Under a deterministic canonical-witness regime, a query-local projection theorem and an exact residual-leaf law are proved: recovery fails exactly when an erased base leaf retains a cache-free path to the query.
Abstract
We study reliable query recovery under independent premise erasures in semantically transparent caching systems, where every cached object must be a logical consequence of the premise base. Recovery succeeds only when the query remains derivable from surviving premises and the cache. Under a deterministic canonical-witness regime, we prove a query-local projection theorem and an exact residual-leaf law: recovery fails exactly when an erased base leaf retains a cache-free path to the query. Single-query design becomes weighted partial path interception. For shared workloads, we introduce semantic modules and derive exact reliability laws under joint and maximal-error criteria. The shared-module cache is exactly optimal under exact module routing and homogeneous costs, whereas optimal selection in general derivation DAGs is NP-complete at depth two. Against a coded benchmark recovering workload-relevant leaf payloads, MDS parity caching is optimal up to one packet. Leaf-only transparency incurs a first-order overhead inversely proportional to the erasure rate; shared modules multiply that inverse-erasure-rate scaling by the module-to-leaf cost ratio divided by the number of protected leaves. A Datalog instance and Monte Carlo checks illustrate the theory. For derivation-structured content, the results provide exact stochastic-erasure counterparts of function-correcting storage and an exact distributional quantification of maximal recoverability.
The cross-encoder study shows that thresholds do not transfer between embedding models, and LFU is the strongest simple default in this protocol; deployment decisions should first establish answer validity and then test sub-point policy differences with exact search.
Y. Kulkarni, Shubham Harkare, A. Babu· 0 citations
It is proved that no estimator computable from the information a deterministic scheme retains is consistent for its own eviction error: evicted values can be altered so that everything retained is unchanged while the true attention-output error grows without bound.
Results show that object-value signals rank what to retain, while persistent responsibility determines which group bears reclamation pressure, which shows that object-value signals rank what to retain, while persistent responsibility determines which group bears reclamation pressure.
When affinity recovers too little KV work, its residual load skew reduces or erases the improvement, so gating any deployment with a shadow replay rather than enabling affinity from workload statistics alone is recommended.
This work presents a robust KV cache management framework for LLM serving that jointly optimizes GPU parallelism configuration, KV cache reservation per request class, request routing across heterogeneous serving groups, and prefix caching for shared prompts that incorporates latency SLO constraints and captures the in...
Jiaming Cheng, Duong The Do, D. Nguyen· arXiv.org· 1 citation· ⚡1
RestoreKV is introduced, which complements this selection-based formulation with learned restoration with learned restoration under the same total KV budget, and substantially reduces compression-induced degradation.