1 citations · 2 across the 7 of their papers we have counts for
7 papers
Scaling Web Agent Training through Automatic Data Generation and Fine-grained Evaluation
Lajanugen Logeswaran, Jaekyeom Kim, Sungryull Sohn +2
We present a scalable pipeline for automatically generating high-quality training data for web agents. In particular, a major challenge in identifying high-quality training instanc…
Selective LoRA for Visual Tokens and Attention Heads
Tiange Luo, Lajanugen Logeswaran, Jaekyeom Kim +2
Low-rank adaptation (LoRA) is widely used for parameter-efficient fine-tuning, but its standard all-token, all-head design ignores the heterogeneous structure of vision language mo…
Scalable Video-to-Dataset Generation for Cross-Platform Mobile Agents
Yunseok Jang, Yeda Song, Sungryull Sohn +5
Recent advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have sparked significant interest in developing GUI visual agents. We introduce MONDAY (Mobile…
Do Not Trust Licenses You See: Dataset Compliance Requires Massive-Scale AI-Powered Lifecycle Tracing
Jaekyeom Kim, Sungryull Sohn, Gerrard Jeongwon Jo +5
This paper argues that a dataset's legal risk cannot be accurately assessed by its license terms alone; instead, tracking dataset redistribution and its full lifecycle is essential…
Interactive and Expressive Code-Augmented Planning with Large Language Models
Anthony Z. Liu, Xinhe Wang, Jacob Sansom +5
Large Language Models (LLMs) demonstrate strong abilities in common-sense reasoning and interactive decision-making, but often struggle with complex, long-horizon planning tasks. R…
Auto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents
Jaekyeom Kim, Dong-Ki Kim, Lajanugen Logeswaran +2
In this paper, we introduce Auto-Intent, a method to adapt a pre-trained large language model (LLM) as an agent for a target domain without direct fine-tuning, where we empirically…
AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents
Yao Fu, Dong-Ki Kim, Jaekyeom Kim +4
Recent advances in large language models (LLMs) have empowered AI agents capable of performing various sequential decision-making tasks. However, effectively guiding LLMs to perfor…