SeeFar: Separating Evidence, Temporal Records, and Relational State in Long-Term Agent Memory

Persistent language-model agents must distinguish retrieved experience, tentative evidence, committed records, and relationship-specific adaptation. We study this distinction as a memory representation problem and present SeeFar, a four-layer architecture with an explicit consolidation path and a parallel relational state. The architecture combines heat-based semantic retention, confidence-threshold admission, bitemporal versioning, and mean-reverting relational updates, with asynchronous maintenance as an execution policy. We clarify the assumptions under which these mechanisms provide history preservation and conditional mean stability, while distinguishing both properties from factual correctness and behavioral persona consistency. Across five evaluated backbones, SeeFar attains the highest open-domain LoCoMo F1 among the compared configurations and the highest Temporal F1 in a separate diagnostic workload. The results are task-dependent: SeeFar does not lead the LoCoMo temporal category on any backbone, evidence-promotion precision remains approximately 52%, and persona-oriented ablations show mixed outcomes. Recorded foreground latencies are lower in the diagnostic comparison, but do not establish a reduction in total computational work. These findings support studying memory lifecycle and relational scope jointly, while exposing limits of confidence-based admission, component attribution, and generalization beyond the evaluated settings.