9 papers
A2Z GameSpec-Bench: How Faithfully Can Coding Agents Generate Games from Game Design Specifications?
Seonho Lee, Wonryeol Jeong, Alberto Cereser +4
Delegating complete application development to coding agents requires preserving the intended design rather than simply producing plausible outputs through naive prompting. Game de…
Raon-OpenTTS: Open Models and Data for Robust Text-to-Speech
Semin Kim, Seungjun Chung, Taehong Moon +8
Recent advances in text-to-speech (TTS) models show impressive speech naturalness and quality, yet the role of large-scale open data in driving this progress remains underexplored.…
MMTB: Evaluating Terminal Agents on Multimedia-File Tasks
Chiyeong Heo, Jaechang Kim, Junhyuk Kwon +4
Terminals provide a powerful interface for AI agents by exposing diverse tools for automating complex workflows, yet existing terminal-agent benchmarks largely focus on tasks groun…
See and Fix the Flaws: Enabling VLMs and Diffusion Models to Comprehend Visual Artifacts via Agentic Data Synthesis
Jaehyun Park, Minyoung Ahn, Minkyu Kim +3
Despite recent advances in diffusion models, AI generated images still often contain visual artifacts that compromise realism. Although more thorough pre-training and bigger models…
VLM-SubtleBench: How Far Are VLMs from Human-Level Subtle Comparative Reasoning?
Minkyu Kim, Sangheon Lee, Dongmin Park
The ability to distinguish subtle differences between visually similar images is essential for diverse domains such as industrial anomaly detection, medical imaging, and aerial sur…
FlashAdventure: A Benchmark for GUI Agents Solving Full Story Arcs in Diverse Adventure Games
Jaewoo Ahn, Junseo Kim, Heeseung Yun +4
GUI agents powered by LLMs show promise in interacting with diverse digital environments. Among these, video games offer a valuable testbed due to their varied interfaces, with adv…
Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games
Dongmin Park, Minkyu Kim, Beongjun Choi +13
Large Language Model (LLM) agents are reshaping the game industry, by enabling more intelligent and human-preferable characters. Yet, current game benchmarks fall short of practica…
Test-time Alignment of Diffusion Models without Reward Over-optimization
Sunwoo Kim, Minkyu Kim, Dongmin Park
Diffusion models excel in generative tasks, but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often s…
Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM Guidance
Dongmin Park, Sebin Kim, Taehong Moon +3
State-of-the-art text-to-image (T2I) diffusion models often struggle to generate rare compositions of concepts, e.g., objects with unusual attributes. In this paper, we show that t…