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NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction
NCP Team, Jiaqi Cao, Chiyu Chen +25
We introduce NCP-ArchPreview, a latent-space language model that pushes autoregressive pretraining beyond standard next-token prediction (NTP). Alongside NTP, the model learns thro…
Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory
Rubin Wei, Jiaqi Cao, Jiarui Wang +4
Decoder-only language models entangle long-term memory and reasoning in a single parameter set, making it difficult to scale memory capacity independently. Memory Decoder introduce…
Information-Aware KV Cache Compression for Long Reasoning
Jushi Kai, Zhuiri Xiao, Alexandra Birch +1
Reasoning capability has advanced rapidly in large language models (LLMs), leading to an increasing size of key-value (KV) cache in both prefilling and decoding stages. Existing KV…
Depth-Attention: Cross-Layer Value Mixing for Language Models
Boyi Zeng, Yiqin Hao, Zitong Wang +7
Self-attention selects information freely across the sequence, but across depth, Transformers merely add each layer's output to the residual stream, so later layers cannot selectiv…
FlowLM: Few-Step Language Modeling via Diffusion-to-Flow Adaptation
Runzhe Zhang, Letian Chen, Wenpeng Zhang +2
We present FlowLM, a flow matching language model transformed from pre-trained diffusion language models via efficient fine-tuning. By re-aligning the curved sampling trajectories…
VQKV: High-Fidelity and High-Ratio Cache Compression via Vector-Quantization
Yixuan Wang, Qingyu Shi, Jiayu Zhou +3
The growing context length of Large Language Models (LLMs) enlarges the Key-Value (KV) cache, limiting deployment in resource-limited environments. Prior training-free approaches f…