Machines With Human-Like Memory

Most AI systems still treat memory as an afterthought: short-lived context windows, opaque hidden state, or retrieval layers that struggle to represent what should persist, what should fade, and how past experience should shape later behavior. That makes it difficult to study memory itself as an architecture rather than a side effect of prompting or scale.

Machines With Human-Like Memory investigates AI memory architectures through successive concrete systems: benchmark environments, heuristic agents, reinforcement-learning agents with explicit memory systems, and later temporal knowledge-graph approaches. Together, those projects turn memory into something that can be modeled, compared, and improved directly rather than left implicit inside a model.

This project forms the research base behind several later HumemAI efforts. It connects long-term questions about memory architecture to practical systems work and product direction, and it creates a coherent line of work rather than a set of disconnected papers.