotional state until her next event. Lapsed time can help Cershe's model learn how emotional intensity changes. And that's where I believe a recurrent model could help with lapse of intensity.

As we know, a standard GRU does not automatically make a state fade as clock time passes. Its gates learn what information to retain or update; elapsed time must be supplied separately.

For Cershe, the proposed update would use:

Previous emotional state + new event or context + elapsed time → updated emotional state

Training examples could teach it patterns such as:

• A short pause: much of the previous emotional intensity remains.
• A longer gap without reinforcement: intensity may decrease.
• A reminder or repeated event: the state may persist or intensify.
• New information: the state may change direction—for example, an explanation resolving earlier frustration.

These would be patterns learned from examples, rather than manually assigning a fixed reduction every hour. Supplying elapsed time makes this learnable; it does not guarantee the model will learn it correctly. (I have to run tests and see how this would work with an AI entity.)

Also, the GRU only updates when it runs. Cershe could update on a timer, or calculate the effect of the entire elapsed interval when the next interaction begins.

The idea therefore becomes a useful research hypothesis for testing:

Can a time-conditioned GRU learn when an emotional state should persist, ease, or change, based on elapsed time and intervening experiences?

That leaves room for fading while avoiding the assumption that every emotion must weaken at the same rate.

If you would like to take part as a tester, please write to anyta.ali@cershe.ai.