4 papers
A.X K2 Technical Report
Cheolseung Baek, Dhammiko Arya, Eunki Kim +40
We introduce A.X K2, a 688B-parameter Mixture-of-Experts (MoE) language model trained from scratch as a high-performance foundation for \emph{agentic} applications. Trained on appr…
Noise-Robust Abstractive Compression in Retrieval-Augmented Language Models
Singon Kim
Abstractive compression utilizes smaller langauge models to condense query-relevant context, reducing computational costs in retrieval-augmented generation (RAG). However, retrieve…
Meta-cognitive Multi-scale Hierarchical Reasoning for Motor Imagery Decoding
Si-Hyun Kim, Heon-Gyu Kwak, Byoung-Hee Kwon +1
Brain-computer interface (BCI) aims to decode motor intent from noninvasive neural signals to enable control of external devices, but practical deployment remains limited by noise…
ACoRN: Noise-Robust Abstractive Compression in Retrieval-Augmented Language Models
Singon Kim, Gunho Jung, Seong-Whan Lee
Abstractive compression utilizes smaller langauge models to condense query-relevant context, reducing computational costs in retrieval-augmented generation (RAG). However,retrieved…