11 papers
DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression
DeepSeek-AI, :, Anyi Xu +585
The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation…
How Does mHC Use Its Residual Streams? Selective Routing and Near-Identity Mixing
Pengxiang Zhao, Xing Li, Xianzhi Yu +2
Hyper-Connections and their manifold-constrained variant mHC widen a residual pathway from one stream to n, yet how trained models use this capacity remains unclear: how broadly bl…
AdaFlash: Adaptive Speculative Decoding via On-Policy Distilled Diffusion Drafters
Yu-Yang Qian, Hao-Cong Wu, Chen Chen +4
Speculative decoding, in which a lightweight draft model first generates a draft sequence that is then verified by the target model, has become a prevalent paradigm for acceleratin…
QuantClaw: Precision Where It Matters for OpenClaw
Manyi Zhang, Ji-Fu Li, Zhongao Sun +5
Autonomous agent systems such as OpenClaw introduce significant efficiency challenges due to long-context inputs and multi-turn reasoning. This results in prohibitively high comput…
HAWK: Head Importance-Aware Visual Token Pruning in Multimodal Models
Qihui Zhu, Tao Zhang, Yuchen Wang +9
In multimodal large language models (MLLMs), the surge of visual tokens significantly increases the inference time and computational overhead, making them impractical for real-time…
BATQuant: Outlier-resilient MXFP4 Quantization via Learnable Block-wise Optimization
Ji-Fu Li, Manyi Zhang, Xiaobo Xia +4
Microscaling floating-point (MXFP) formats have emerged as a promising standard for deploying Multi-modal Large Language Models (MLLMs) and Large Language Models (LLMs) on modern a…
What Matters For Safety Alignment?
Xing Li, Hui-Ling Zhen, Lihao Yin +3
This paper presents a comprehensive empirical study on the safety alignment capabilities. We evaluate what matters for safety alignment in LLMs and LRMs to provide essential insigh…
Benchmarking Post-Training Quantization of Large Language Models under Microscaling Floating Point Formats
Manyi Zhang, Ji-Fu Li, Zhongao Sun +4
Microscaling Floating-Point (MXFP) has emerged as a promising low-precision format for large language models (LLMs). Despite various post-training quantization (PTQ) algorithms bei…
SwiftMem: Fast Agentic Memory via Query-aware Indexing
Anxin Tian, Yiming Li, Xing Li +5
Agentic memory systems have become critical for enabling LLM agents to maintain long-term context and retrieve relevant information efficiently. However, existing memory frameworks…
Towards Efficient Agents: A Co-Design of Inference Architecture and System
Weizhe Lin, Hui-Ling Zhen, Shuai Yang +14
The rapid development of large language model (LLM)-based agents has unlocked new possibilities for autonomous multi-turn reasoning and tool-augmented decision-making. However, the…
PreMoE: Proactive Inference for Efficient Mixture-of-Experts
Zehua Pei, Ying Zhang, Hui-Ling Zhen +6
Mixture-of-Experts (MoE) models offer dynamic computation, but are typically deployed as static full-capacity models, missing opportunities for deployment-specific specialization.…