1 citations · 1 across the 15 of their papers we have counts for
19 papers
Prefixes of Coxeter elements are Coxeter elements of the reflection subgroups they generate
Patrick Wegener
Let be a Coxeter system of finite rank, let be its set of reflections and let be a Coxeter element. For in the interval of the absolute order, l…
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…
LongCat Sparse Attention: Taming the Lightning via Streaming-aware Hierarchical Cross-Layer Indexing
Wen Zan, Jiaqi Zhang, Jianchao Tan +11
DeepSeek Sparse Attention (DSA) enables efficient long-context modeling through its Lightning Indexer. However, practical deployment remains constrained by the indexer's expensive…
MONA: Muon Optimizer with Nesterov Acceleration for Scalable Language Model Training
Jiacheng Li, Jianchao Tan, Hongtao Xu +5
The Muon optimizer has recently offered a promising alternative to AdamW for large language model training, leveraging matrix orthogonalization to produce geometry-aware updates. H…
SparseBalance: Load-Balanced Long Context Training with Dynamic Sparse Attention
Hongtao Xu, Jianchao Tan, Yuxuan Hu +8
While sparse attention mitigates the computational bottleneck of long-context LLM training, its distributed training process exhibits extreme heterogeneity in both \textit{1)} sequ…
AsyncTLS: Efficient Generative LLM Inference with Asynchronous Two-level Sparse Attention
Yuxuan Hu, Jianchao Tan, Jiaqi Zhang +7
Long-context inference in LLMs faces the dual challenges of quadratic attention complexity and prohibitive KV cache memory. While token-level sparse attention offers superior accur…
FG-GDN: Enhancing Long-Context Gated Delta Networks with Doubly Fine-Grained Control
Pingwei Sun, Yuxuan Hu, Jianchao Tan +6
Linear attention mechanisms have emerged as promising alternatives to softmax attention, offering linear-time complexity during inference. Recent advances such as Gated DeltaNet (G…
AFA-LoRA: Enabling Non-Linear Adaptations in LoRA with Activation Function Annealing
Jiacheng Li, Jianchao Tan, Zhidong Yang +4
Low-Rank Adaptation (LoRA) is a widely adopted parameter-efficient fine-tuning (PEFT) method. However, its linear adaptation process limits its expressive power. This means there i…
Accelerate Speculative Decoding with Sparse Computation in Verification
Jikai Wang, Jianchao Tan, Yuxuan Hu +6
Speculative decoding accelerates autoregressive language model inference by verifying multiple draft tokens in parallel. However, the verification stage often becomes the dominant…
Optimizing Native Sparse Attention with Latent Attention and Local Global Alternating Strategies
Yuxuan Hu, Jianchao Tan, Jiaqi Zhang +7
In this work, we conduct a systematic analysis of Native Sparse Attention (NSA) and propose targeted improvements that enhance long-context modeling. A key insight is that alternat…
LongCat-Flash Technical Report
Meituan LongCat Team, Bayan, Bei Li +179
We introduce LongCat-Flash, a 560-billion-parameter Mixture-of-Experts (MoE) language model designed for both computational efficiency and advanced agentic capabilities. Stemming f…
WISCA: A Lightweight Model Transition Method to Improve LLM Training via Weight Scaling
Jiacheng Li, Jianchao Tan, Zhidong Yang +11
Transformer architecture gradually dominates the LLM field. Recent advances in training optimization for Transformer-based large language models (LLMs) primarily focus on architect…
MaskPrune: Mask-based LLM Pruning for Layer-wise Uniform Structures
Jiayu Qin, Jianchao Tan, Kefeng Zhang +2
The remarkable performance of large language models (LLMs) in various language tasks has attracted considerable attention. However, the ever-increasing size of these models present…
C2T: A Classifier-Based Tree Construction Method in Speculative Decoding
Feiye Huo, Jianchao Tan, Kefeng Zhang +2
The growing scale of Large Language Models (LLMs) has exacerbated inference latency and computational costs. Speculative decoding methods, which aim to mitigate these issues, often…
PrefixKV: Adaptive Prefix KV Cache is What Vision Instruction-Following Models Need for Efficient Generation
Ao Wang, Hui Chen, Jiaxin Li +6
Recently, large vision-language models (LVLMs) have rapidly gained popularity for their strong generation and reasoning capabilities given diverse multimodal inputs. However, these…
EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference
Yulei Qian, Fengcun Li, Xiangyang Ji +4
The Mixture-of-Experts (MoE) model has emerged as a prominent architecture in the field of Large Language Models (LLMs), providing a better balance between model performance and co…
CountFormer: Multi-View Crowd Counting Transformer
Hong Mo, Xiong Zhang, Jianchao Tan +4
Multi-view counting (MVC) methods have shown their superiority over single-view counterparts, particularly in situations characterized by heavy occlusion and severe perspective dis…