3 papers
cs.AI2026
RaBiT: Residual-Aware Binarization Training for Accurate and Efficient LLMs
Youngcheon You, Banseok Lee, Minseop Choi +5
Efficient deployment of large language models (LLMs) requires extreme quantization, forcing a critical trade-off between low-bit efficiency and performance. Residual binarization e…
cs.LG2026
LittleBit-2: Maximizing the Spectral Energy Gain in Sub-1-Bit LLMs via Latent Geometry Alignment
Banseok Lee, Youngmin Kim
We identify the Spectral Energy Gain in extreme model compression, where low-rank binary approximations outperform tiny-rank floating-point baselines for heavy-tailed spectra. Howe…
cs.LG2026
LittleBit: Ultra Low-Bit Quantization via Latent Factorization
Banseok Lee, Dongkyu Kim, Youngcheon You +1
The deployment of large language models (LLMs) is frequently hindered by prohibitive memory and computational requirements. While quantization mitigates these bottlenecks, maintain…