2 citations · 2 across the 4 of their papers we have counts for
6 papers
Confining Nondeterminism: AI-Driven Research Systems as DBMSs for Reliable, Non-Wasteful, Transparent, and Collaborative Research [Vision]
Kyoungmin Kim, Anastasia Ailamaki
LLM agents that conduct research (proposing ideas, writing and running code, analyzing results) can already carry a study from research question to figures, yet cannot be fully tru…
Saving GPU Hours in LLM Inference System Development and Online Workloads with Simulation and DBMS-Inspired Cache Replacement Policies
Kyoungmin Kim, Jiacheng Li, Kijae Hong +3
LLMs are increasingly used world-wide from daily tasks to agentic systems and data analytics, requiring significant GPU resources. While LLM inference systems are capable of servin…
Fast LLM-Based Semantic Filtering: From a Unified Framework to an Adaptive Two-Phase Method
Kyoungmin Kim, Martin Catheland, Anastasia Ailamaki
Evaluating a natural-language yes/no predicate over a document corpus under an accuracy target - the semantic filter - is a cornerstone of LLM-based data processing. Calling the LL…
Fast Approximate Vector Joins via Offline-Online Co-Design
Kyoungmin Kim, Lennart Roth, Liang Liang +1
Vector joins - finding all vector pairs between a set of query and data vectors whose distances are below a given threshold - are fundamental to modern vector and vector-relational…
QVCache: A Query-Aware Vector Cache
Anıl Eren Göçer, Ioanna Tsakalidou, Hamish Nicholson +2
Vector databases have become a cornerstone of modern information retrieval, powering applications in recommendation, search, and retrieval-augmented generation (RAG) pipelines. How…
Trustworthy and Efficient LLMs Meet Databases
Kyoungmin Kim, Anastasia Ailamaki
In the rapidly evolving AI era with large language models (LLMs) at the core, making LLMs more trustworthy and efficient, especially in output generation (inference), has gained si…