2 citations · 7 across the 11 of their papers we have counts for
12 papers
ReasonAlloc: Hierarchical Decoding-Time KV Cache Budget Allocation for Reasoning Models
Wenhao Liu, Hao Shi, Yunhe Li +7
Long chain-of-thought (CoT) trajectories in large language model (LLM) reasoning cause severe inference bottlenecks due to rapid key-value (KV) cache growth. Current decoding-time…
Neural Scalable Symbolic Search Framework for Complex Logical Queries with Multiple Free Variables
Weizhi Fei, Hang Yin, Zihao Wang +3
Complex Query Answering (CQA) is a fundamental knowledge representation and reasoning task over incomplete knowledge graphs (KGs). Answering existential first-order queries with $k…
Toward Native Multimodal Modeling: A Roadmap
Siyu An, Junru Lu, Junnan Dong +18
Multimodal modeling represents a vital step from modality-agnostic reasoning toward world modeling. While early approaches predominantly rely on late-fusion that assembles encoders…
NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database
Weizhi Fei, Hao Shi, Jing Xu +7
Efficiently editing knowledge stored in large language models (LLMs) enables model updates without large-scale training. One possible solution is Locate-and-Edit (L\&E), allowing s…
Efficient and Scalable Neural Symbolic Search for Knowledge Graph Complex Query Answering
Weizhi Fei, Zihao Wang, hang Yin +3
Complex Query Answering (CQA) is a crucial reasoning task over Knowledge Graphs (KGs), which aims to answer first-order logical queries from incomplete KGs. While existing neural-s…
Efficient Prompt Compression with Evaluator Heads for Long-Context Transformer Inference
Weizhi Fei, Xueyan Niu, Guoqing Xie +3
Although applications involving long-context inputs are crucial for the effective utilization of large language models (LLMs), they also result in increased computational costs and…
Top Ten Challenges Towards Agentic Neural Graph Databases
Jiaxin Bai, Zihao Wang, Yukun Zhou +16
Graph databases (GDBs) like Neo4j and TigerGraph excel at handling interconnected data but lack advanced inference capabilities. Neural Graph Databases (NGDBs) address this by inte…
Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding
Weizhi Fei, Xueyan Niu, Guoqing Xie +4
Current Large Language Models (LLMs) face inherent limitations due to their pre-defined context lengths, which impede their capacity for multi-hop reasoning within extensive textua…
Extending Complex Logical Queries on Uncertain Knowledge Graphs
Weizhi Fei, Zihao Wang, Hang Yin +2
The study of machine learning-based logical query answering enables reasoning with large-scale and incomplete knowledge graphs. This paper advances this area of research by address…
Extending Context Window of Large Language Models via Semantic Compression
Weizhi Fei, Xueyan Niu, Pingyi Zhou +4
Transformer-based Large Language Models (LLMs) often impose limitations on the length of the text input to ensure the generation of fluent and relevant responses. This constraint r…
-CQA: Towards Knowledge Graph Complex Query Answering beyond Set Operation
Hang Yin, Zihao Wang, Weizhi Fei +1
To answer complex queries on knowledge graphs, logical reasoning over incomplete knowledge is required due to the open-world assumption. Learning-based methods are essential becaus…
Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport
Zihao Wang, Weizhi Fei, Hang Yin +3
Answering complex queries on knowledge graphs is important but particularly challenging because of the data incompleteness. Query embedding methods address this issue by learning-b…