Fail-Closed Lowering of Resident KV Claims onto LLM Serving Runtimes
On the map: Efficient LLM inference and serving → KV cache optimization
Abstract (arXiv)
LLM serving runtimes increasingly expose KV-cache primitives that resemble future-reuse controls: retention priority, TTL-like duration, host or storage offload, block events, active no-evict scheduling, and KV-aware routing. This paper argues that such primitives are weaker than accepted future-KV obligations. A runtime can expose priority, offload, events, and routing without accepting responsibility for a future reuse claim. We study ResidentClaim lowering: when a runtime primitive, trusted adapter, or patch can be treated as satisfying an accepted claim about future KV reuse. A conformant lowering must bind behavior to accepted claim identity, a materialization predicate, ordered lifecycle events, and claim-scoped outcomes. We contribute a fail-closed lowering relation, checker, descriptor format, and bad-lowering suite that classify runtime/mode mappings as native conformance, adapter-observational evidence, adapter-policy evidence under controlled pressure, approximation substrate, rejected mapping, or unknown evidence. The checker validates manually curated, anchored runtime descriptors against obligation bundles; it does not prove that unaudited runtime behavior is complete. Public TensorRT-LLM, SGLang/HiCache, and Dynamo expose strong substrates and selected adapter positives, but not native ResidentClaim conformance. The positive systems witness is a local patched vLLM connector/scheduler-boundary mechanism: claim metadata flows through real in-process offload/load behavior, and controlled same-claim restoration failure reaches vLLM's invalid-KV-load path and becomes an ordered claim-scoped fail-closed outcome. The result is a calibrated semantics boundary, not a production performance claim or a compatibility survey.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in KV cache optimization, one of 12 directions in Efficient LLM inference and serving. Builds on 0 of its 3 references that are in the map. First posted on arXiv 31 May 2026.
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