7 papers
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…