9 citations · 18 across the 10 of their papers we have counts for
11 papers
SSRLive: Live Streaming Recommendation with Dynamic Semantic ID
Teng Shi, Zhaoheng Li, Yuanhang Qu +3
Live streaming has emerged as one of the fastest-growing forms of online media, enabling instant content broadcasting and real-time engagement between users and streamers. Despite…
Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning
Banghao Chi, Yining Xie, Mingyuan Wu +9
Spreadsheet systems (e.g., Microsoft Excel, Google Sheets) play a central role in modern data-centric workflows. As AI agents grow increasingly capable of automating complex tasks,…
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…
Aha Moment Revisited: Are VLMs Truly Capable of Self Verification in Inference-time Scaling?
Mingyuan Wu, Meitang Li, Jingcheng Yang +6
Inference time techniques such as decoding time scaling and self refinement have been shown to substantially improve mathematical reasoning in large language models (LLMs), largely…
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…
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…