3 papers
cs.CL2025
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…
cs.CL2025
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…
cs.LG2025
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…