activity
20242026
most citedVideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SE2026

Super Library Agent: Joint Generation and Maintenance of Multiple Applications Beyond the Single Codebase

Daegyu Sung, Yukyeong Lee, Geon Park +2

Organizations often develop and maintain portfolios of related applications: independently deployable codebases that share substantial domain logic, interface patterns, or operatio…

cs.CV2025

WorldGen: From Text to Traversable and Interactive 3D Worlds

Dilin Wang, Hyunyoung Jung, Tom Monnier +22

We introduce WorldGen, a system that enables the automatic creation of large-scale, interactive 3D worlds directly from text prompts. Our approach transforms natural language descr…

cs.CV2025

RaDL: Relation-aware Disentangled Learning for Multi-Instance Text-to-Image Generation

Geon Park, Seon Bin Kim, Gunho Jung +1

With recent advancements in text-to-image (T2I) models, effectively generating multiple instances within a single image prompt has become a crucial challenge. Existing methods, whi…

cs.CL2025

InfiniteHiP: Extending Language Model Context Up to 3 Million Tokens on a Single GPU

Heejun Lee, Geon Park, Jaduk Suh +1

In modern large language models (LLMs), handling very long context lengths presents significant challenges as it causes slower inference speeds and increased memory costs. Addition…

cs.CV2024★ 1 cited

VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding

Kangsan Kim, Geon Park, Youngwan Lee +2

Recent advancements in video large multimodal models (LMMs) have significantly improved their video understanding and reasoning capabilities. However, their performance drops on ou…

cs.CL2024

A Training-free Sub-quadratic Cost Transformer Model Serving Framework With Hierarchically Pruned Attention

Heejun Lee, Geon Park, Youngwan Lee +7

In modern large language models (LLMs), increasing the context length is crucial for improving comprehension and coherence in long-context, multi-modal, and retrieval-augmented lan…