most citedOptimizing LLM Queries in Relational Data Analytics Workloads

6 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025

vAttention: Verified Sparse Attention

Aditya Desai, Kumar Krishna Agrawal, Shuo Yang +5

State-of-the-art sparse attention methods for reducing decoding latency fall into two main categories: approximate top- (and its extension, top-) and recently introduced samp…

cs.AI2025★ 1 cited

The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks

Alejandro Cuadron, Dacheng Li, Wenjie Ma +13

Large Reasoning Models (LRMs) represent a breakthrough in AI problem-solving capabilities, but their effectiveness in interactive environments can be limited. This paper introduces…

cs.LG2025

vCache: Verified Semantic Prompt Caching

Luis Gaspar Schroeder, Aditya Desai, Alejandro Cuadron +7

Semantic caches return cached responses for semantically similar prompts to reduce LLM inference latency and cost. They embed cached prompts and store them alongside their response…

cs.LG2024★ 6 cited

Optimizing LLM Queries in Relational Data Analytics Workloads

Shu Liu, Asim Biswal, Amog Kamsetty +8

Batch data analytics is a growing application for Large Language Models (LLMs). LLMs enable users to perform a wide range of natural language tasks, such as classification, entity…

cs.AI2024

Alto: Orchestrating Distributed Compound AI Systems with Nested Ancestry

Deepti Raghavan, Keshav Santhanam, Muhammad Shahir Rahman +7

Compound AI applications chain together subcomponents such as generative language models, document retrievers, and embedding models. Applying traditional systems optimizations such…