To address this limitation, we present the Self-
Controlled Memory (SCM) system, which enables
Large Language Models (LLMs) to process text
of inﬁnite length without any modiﬁcation or ad-
ditional training. The input is partitioned into seg-
ments and fed to the LLM as observations (inputs).
The SCM expands the LLM with a long-term mem-
ory (archived memory), a short-term memory (ﬂash
memory), and a memory controller. The archived
memory preserves all historical information, while
the ﬂash memory captures real-time memory in-
formation from previous rounds. The memory
controller determines when and how to introduce
archived information, allowing the LLM to efﬁ-
ciently handle ultra-long text without sacriﬁcing
any essential information.
Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System