AffectLedger: Preserving Affective Evidence and Reconstructing Longitudinal Context for LLM Agents

For memory-augmented LLM agents, retaining a longitudinal history does not guarantee that downstream reasoning receives the relevant evidence or its cross-time relationships. This gap is especially salient in affective histories, where interpreting local expressions often requires relating observations across interactions. We investigate longitudinal context construction through two complementary dimensions: historical evidence availability and cross-time representation. We introduce AffectLedger, a host-modular memory layer that separates persistent evidence preservation from access-time longitudinal context construction. At write time, it retains source-grounded local affective observations in an append-only Ledger. At memory access, it first assembles relevant observations into a bounded historical evidence sequence, then reconstructs compact, evidence-linked claims that make their supported cross-time relationships explicit. These claims supplement the existing memory Host's context without modifying its native memory procedures. Across five heterogeneous Hosts and two CloneMem history scales, AffectLedger improves accuracy in all ten comparisons (+3.10–13.98 percentage points). Controlled studies show that assembled evidence contributes utility before reconstruction, while relation-aware reconstruction produces shorter supplements with accuracy comparable to generic reconstruction. Compared with a persistent-account-plus-raw design, AffectLedger matches accuracy at 100k using 92.3% fewer added downstream tokens, with an accuracy–context trade-off at 500k. Additional benchmarks show task-dependent benefits in integrating and updating distributed histories. These findings highlight the complementary utility of constructing explicit longitudinal context from revisitable evidence.