9 citations · 27 across the 14 of their papers we have counts for
16 papers
PAIR: Memory-Efficient and Fast Inference with Reading--Reasoning Fusion
Sejun Park, Haocheng Xia, Yongjoo Park
For context-based question answering, using one large model for both evidence extraction and answer reasoning requires substantial memory and computation. Existing approaches trade…
Efficient Iterative Retrieval with Heterogeneous Batching
Dohyun Park, Hubertus Franke, Daniel G. Waddington +2
Modern information retrieval increasingly employs both embedding and generative models to handle complex queries. However, current serving systems suffer from low throughput and po…
Passes Alone, Fails Together: Benchmarking Semantic Coordination in Parallel LLM-Agent Development
Haocheng Xia, Eugene Wu, Yongjoo Park
Parallel coding agents can produce patches that work alone but fail when merged. This happens when one agent changes an interface or rule that another agent still relies on. We stu…
Bolo: Verified Model Hub for Next-Generation AI Databases
Yunqi Li, Ila Petrovic, Yongjoo Park
Verified, ready-to-use inference pipelines are a cornerstone of future AI databases. They allow multi-modal databases to incorporate specialized language, vision, and tabular model…
SlotGuard: Stop Oversharing Private Local Context in LLM Agent Transcri
Haocheng Xia, Yongjoo Park
LLM agents can leak privacy (e.g., paths, emails) and credentials (e.g., API keys) as agent observations (e.g., tool outputs, shell logs, and file reads) are appended to provider-b…
LazyAttention: Efficient Retrieval-Augmented Generation with Deferred Positional Encoding
Haocheng Xia, Mihir Pamnani, Hanxi Fang +2
Key-value (KV) caching accelerates inference of large language models (LLMs) by reusing past computations for generated tokens. Its importance becomes even greater in long-context…
Cloud-Native Vector Search: A Comprehensive Performance Analysis
Zhaoheng Li, Wei Ding, Silu Huang +5
Vector search has been widely employed in recommender system and retrieval-augmented-generation pipelines, commonly performed with vector indexes to efficiently find similar items…
Chipmink: Efficient Delta Identification for Massive Object Graph
Supawit Chockchowwat, Sumay Thakurdesai, Zhaoheng Li +2
Ranging from batch scripts to computational notebooks, modern data science tools rely on massive and evolving object graphs that represent structured data, models, plots, and more.…
SIEVE: Effective Filtered Vector Search with Collection of Indexes
Zhaoheng Li, Silu Huang, Wei Ding +2
Many real-world tasks such as recommending videos with the kids tag can be reduced to finding most similar vectors associated with hard predicates. This task, filtered vector searc…
QStore: Quantization-Aware Compressed Model Storage
Raunak Shah, Zhaoheng Li, Yongjoo Park
Modern applications commonly leverage large, multi-modal foundation models. These applications often feature complex workflows that demand the storage and usage of similar models i…
MojoFrame: Dataframe Library in Mojo Language
Shengya Huang, Zhaoheng Li, Derek Warner +1
Mojo is an emerging programming language built on MLIR (Multi-Level Intermediate Representation) and supports JIT (Just-in-Time) compilation. It enables transparent hardware-specif…
Large-scale Evaluation of Notebook Checkpointing with AI Agents
Hanxi Fang, Supawit Chockchowwat, Hari Sundaram +1
Saving, or checkpointing, intermediate results during interactive data exploration can potentially boost user productivity. However, existing studies on this topic are limited, as…
Enhancing Computational Notebooks with Code+Data Space Versioning
Hanxi Fang, Supawit Chockchowwat, Hari Sundaram +1
There is a gap between how people explore data and how Jupyter-like computational notebooks are designed. People explore data nonlinearly, using execution undos, branching, and/or…
Cache-of-Thought: Master-Apprentice Framework for Cost-Effective Vision Language Model Reasoning
Mingyuan Wu, Jize Jiang, Haozhen Zheng +8
Vision Language Models (VLMs) have achieved remarkable success in a wide range of vision applications of increasing complexity and scales, yet choosing the right VLM model size inv…
Kishu: Time-Traveling for Computational Notebooks
Zhaoheng Li, Supawit Chockchowwat, Ribhav Sahu +2
Computational notebooks (e.g., Jupyter, Google Colab) are widely used by data scientists. A key feature of notebooks is the interactive computing model of iteratively executing cel…
ElasticNotebook: Enabling Live Migration for Computational Notebooks
Zhaoheng Li, Pranav Gor, Rahul Prabhu +3
Computational notebooks (e.g., Jupyter, Google Colab) are widely used for interactive data science and machine learning. In those frameworks, users can start a session, then execut…