LLM News

Researchers introduced ms-Mamba, a multi-scale Mamba architecture for time-series forecasting that outperforms recent Transformer and Mamba-based models while using significantly fewer parameters. On the Solar-Energy dataset, ms-Mamba achieved 0.229 mean-squared error versus 0.240 for S-Mamba while using only 3.53M parameters compared to 4.77M.

Researchers introduce ByteFlow Net, a tokenizer-free language model architecture that learns to segment raw byte streams into semantically meaningful units through compression-driven segmentation. The method adapts internal representation granularity per input, outperforming both BPE-based Transformers and previous byte-level approaches in experiments.