2 citations · 3 across the 12 of their papers we have counts for
15 papers
Accelerating VLA Inference with Perturbation Informed Execution
Jehun Lee, Hajun Lee, Jimin Ryu +4
Action-chunking vision-language-action (VLA) models predict sequences of future robot actions. Under fixed-horizon execution, the robot executes a fixedlength prefix of each predic…
ParaPairAudioBench: Paralinguistic Pairwise Audio Benchmark for LALM-as-a-Judge
Jisu Jeon, Seungyeon Jwa, Joosung Lee +6
Large Audio-Language Models (LALMs) have been widely used as judge models for the automatic evaluation of generated speech. However, prior approaches predominantly focus on holisti…
RTSGameBench: An RTS Benchmark for Strategic Reasoning by Vision-Language Models
San Kim, Daechul Ahn, Reokyoung Kim +3
Modern Vision-Language Models (VLMs) often struggle with strategic reasoning, i.e., anticipating and influencing other agents' actions, under uncertainty in competitive and coopera…
SCALE: Self-uncertainty Conditioned Adaptive Looking and Execution for Vision-Language-Action Models
Hyeonbeom Choi, Daechul Ahn, Youhan Lee +3
Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robotic control, with test-time scaling (TTS) gaining attention to enhance robustness b…
Becoming Experienced Judges: Selective Test-Time Learning for Evaluators
Seungyeon Jwa, Daechul Ahn, Reokyoung Kim +2
Automatic evaluation with large language models, commonly known as LLM-as-a-judge, is now standard across reasoning and alignment tasks. Despite evaluating many samples in deployme…
What Happens When: Learning Temporal Orders of Events in Videos
Daechul Ahn, Yura Choi, Hyeonbeom Choi +3
Video Large Multimodal Models (VLMMs) have shown impressive performance in video understanding, yet their ability to accurately capture the temporal order of multiple events remain…
BINDER: Instantly Adaptive Mobile Manipulation with Open-Vocabulary Commands
Seongwon Cho, Daechul Ahn, Donghyun Shin +3
Open-vocabulary mobile manipulation (OVMM) requires robots to follow language instructions, navigate, and manipulate while updating their world representation under dynamic environ…
DialNav: Multi-turn Dialog Navigation with a Remote Guide
Leekyeung Han, Hyunji Min, Gyeom Hwangbo +2
We introduce DialNav, a novel collaborative embodied dialog task, where a navigation agent (Navigator) and a remote guide (Guide) engage in multi-turn dialog to reach a goal locati…
Society of Mind Meets Real-Time Strategy: A Hierarchical Multi-Agent Framework for Strategic Reasoning
Daechul Ahn, San Kim, Jonghyun Choi
Large Language Models (LLMs) have recently demonstrated impressive action sequence prediction capabilities but often struggle with dynamic, long-horizon tasks such as real-time str…
NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance
Hanwool Lee, Sara Yu, Yewon Hwang +4
Financial text embeddings must distinguish changes in event status, perspective, and obligations even when passages share similar wording. NMIXX adapts existing encoders through 18…
ISAC: Training-Free Instance-to-Semantic Attention Control for Multi-Instance Generation
Sanghyun Jo, Wooyeol Lee, Ziseok Lee +3
Recent open-weight text-to-image (T2I) diffusion models still struggle with multi-instance prompts, often omitting or merging instances and mixing semantics among similar objects.…
Representation Bending for Large Language Model Safety
Ashkan Yousefpour, Taeheon Kim, Ryan S. Kwon +7
Large Language Models (LLMs) have emerged as powerful tools, but their inherent safety risks - ranging from harmful content generation to broader societal harms - pose significant…
SyncVSR: Data-Efficient Visual Speech Recognition with End-to-End Crossmodal Audio Token Synchronization
Young Jin Ahn, Jungwoo Park, Sangha Park +2
Visual Speech Recognition (VSR) stands at the intersection of computer vision and speech recognition, aiming to interpret spoken content from visual cues. A prominent challenge in…
ISR-DPO: Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective DPO
Daechul Ahn, Yura Choi, San Kim +3
Iterative self-improvement, a concept extending beyond personal growth, has found powerful applications in machine learning, particularly in transforming weak models into strong on…
Tuning Large Multimodal Models for Videos using Reinforcement Learning from AI Feedback
Daechul Ahn, Yura Choi, Youngjae Yu +2
Recent advancements in large language models have influenced the development of video large multimodal models (VLMMs). The previous approaches for VLMMs involved Supervised Fine-Tu…