11 papers
PerMix-RLVR: Preserving Persona Expressivity under Verifiable-Reward Alignment
Jihwan Oh, Soowon Oh, Murad Aghazada +3
Persona prompting has been widely adopted to steer large language models (LLMs) behavior and improve their instruction performance by assigning specific characters. However, identi…
Scalable Frameworks for Real-World Audio-Visual Speech Recognition
Sungnyun Kim
The practical deployment of Audio-Visual Speech Recognition (AVSR) systems is fundamentally challenged by significant performance degradation in real-world environments, characteri…
Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation
Sangmin Bae, Yujin Kim, Reza Bayat +8
Scaling language models unlocks impressive capabilities, but the accompanying computational and memory demands make both training and deployment expensive. Existing efficiency effo…
Flex-Judge: Text-Only Reasoning Unleashes Zero-Shot Multimodal Evaluators
Jongwoo Ko, Sungnyun Kim, Sungwoo Cho +1
Human-generated reward signals are critical for aligning generative models with human preferences, guiding both training and inference-time evaluations. While large language models…
Two Heads Are Better Than One: Audio-Visual Speech Error Correction with Dual Hypotheses
Sungnyun Kim, Kangwook Jang, Sungwoo Cho +3
This paper introduces a new paradigm for generative error correction (GER) framework in audio-visual speech recognition (AVSR) that reasons over modality-specific evidences directl…
DiffBlender: Composable and Versatile Multimodal Text-to-Image Diffusion Models
Sungnyun Kim, Junsoo Lee, Kibeom Hong +2
In this study, we aim to enhance the capabilities of diffusion-based text-to-image (T2I) generation models by integrating diverse modalities beyond textual descriptions within a un…