Runtime Observability Contract for Heterogeneous Attention Memory

A new arXiv paper (2608.05863) introduces a runtime observability contract for heterogeneous attention memory in modern AI models. The contract covers four memory classes—latent caches, learned sparse selectors, recurrent states, and plain KV caches—using three operators. It is instantiated on six model configurations across five architecture families, composing per-stage bounds into an executable request-level risk ledger. The contract carries error metrics as types, ensuring composition only when metrics match; the authors' initial composed chain was rejected, and the repaired chain uses two proved bridges to cross metrics. Claims are certified, partially certified, or empirical, with composition inheriting the weakest tier, decided automatically by the machine. The paper reports results replayed over $12.4$ (likely a dataset or benchmark). This work addresses the challenge of monitoring and ensuring reliability in models with diverse memory mechanisms, which fail differently under compression.