论文提出LiveMem，给LLM加了个永久记忆状态，上下文窗口再长也不怕忘事。在LongMemEval上跑分领先，值得一看。
LiveMem为预训练全注意力大语言模型引入独立于活动上下文的记忆状态，主注意力路径只保留有界KV窗口。在LongMemEval基准上，LiveMem在对比系统中取得领先总分。即使支持证据已从当前上下文移除，LiveMem仍能依据记忆状态正确作答。证据距离分析表明，有用信息可持久保留在活动窗口之外。
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 while the main attention path retains a bounded KV window. Context turnover and memory state maintaining, memory-oriented post-training, and state-aware serving jointly make this memory state load bearing after its originating tokens are released. Our experiments show that LiveMem achieves leading overall performance among evaluated systems and other intrinsic memory methods. Experiments on LongMemEval show that LiveMem is able to answer the question based on the memory state, even when the supporting evidence has been removed from the current context, and evidence-distance analysis shows that useful information persists beyond the active window. LiveMem thus establishes state continuity as a distinct and complementary abstraction for continual LLM inference.