613 citations · 1.2k across the 67 of their papers we have counts for
74 papers
HumanSkill-4D: Training-Free Language Querying of Animatable Gaussian Avatars in Space and Time
Junsu Kim, unsu Kim, Naeun Kim +2
We introduce avatar referring segmentation, which selects regions of animatable Gaussian avatars from language queries. A query can also specify a pose or motion to select matching…
Speculative Forcing for Efficient Autoregressive Video Generation
Hyunwook Choi, Seungryong Kim, Dongyoon Han +1
Few-step autoregressive video generators enable streaming generation, yet they run every token through the final denoising step, regardless of how much refinement it needs. We find…
Omni-Modal LLMs Know More Than They Say
Na Min An, Taekyung Kim, Jaemyung Yu +2
Omni-modal large language models (Omni-LLMs) extend LLMs to process heterogeneous modalities (e.g., vision, audio, and IMU signals) alongside language, enabling a broader range of…
Learning Transferable World-Action Models from Task-Paired Human Videos
Jeongeun Park, Taekyung Kim, Dongyoon Han +1
World-action models inherit broad visual and physical priors from video pre-training. However, standard within-video next-frame prediction allows them to rely on appearance shared…
Isotropic Embedding Perturbations for Robust Vision Language Encoders
Hyesong Choi, Daeun Kim, Song Park +5
Data augmentation is fundamental to training modern deep vision and multimodal models. While individual methods, such as RandAug, CutMix, Mixup, RandErase, and DropPath, offer stro…
Router Prior Bias: Preserving Base Routing Structure in MoE Post-Training
Jaedeok Lee, Keonwoo Kim, Dongyoon Han +3
Mixture-of-Experts (MoE) pretraining relies on an auxiliary load-balancing loss (LBL) to drive per-expert utilization toward uniformity. Post-training inherits a different situatio…
Revisiting Complete Reasoning Traces for Post-Training
Jaehui Hwang, Sangdoo Yun, Byeongho Heo +1
Large language models (LLMs) are often post-trained on pre-collected reasoning trajectories to improve their reasoning capability. Such trajectories tend to be long due to complex,…
Beneath the Surface of Chains-of-Thought: A Mechanistic Interpretation of Reasoning Operations in LLMs
Seogyeong Jeong, Jaehui Hwang, Dongyoon Han +3
Reasoning in large language models unfolds through diverse functional operations, such as problem formulation, goal decomposition, and deduction. Although these operations are expl…
Verification-Aware Training for Speculative Decoding
Geonmo Gu, Byeongho Heo, HeeJae Jun +4
Speculative decoding accelerates large language model inference by using a draft model to generate candidate tokens, which are verified by the target model in a single forward pass…
On-Policy Delta Distillation for Multilingual Math Reasoning
Byeongho Heo, Jaehui Hwang, Sangdoo Yun +1
On-Policy Distillation (OPD) is emerging as a promising alternative to reinforcement learning for LLM post-training, yet its effectiveness in multilingual settings remains underexp…
Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs
Seunghyun Lee, Dongyoon Han, Sangdoo Yun
Safety interventions on dual-use knowledge typically choose between destroying hazardous content (e.g., unlearning, filtering) and suppressing it at the output layer (e.g., refusal…
On-Policy Delta Distillation
Byeongho Heo, Jaehui Hwang, Sangdoo Yun +1
On-policy distillation is an alternative post-training method in reinforcement learning that alleviates the constraints imposed by reward models by providing token-level supervisio…
MuCo: Multi-turn Contrastive Learning for Multimodal Embedding Model
Geonmo Gu, Byeongho Heo, Jaemyung Yu +7
Universal Multimodal embedding models built on Multimodal Large Language Models (MLLMs) have traditionally employed contrastive learning, which aligns representations of query-targ…
Which Concepts to Forget and How to Refuse? Decomposing Concepts for Continual Unlearning in Large Vision-Language Models
Hyundong Jin, Dongyoon Han, Eunwoo Kim
Continual unlearning poses the challenge of enabling large vision-language models to selectively refuse specific image-instruction pairs in response to sequential deletion requests…
Grounding World Simulation Models in a Real-World Metropolis
Junyoung Seo, Hyunwook Choi, Minkyung Kwon +10
What if a world simulation model could render not an imagined environment but a city that actually exists? Prior generative world models synthesize visually plausible yet artificia…
VisualScratchpad: Inference-time Visual Concepts Analysis in Vision Language Models
Hyesu Lim, Jinho Choi, Taekyung Kim +3
High-performing vision language models still produce incorrect answers, yet their failure modes are often difficult to explain. To make model internals more accessible and enable s…
On the Reliability of Cue Conflict and Beyond
Pum Jun Kim, Seung-Ah Lee, Seongho Park +2
Understanding how neural networks rely on visual cues offers a human-interpretable view of their internal decision processes. The cue-conflict benchmark has been influential in pro…
Fast KVzip: Efficient and Accurate LLM Inference with Gated KV Eviction
Jang-Hyun Kim, Dongyoon Han, Sangdoo Yun
Efficient key-value (KV) cache management is crucial for the practical deployment of large language models (LLMs), yet existing compression techniques often incur a trade-off betwe…
Oops, Wait: Discourse Tokens Matter in Reasoning Model
Jaehui Hwang, Byeongho Heo, Sangdoo Yun +1
Recent studies suggest that even data-efficient training with (1K) reasoning trajectories can induce non-trivial reasoning capabilities in large language models through pos…
What Defines Good Reasoning in LLMs? Dissecting Reasoning Steps with Multi-Aspect Evaluation
Heejin Do, Jaehui Hwang, Dongyoon Han +2
Evaluating large language models (LLMs) on final-answer correctness is the dominant paradigm. This approach, however, provides a coarse signal for model improvement and overlooks t…
RL makes MLLMs see better than SFT
Junha Song, Sangdoo Yun, Dongyoon Han +2
A dominant assumption in Multimodal Language Model (MLLM) research is that its performance is largely inherited from the LLM backbone, given its immense parameter scale and remarka…
Exploring Conditions for Diffusion models in Robotic Control
Heeseong Shin, Byeongho Heo, Dongyoon Han +2
While pre-trained visual representations have significantly advanced imitation learning, they are often task-agnostic as they remain frozen during policy learning. In this work, we…
Revisiting Reliability in the Reasoning-based Pose Estimation Benchmark
Junsu Kim, Naeun Kim, Jaeho Lee +3
The reasoning-based pose estimation (RPE) benchmark has emerged as a widely adopted evaluation standard for pose-aware multimodal large language models (MLLMs). Despite its signifi…
Token Bottleneck: One Token to Remember Dynamics
Taekyung Kim, Dongyoon Han, Byeongho Heo +2
Deriving compact and temporally aware visual representations from dynamic scenes is essential for successful execution of sequential scene understanding tasks such as visual tracki…