Most discussions around long-term memory in LLMs focus on context size, retrieval pipelines, or better fact extraction. But what if we’re solving the wrong layer of the problem? Right now, LLM memory systems mostly store text chunks or embeddings. Even when we “promote” important information, we’re still promoting sentences. That’s storage optimization — not structural intelligence. What if instead we abstracted every meaningful interaction into a modeled scene? By scene, I mean something structured like: Actors involved Estimated intent Emotional intensity Moral polarity Confidence score Contextual relevance weight Instead of saving raw dialogue, the system stores weighted semantic events. Over time, those events form something more interesting than memory — they form a behavioral trajectory graph. At that point, the question isn’t: “What should be stored?” It becomes: “Given the trajectory so far, what future states are probabilistically emerging?” If certain emotional or decision patterns repeat, could the system simulate possible future behavioral states of an agent or even a user? Not deterministically — but as drift projections. That shifts the framing entirely: From memory scaling To episodic abstraction To trajectory-aware intelligence Maybe scaling tokens isn’t the real frontier. Maybe structured episodic modeling is. Curious where this would break — technically, computationally, or philosophically. submitted by /u/revived_soul_37
Originally posted by u/revived_soul_37 on r/ArtificialInteligence