Semantic caching reduces LLM inference costs by returning cached responses for semantically similar queries, but current evaluation using PR‑AUC only ranks scores and ignores usability at a fixed threshold, leading to poor deployment choices. The authors propose a cache‑aware metric, Precision–Cache Hit Ratio (P‑CHR) AUC, and an Operational Retention Rate (ORR) to measure how offline ranking quality translates to deployment. They decompose the operational gap into a recoverable threshold‑utility component and an irreducible structural component, showing that the gap is driven by the training objective rather than data scale and can be mitigated by score re‑normalization or objective changes, framing model selection as a threshold‑utility problem.
By Aditeya Baral, Radoslav Ralev, Iliya Sotirov Zhechev, Srijith Rajamohan, Jen Agarwal
arXiv:2606. 19719v1 Announce Type: cross Abstract: Semantic caching cuts LLM inference costs by serving a cached response to semantically similar queries.
By Aditeya Baral, Radoslav Ralev, Iliya Sotirov Zhechev, Srijith Rajamohan, Jen Agarwal
Current LLM memory systems treat all personal facts identically, so stores grow without bound while retrieval precision degrades. The core challenge is lifecycle management: which memories should pers...
Fortunate Recall (FR) introduces an ontology-driven policy layer that categorizes personal facts into over ten behavioral types and applies tailored lifecycle rules—such as differential decay, supersession, and event-time validity—to manage memory persistence in large language models. The FR-Bank implementation, independent of underlying infrastructure, achieves a 76.9% pass rate on the new LifecycleBench benchmark and improves LongMemEval-S performance, while significantly reducing confabulation rates compared to prior systems. Ablation studies show that the generic lifecycle metadata drives correctness, whereas the behavioral ontology enhances calibration and reduces downstream hallucinations.
By Ansuman Mullick, Eray T\"uz\"un
arXiv:2609.35908v1 Announce Type: cross
Abstract: Semantic caches reduce LLM serving costs by reusing previously generated answers for semantically similar queries. However, retrieval is based solely...
By Zihan Zhang, Shuangjie Yao, Zesen Liu, Zhixiang Zhang, Wai Ip Lai, Dung Hiu Hilton Yeung, Chun Kit Zhang, Fuchen Ma, Yuanyuan Yuan, Yu Jiang, Dongdong She
arXiv:2606. 26511v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) gives agents access to accumulated knowledge, but has no model of time.
By Neeraj Yadav