From the 1 of 22 linked papers with an AI index.
1 citations · 1 across the 16 of their papers we have counts for
14 papers · 1 filter
Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory
Rubin Wei, Jiaqi Cao, Jiarui Wang +4
The paper presents Memory Decoder at Scale, a pretrained parametric long‑term memory module for decoder‑only language models that is scaled up to 6.9 B parameters and shown to impr…
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…
AdaPonderLM: Gated Pondering Language Models with Token-Wise Adaptive Depth
Shixiang Song, He Li, Zitong Wang +6
Test-time scaling via recurrent/iterative Transformers enables large language models to spend more computation at inference, but most pretrained recurrent LMs run a fixed number of…