长期智能体老是记错？TARL把记忆更新拆成五种动作，还带基准，比单纯写/忽略更靠谱。
TARL框架把记忆更新分成五种可执行动作：添加、忽略、修订、拒绝、延迟，替代原先单一的写/忽略二元判断。配套基准TARL-Mem提供细粒度动作标签和下一状态目标。在域内、跨源、时间、反事实和顺序五类评估中，TARL提升了动作预测和状态恢复效果，同时减少记忆污染、保留冲突证据。
TARL: Transaction-Aware Reliable Ledgers for Executable Memory Management in Long-Term Agents
Persistent memory helps long-term agents retain knowledge, yet a single update error can repeatedly distort future retrieval and reasoning. Most existing systems reduce memory updating to a binary Write/Hold decision, which cannot distinguish whether new information should be added, ignored, used to revise an outdated belief, rejected as unreliable, or deferred for verification. These choices may share the same binary label while producing fundamentally different memory states. We introduce TARL, a memory state update framework that maps each statement to one of five executable actions. TARL identifies the affected memory, resolves its temporal scope, compares source reliability, and updates accepted, pending, and rejected ledgers. It is further trained by comparing the memory states produced by alternative update operations, encouraging the model to select the operation that leads to the correct result. We also introduce TARL-Mem, a benchmark with fine-grained action labels and next-state targets. Across in-domain, cross-source, temporal, counterfactual, and sequential evaluations, TARL improves action prediction and state recovery, reduces memory pollution, preserves conflicting evidence, and limits cumulative corruption. The complete model implementation is provided in the supplementary material.