arXiv:2602. 02061v2 Announce Type: replace Abstract: Explosive demands for LLMs often cause user queries to accumulate in server queues, requiring efficient routing (query-LLM matching) and scheduling (query prioritization) mechanisms.
arXiv:2605. 07096v2 Announce Type: replace Abstract: Evaluating a new model on an existing benchmark is often necessary to understand its behavior before deployment.
arXiv:2607. 04281v1 Announce Type: cross Abstract: Semantic caching reduces the latency and cost of retrieval-augmented generation (RAG) by serving cached answers to semantically similar queries, but most existing methods do not model the time-varying freshness of open-web evidence.
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.
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×.
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.
arXiv:2606. 19719v1 Announce Type: cross Abstract: Semantic caching cuts LLM inference costs by serving a cached response to semantically similar queries.
arXiv:2509. 15676v2 Announce Type: replace-cross Abstract: In-context learning (ICL) has emerged as a powerful paradigm for adapting large language models (LLMs) to new and data-scarce tasks using only a few carefully selected task-specific examples presented in the prompt.
arXiv:2609.39329v1 Announce Type: new Abstract: Long-context inference with Large Language Models (LLMs) is bottlenecked by the linearly growing memory of the key-value (KV) cache. Existing compressi...
arXiv:2607. 19199v1 Announce Type: new Abstract: Offline reinforcement learning (RL) aims to learn an effective policy from a static dataset, but its performance is fundamentally limited by dataset coverage.
arXiv:2608. 07954v1 Announce Type: new Abstract: Large language models can answer knowledge-intensive questions more reliably when they are grounded with knowledge graphs, but systems such as Think-on-Graph and Reasoning-on-Graph repeatedly query the same graph neighborhoods across different questions.
The paper introduces compositional online learning for semantic data processing systems, addressing the high cost and latency of large language model (LLM) calls. It proposes a framework that combines lightweight online-learning components—such as memoization, per-call filter-ordering, and per-batch cascade-routing—within the LLM call boundary, allowing each component to make real-time decisions and update its models without exceeding the LLM round-trip time. A production case study in Cortex AISQL demonstrates that these components can reduce the per-row LLM cost by up to 8× compared to a baseline workload.