activity
20242026
most citedHyperCLOVA X Technical Report

7 citations · 7 across the 5 of their papers we have counts for

collaborators

7 papers

cs.RO2026

Locomotion-Grounded Humanoid Soccer: Task-Gated Reinforcement Learning of a Multi-Directional Kicking Library

Abu Hanif Muhammad Syarubany, Jaehyun Jang, Hwanhee Kim +3

Recent humanoid soccer systems make motion tracking the substrate and derive locomotion from it, typically by steering a motion-reference anchor toward the ball. This yields strong…

cs.CV2026

Event-Driven Refresh and Recurrence Memory to Reduce Stale Grounding in Referring Video Object Segmentation

Abu Hanif Muhammad Syarubany, Jaehyun Jang, Siwoo Lim +2

Referring Video Object Segmentation (RVOS) aims to produce a pixel-accurate mask sequence for an object specified by natural language. Sa2VA combines a multimodal large language mo…

cs.RO2026

Action- and Language-Conditioned Video Assessment for Embodied Control

Hwanhee Kim, Jaehyun Jang, Seungmin Cha +3

Vision-based embodied agents executing multi-step natural language instructions require feedback mechanisms that assess task progress over complete trajectories. Conventional appro…

cs.CV2026

Token-level Response-visual Attention Guidance for Multimodal LLMs Knowledge Distillation

Jaehyun Jang, Eunseop Yoon, Hee Suk Yoon +3

While knowledge distillation (KD) is widely adopted for training lightweight models by leveraging supervision from larger teacher models, relying solely on output token distributio…

cs.CV2026

Decomposed On-Policy Distillation for Vision-Language Reasoning: Steering Gradients for Visual Grounding

Hee Suk Yoon, Eunseop Yoon, Jaehyun Jang +6

While on-policy distillation offers dense supervision for training small reasoning models, its optimization dynamics in the multimodal domain remain under-explored. In this work, w…

cs.AI2025

PACR: Progressively Ascending Confidence Reward for LLM Reasoning

Eunseop Yoon, Hee Suk Yoon, Jaehyun Jang +5

Reinforcement Learning with Verifiable Rewards (RLVR) has significantly improved LLM reasoning, but its sparse, outcome-based reward provides no guidance for intermediate steps, sl…

cs.CL2024★ 7 cited

HyperCLOVA X Technical Report

Kang Min Yoo, Jaegeun Han, Sookyo In +393

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. H…