5 papers
KVoiceBench, KOpenAudioBench, and KMMAU: Agent-Driven Korean Speech Benchmarks for Evaluating SpeechLMs
Haechan Kim, Seungjun Chung, Inkyu Park +2
Speech language models (SpeechLMs) have achieved substantial progress by extending large language models (LLMs) to the speech modality. However, SpeechLM evaluation remains heavily…
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…
Raon-Speech Technical Report
Beomsoo Kim, Changho Choi, Dohyun Kim +23
We present Raon-Speech, a top-performing 9B-parameter speech language model (SpeechLM) for English and Korean speech understanding, answering, and generation, and Raon-SpeechChat,…
Adversarial Reinforcement Learning Framework for ESP Cheater Simulation
Inkyu Park, Jeong-Gwan Lee, Taehwan Kwon +4
Extra-Sensory Perception (ESP) cheats, which reveal hidden in-game information such as enemy locations, are difficult to detect because their effects are not directly observable in…
Simple Drop-in LoRA Conditioning on Attention Layers Will Improve Your Diffusion Model
Joo Young Choi, Jaesung R. Park, Inkyu Park +3
Current state-of-the-art diffusion models employ U-Net architectures containing convolutional and (qkv) self-attention layers. The U-Net processes images while being conditioned on…