2 citations · 5 across the 67 of their papers we have counts for
35 papers · 1 filter
An Agentic Framework for Proactive Robots with Procedural Assistance Memory
Dohyun Lee, Minho Park, Jeonghoon Park +5
Proactive robots should anticipate user needs and provide assistance before being explicitly asked. A straightforward approach is to infer human intentions from commonsense knowled…
HiPLEX: Hierarchical Policy Factorization for Full Duplex Speech Language Models
Kyudan Jung, Hyunsin Park, Yoonhyung Lee +5
As human--AI interactions become more conversational, full-duplex speech language models capable of natural real-time dialogue are growing in importance. Beyond generating appropri…
Hiding Tool Latency in On-Device Cascaded Voice Agent through Speculative Execution
Kyudan Jung, Hyunsin Park, Yoonhyung Lee +5
Tool-augmented speech assistants typically serialize automatic speech recognition, large language model inference, and external tool execution. As a result, tool latency is incurre…
FlashDexRetarget: Accelerating Dexterous Manipulation Data Generation through Multi-Motion Retargeting
Kyungmin Lee, Sibeen Kim, Dongyoon Hwang +6
Human hand-object demonstrations provide a scalable source of data for dexterous robot learning, but transferring them across embodiments requires physically feasible retargeting.…
RECAST: Recasting Vision-Language Semantics into an Actionable Cost Map for Robot Navigation
Incheol Cho, Jintae Park, Jinkyu Kim +3
Safe and robust robot navigation across diverse environments requires a high-level understanding of complex scenes and the ability to carry it into stable motion. Recent works tack…
PIA: A Personal Intelligence Agent Turning Health Conversations into Records and Records into Understanding
Jeonghun Yoon, Dongchan Kim, Hongyeon Yu +2
General-purpose agent memory summarizes conversations: it extracts salient snippets, embeds them, and retrieves the top-k into the prompt. A health agent cannot run on summaries: a…
Tracing and Relearning Detection Evidence in Text-to-Speech Systems
Eunji Shin, Kyudan Jung, Jihwan Kim +2
Recent audio deepfake detectors separate bona fide speech from synthetic speech, yet it remains unclear which stage of a text-to-speech system supplies the detection evidence. We a…
Joint Analysis of Latent Dimensionality and Frame Rate in Continuous Audio Encoders
Kyudan Jung, Sehyun Lee, Song-ha Jo +4
Continuous audio encoders compress audio along feature and time axes through latent width and frame rate, but their joint effect on downstream performance remains unclear. We train…
A Dual-Expert Strategy Integrating LLMs to Mitigate Negative Transfer in Cross-Domain Sequential Recommendation
Hyeongjun Yun, Kihyuk Song, Jaegul Choo +1
Cross-Domain Sequential Recommendation (CDSR) predicts the next item a user will interact with based on their historical interaction sequences across multiple domains. Recent appro…
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control
Donghu Kim, Youngdo Lee, Hojoon Lee +6
Improving sample efficiency remains a core challenge in reinforcement learning (RL), especially in real-world settings like robotics, where data collection is costly. This challeng…
PersonaTrail: Benchmarking Personalized Web Agents through Browsing Trails
Seungbin Yang, Chaewoon Ki, Dohyun Lee +2
Recent advances in large language models have enabled web agents to autonomously execute complex tasks. In practice, users frequently provide underspecified instructions, requiring…
EditPPT: Structured Execution and Evaluation for Reliable Slide Editing
Jiheon Kim, Kyudan Jung, Jaegul Choo
Reliable slide editing requires precise modifications to intended objects while preserving the rest of the presentation. We argue that slide editing is a problem of structured, con…
ReDesign: Recovering Editable Design Structures from Images via Agentic Decomposition
Jooyeol Yun, Jintae Park, Hyesu Lim +3
Recovering an editable design file from a raster image is a common and costly bottleneck in modern design workflows, yet remains challenging since editability depends on recovering…
Accelerating Masked Diffusion Large Language Models: A Survey of Efficient Inference Techniques
Daehoon Gwak, Minhyung Lee, Junwoo Park +1
Diffusion large language models (dLLMs) offer a theoretical advantage in parallel generation over standard autoregressive models. However, parallel generation alone does not guaran…
See like a Robot: Robot-Centric Pointmaps for VLA Models
Byungkun Lee, Dongyoon Hwang, Dongjin Kim +4
Vision-language-action (VLA) models require 3D spatial reasoning, yet RGB observations encode robot-object geometry only implicitly. Lifting depth with camera intrinsics makes this…
3D HAMSTER: Bridging Planning and Control in Hierarchical Vision Language Action Models through 3D Trajectory Guidance
Dongyoon Hwang, Byungkun Lee, Dongjin Kim +7
Hierarchical Vision-Language-Action (VLA) models decouple high-level planning from low-level control to improve generalization in robot manipulation. Recent work in this paradigm u…
Object-Centric Residual RL for Zero-Shot Sim-to-Real VLA Enhancement
Kinam Kim, Namiko Saito, Heecheol Kim +3
Vision-Language-Action (VLA) models can generalize across diverse manipulation tasks, but their imitation-learning-based policies remain brittle in precise physical interactions du…
Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning
Youngwoo Cho, Seunghoon Yi, Wooil Yang +6
Pre-trained materials foundation models, or machine learning interatomic potentials, leverage general physicochemical knowledge to effectively approximate potential energy surfaces…
Membrane: A Self-Evolving Contrastive Safety Memory for LLM Agent Defense
Minseok Choi, Seungbin Yang, Dongjin Kim +5
Despite advances in safety alignment, large language models remain vulnerable to continuously evolving jailbreaks. Existing fine-tuned safety classifiers cannot adapt to these evol…
Probability-Conserving Flow Guidance
Parsa Esmati, Junha Hyung, Amirhossein Dadashzadeh +2
Diffusion and flow-based generative models dominate visual synthesis, with guidance aligning samples to user input and improving perceptual quality. However, Classifier-Free Guidan…
Don't Let Bandit Feedback Pull Continual LLM-Recommender Updates Off Target
Taesan Kim, Hyeongjun Yun, Jaegul Choo +1
Generative LLM-based recommenders (LLM-Rec) require continual post-deployment updates, yet deployment logs provide only policy-shaped contextual bandit feedback: outcomes are obser…
FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control
Donghu Kim, Youngdo Lee, Minho Park +10
Reinforcement learning (RL) is a core approach for robot control when expert demonstrations are unavailable. On-policy methods such as Proximal Policy Optimization (PPO) are widely…
Gaze Attention: Query-Adaptive Visual Routing for Efficient Multimodal LLMs
Junha Song, Byeongho Heo, Geonmo Gu +3
When humans describe a visual scene, they do not process the entire image uniformly; instead, they selectively fixate on regions relevant to their intended description. In contrast…
AHS: Adaptive Head Synthesis via Synthetic Data Augmentations
Taewoong Kang, Hyojin Jang, Sohyun Jeong +4
Recent digital media advancements have created increasing demands for sophisticated portrait manipulation techniques, particularly head swapping, where one's head is seamlessly int…