Researcher Argues Memory Mechanisms, Not Scaling Laws, Are LLMs' Next Frontier

A researcher and developer has argued that capability gains in large language models stem from paradigm shifts in understanding intelligence — such as the Transformer and chain-of-thought reasoning — rather than scaling laws alone. The author contends that both breakthroughs essentially mimic higher human cognitive functions, with the Transformer simulating semantic understanding and CoT replicating explicit logical reasoning. Looking ahead, the author proposes that giving LLMs a human-like memory mechanism — distinct from the attention mechanism — is the critical next step. The argument draws on how human memory retains fuzzy state information rather than full detail, enabling reasoning and long-range control, a capability current attention windows cannot replicate. The author conducted a 3-million-token recall experiment to support the case that memory, not expanded context windows alone, is what LLMs fundamentally lack.