Hybrid language models combine Transformer attention with recurrent, state-space, or online-updated components to lower long-context serving cost while preserving memory fidelity. Existing surveys mostly organize this space by architecture family, chronology, or efficiency technique, leaving workload-specific memory functions underspecified. We propose State-Evidence-Access (SEA) for this constraint: bounded State may lose distinctions needed by future queries not fixed in advance. Architectures therefore allocate active State, addressable Evidence, and a per-step Access budget to jointly support reusable structure, query-conditioned recovery, and effective use. This view helps explain why attention ratios or average perplexity can be misleading: a hybrid can save memory while losing the retrieval, state-update, or access function needed by the task. We survey hybrids by allocation locus—from key-value (K-V) and head-level mechanisms through layer, block, external-memory, and system-cache policies—and characterize their *function-granularity mismatch* risks. For workloads such as long-document QA, SEA attributes failure to missing evidence, unstable state updates, access bottlenecks, or poor integration.