25 citations · 53 across the 23 of their papers we have counts for
9 papers · 1 filter
Cornfigurator: Automated Planning for Any-to-Any Multimodal Model Serving
Jeff J. Ma, Jae-Won Chung, Jisang Ahn +5
Any-to-Any models are an emerging class of multimodal models that accept combinations of text and multimodal data as input and generate them as output, introducing heterogeneous co…
Sphinx: Efficiently Serving Novel View Synthesis using Regression-Guided Selective Refinement
Yuchen Xia, Souvik Kundu, Mosharaf Chowdhury +1
Novel View Synthesis (NVS) generates novel views of a scene from a sparse set of input images and is a key component in applications such as immersive rendering and 3D content gene…
Branch-and-Browse: Efficient and Controllable Web Exploration with Tree-Structured Reasoning and Action Memory
Shiqi He, Yue Cui, Xinyu Ma +3
Autonomous web agents powered by large language models (LLMs) show strong potential for performing goal-oriented tasks such as information retrieval, report generation, and online…
TetriServe: Efficiently Serving Mixed DiT Workloads
Runyu Lu, Shiqi He, Wenxuan Tan +5
Diffusion Transformer (DiT) models excel at generating high-quality images through iterative denoising steps, but serving them under strict Service Level Objectives (SLOs) is chall…
EXP-Bench: Can AI Conduct AI Research Experiments?
Patrick Tser Jern Kon, Jiachen Liu, Xinyi Zhu +10
Automating AI research holds immense potential for accelerating scientific progress, yet current AI agents struggle with the complexities of rigorous, end-to-end experimentation. W…
The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimization
Jae-Won Chung, Jeff J. Ma, Ruofan Wu +5
As the adoption of Generative AI in real-world services grow explosively, energy has emerged as a critical bottleneck resource. However, energy remains a metric that is often overl…
Efficient Distributed MLLM Training with Cornstarch
Insu Jang, Runyu Lu, Nikhil Bansal +2
Multimodal large language models (MLLMs) extend the capabilities of large language models (LLMs) by combining heterogeneous model architectures to handle diverse modalities like im…
Curie: Toward Rigorous and Automated Scientific Experimentation with AI Agents
Patrick Tser Jern Kon, Jiachen Liu, Qiuyi Ding +7
Scientific experimentation, a cornerstone of human progress, demands rigor in reliability, methodical control, and interpretability to yield meaningful results. Despite the growing…
Mordal: Automated Pretrained Model Selection for Vision Language Models
Shiqi He, Insu Jang, Mosharaf Chowdhury
Incorporating multiple modalities into large language models (LLMs) is a powerful way to enhance their understanding of non-textual data, enabling them to perform multimodal tasks.…