LiveMem: Maintaining Memory State Continuity in Long-Running LLM Inference
Long-running assistants and agents consume interaction streams that eventually outgrow the context. Existing context retention, summarization, and retrieval preserve access to selected history, but do not provide a persistent state over the full lifecycle when working context changes. We formulate this missing inference capability as \emph{state continuity under context turnover}: carrying computation forward through a fixed-capacity memory state whose lifetime is independent of the active context. We introduce an intrinsic memory method, \textbf{LiveMem}, which augments a pretrained full-attention LLM with a memory state that preserves the historical information over the whole lifecycle ...
| Authors | Zhichen Liu, Ruihan Sun, Hengjie Yang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 August 2026 |
| Last updated at source | 7 August 2026 |
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