arXiv Machine Learning
Aug 31

Closing the Operational Gap in Semantic Caching

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 AI
Oct 5

JOVE: Joint Execution and Verification for Resource-Aware LLM Task Graphs

JOVE is an online framework that jointly assigns large language model (LLM) executors and selects intermediate outputs for paid verification in directed acyclic task graphs. It balances immediate execution costs with future learning by solving per-query mixed‑integer linear programs and updating task‑dependent LLM quality estimates through verification feedback. Experiments on four reasoning benchmarks show that JOVE matches standard inference accuracy while cutting average cost and latency by at least 3.17×.

By Haoran Zhang, Dongjun Kim, Seohyeon Cha, Kevin S Chan, Ananthram Swami, Gustavo De Veciana, Haris Vikalo
arXiv AI
Aug 11

TreeHop: Efficient Embedding-Level Query Rewriter

arXiv:2504. 20114v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) systems face significant challenges in multi-hop question answering (MHQA), where complex queries require synthesizing information across multiple document chunks.

By Zhonghao Li, Kunpeng Zhang, Jinghuai Ou, Shuliang Liu, Xuming Hu