Truth in the Trace: Auditable and Lifecycle-Typed Memory for AI Agents

A conversational assistant must remember user-provided information and keep it accurate as circumstances change. This requires two capabilities at once: absorbing facts that fit existing knowledge, and restructuring memory when new facts conflict, without discarding the displaced values. Existing memory models satisfy only one demand at a time. Append-only stores keep everything but never restructure, so stale and current values become indistinguishable. Overwriting and re-summarizing stores do restructure, but they erase past values and their provenance. We propose FactTrace, a memory architecture built on knowledge equilibration, the balance between absorbing what fits and reorganizing for what does not, which performs both moves under a single governor and maintains each attribute as an evolving structure. A fitting value is absorbed, a conflicting value supersedes the old one, and the old value is demoted rather than deleted, retaining its provenance and replacement history. Because the current value is always explicit, the assistant conditions on the present state correctly even after many changes. Superseded values stay available, and because stored facts are never re-summarized, fine-grained distinctions survive. We evaluate on two benchmarks, one for personal state tracking and one for truthfulness under pressure, across five underlying models. FactTrace outperforms strong memory baselines on both axes, significantly so in the controlled single-backbone comparison in which we test significance, while across the remaining backbones the same ordering holds as a consistent point-estimate margin that we do not test individually. Staying truthful costs raw goal completion (6.55 to 4.68 on a 0–10 scale), and our margin on the benchmark's truthfulness-gated composite is directional but not significant. A per-period decomposition, a read-time causal ablation, and a head-to-head retrieval analysis each tie a gain to the corresponding non-destructive design choice. Our code is available at: https://anonymous.4open.science/r/FactTrace-92CB/