10 papers
Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought
Lingzhe Zhang, Tong Jia, Kangjin Wang +8
As modern microservice systems grow increasingly complex due to dynamic interactions and evolving runtime environments, they experience failures with rising frequency. Ensuring sys…
M2F: Automated Formalization of Mathematical Literature at Scale
Zichen Wang, Wanli Ma, Zhenyu Ming +3
Automated formalization of mathematics enables mechanical verification but remains limited to isolated theorems and short snippets. Scaling to textbooks and research papers is larg…
HyLRA: Hybrid Layer Reuse Attention for Efficient Long-Context Inference
Xuan Ai, Qingqing Yang, Peng Wang +4
Long-context inference in Large Language Models (LLMs) is bottlenecked by the quadratic computation complexity of attention and the substantial memory footprint of Key-Value (KV) c…
Uni-FinLLM: A Unified Multimodal Large Language Model with Modular Task Heads for Micro-Level Stock Prediction and Macro-Level Systemic Risk Assessment
Gongao Zhang, Haijiang Zeng, Lu Jiang
Financial institutions and regulators require systems that integrate heterogeneous data to assess risks from stock fluctuations to systemic vulnerabilities. Existing approaches oft…
A Mathematical Theory of Top- Sparse Attention via Total Variation Distance
Georgios Tzachristas, Lei Deng, Ioannis Tzachristas +2
We develop a unified mathematical framework for certified Top- attention truncation that quantifies approximation error at both the distribution and output levels. For a single…
AtlasKV: Augmenting LLMs with Billion-Scale Knowledge Graphs in 20GB VRAM
Haoyu Huang, Hong Ting Tsang, Jiaxin Bai +3
Retrieval-augmented generation (RAG) has shown some success in augmenting large language models (LLMs) with external knowledge. However, as a non-parametric knowledge integration p…
HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference
Ping Gong, Jiawei Yi, Shengnan Wang +13
Large Language Models (LLMs) have emerged as a pivotal research area, yet the attention module remains a critical bottleneck in LLM inference, even with techniques like KVCache to…
CentroidKV: Efficient Long-Context LLM Inference via KV Cache Clustering
Jie Hu, Shengnan Wang, Yutong He +8
Large language models (LLMs) with extended context windows have become increasingly prevalent for tackling complex tasks. However, the substantial Key-Value (KV) cache required for…
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora
Jiaxin Bai, Wei Fan, Qi Hu +17
We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. Our system leverages large language models t…
XL3M: A Training-free Framework for LLM Length Extension Based on Segment-wise Inference
Shengnan Wang, Youhui Bai, Lin Zhang +7
Length generalization failure problem, namely the large language model (LLM) fails to generalize to texts longer than its maximum training length, greatly restricts the application…