4 citations · 4 across the 5 of their papers we have counts for
6 papers
QuantMLA: Function-Aligned Dual-Path Quantization for Low-Bit MLA KV Caching
Zunhai Su, Yuxuan Sun, Jianchao Tan +5
Multi-Head Latent Attention (MLA) enables expressive multi-head attention with compact caches for its content and decoupled RoPE paths, yet cache memory still scales linearly with…
DASC: Decay-Aware State Compression for Hybrid Linear-Attention Serving
Yanqi Yu, Pingwei Sun, Jianchao Tan +4
Hybrid linear-attention architectures have recently scaled to large open-weight models, offering quality competitive with full attention while substantially reducing key/value (KV)…
DAMP: Decay-Aware Mixed-Precision Recurrent-State Quantization
Tao Zhang, Jianchao Tan, Pingwei Sun +6
Complex reasoning and agentic applications increasingly rely on long-context inference, where growing KV caches increase both memory usage and decoding overhead. Hybrid models redu…
OnceSelect: Reusable Data Selection for Efficient Multimodal Instruction Tuning
Mingkang Dong, Hongyi Cai, Xiwen Lei +8
Multimodal instruction tuning is widely used to adapt multimodal large language models (MLLMs), yet the large-scale image-text datasets it relies on are often highly redundant. Exi…
MixKVQ: Query-Aware Mixed-Precision KV Cache Quantization for Long-Context Reasoning
Tao Zhang, Ziqian Zeng, Hao Peng +2
Long Chain-of-Thought (CoT) reasoning has significantly advanced the capabilities of Large Language Models (LLMs), but this progress is accompanied by substantial memory and latenc…
GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models
Tao Zhang, Ziqian Zeng, Yuxiang Xiao +4
Large Language Models (LLMs) are prone to generating content that exhibits gender biases, raising significant ethical concerns. Alignment, the process of fine-tuning LLMs to better…