6 papers
RING: Retrieval-Internalized Generation for Continual Large-Scale Knowledge Injection
Shicheng Xu, Liang Pang, Liyi Chen +7
Retrieval-augmented generation (RAG) improves factuality but adds latency and engineering overhead at serving time. We propose RING (Retrieval-Internalized Generation), a holistic…
Deep Research as Rubric for Reinforcement Learning
Wangyi Mei, Zhouhong Gu, Zhenhan Bai +9
Open-ended reasoning and long-form generation tasks lack reliable automatic verification signals for reward-based policy optimization. Rubrics offer a promising alternative, but ex…
DecEx-RAG: Boosting Agentic Retrieval-Augmented Generation with Decision and Execution Optimization via Process Supervision
Yongqi Leng, Yikun Lei, Xikai Liu +7
Agentic Retrieval-Augmented Generation (Agentic RAG) enhances the processing capability for complex tasks through dynamic retrieval and adaptive workflows. Recent advances (e.g., S…
ZigZagkv: Dynamic KV Cache Compression for Long-context Modeling based on Layer Uncertainty
Meizhi Zhong, Xikai Liu, Chen Zhang +5
Large Language models (LLMs) have become a research hotspot. To accelerate the inference of LLMs, storing computed caches in memory has become the standard technique. However, as t…
Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective
Meizhi Zhong, Chen Zhang, Yikun Lei +5
Enabling LLMs to handle lengthy context is currently a research hotspot. Most LLMs are built upon rotary position embedding (RoPE), a popular position encoding method. Therefore, a…
MoDification: Mixture of Depths Made Easy
Chen Zhang, Meizhi Zhong, Qimeng Wang +8
Long-context efficiency has recently become a trending topic in serving large language models (LLMs). And mixture of depths (MoD) is proposed as a perfect fit to bring down both la…