Jin Young Kim — accepted AI-conference papers

2026

BiCycle: Group-wise Recursive Transformer Based on ASR Mechanism

Min Ho Jang, Eun Seo Seo, Jin Young Kim, Hyeongsoo Lim, Ji Won Yoon

AAAI 2026technical

Recursive transformer (RT) is a promising parameter-sharing technique for reducing computational burden of large-scale model. While RT has been successfully applied to large language models (LLMs), its effectiveness in automatic speech recognition (ASR) remains limited, despite the parallel trend of

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2026

GrayKD: Distilling Better Knowledge from Black-box LLM via Multi-rationale Injection

Hyeongsoo Lim, Hyung Yong Kim, Jin Young Kim, Min Ho Jang, Eun Seo Seo, Youshin Lim +6

AAAI 2026technical

Knowledge distillation (KD) is a promising compression technique for reducing the computational burden of large language models (LLMs). Depending on access to the teacher model’s internal parameters, KD is typically categorized into white-box and black-box KD. While white-box KD benefits from full a

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