memory-hygiene — Stale Memory Detection and Re-Verification

Addresses the "stale-but-confident" failure mode: verified information that silently drifts while remaining highly ranked in retrieval — identified as a harness failure mode in Scaling the Harness in Agentic AI (arXiv:2605.26112).

Sister asset: arXiv:2607.01935 (A-TMA: Decoupling State-Aware Memory Failures in Long-Term Agent Memory, Shi/Tang/Tung 2026) names this exact class "ghost memory" — outdated / current / transitional facts intermixing during retrieval — and benchmarks it (LTP; conflict-accuracy +0.240, temporal-F1 0.03→0.17 on LoCoMo with a bank-maintenance layer). It is the tighter external frame for what memory-hygiene detects: this skill is the FH-native detection + archival pass over the same failure A-TMA formalizes at the retrieval layer. (Source-verified 2026-07-03; a broader bounded-memory testbed, arXiv:2607.02255 AgenticSTS, was considered and set aside — it targets context-assembly, not staleness, so it is not the sister here.)

FH is an online-first harness. Its memory entries point to live external resources (GitHub repos, arXiv records, Zenodo DOIs, monitoring routines). These drift faster than in offline systems — which makes hygiene both more necessary and more tractable (live re-verification is possible).