1 citations · 2 across the 15 of their papers we have counts for
16 papers
MentorPulse: Refreshing Cross-Model Latent Guidance for Long-Form Generation
Ziwu Liu, Guozhong Li, Chen Qiu +2
Cross-model latent guidance lets a frozen large mentor encode an input once and a frozen small student generate from the resulting signal. Existing methods keep this signal fixed,…
KVDiagnosis: A Diagnostic Benchmark for KV-Cache Compression in Long-Context Language Models
Chen Qiu, Ziwu Liu, Chao Fei +2
KV-cache compression reduces long-context memory, but aggregate task scores reveal neither which correct executions fail nor why. We present KVDiagnosis, a diagnostic dataset and b…
EMAS: Stabilizing Multi-Agent System Evolution through Evidence-Guided Revision
Chao Fei, Qingyi Si, Kaihua Liang +3
Many methods for automated multi-agent system design optimize prompts and topologies during an initial design stage and then deploy the resulting system unchanged on subsequent sam…
ART: Attention Run-time Termination for Efficient Large Language Model Decoding
Chen Qiu, Guozhong Li, Cristian McGee +2
Long-context decoding in Large Language Models (LLMs) is constrained by the cost of accessing and processing the Key-Value (KV) cache. Despite evidence that attention outputs depen…
Exploring a Layer-Wise Design Space for KV Cache Eviction
Chao Fei, Kaihua Liang, Panos Kalnis +4
KV cache eviction methods typically use a single retention-rule family throughout a model, making eviction-method identity a model-level design choice. Yet Transformer layers diffe…
Can Deep Neural Networks Improve Compression of Very Large Scientific Data?
Muhannad Alhumaidi, Guozhong Li, Spiros Skiadopoulos +1
Error-bounded lossy compression is a fundamental technique for managing the rapidly growing volumes of scientific data produced by modern simulations and observational instruments.…
From GPS Points to Travel Patterns: Flexible and Semantic Trajectory Generation with LLMs
Silin Zhou, Chenhao Wang, Yuntao Wen +3
Urban trajectories play a crucial role in modeling urban dynamics and supporting various smart city applications. However, privacy concerns restrict access to large-scale and high-…
Leveraging LLM-GNN Integration for Open-World Question Answering over Knowledge Graphs
Hussein Abdallah, Ibrahim Abdelaziz, Panos Kalnis +1
Open-world Question Answering (OW-QA) over knowledge graphs (KGs) aims to answer questions over incomplete or evolving KGs. Traditional KGQA assumes a closed world where answers mu…
CHESS: Context-aware Hierarchical Efficient Semantic Selection for Long-Context LLM Inference
Chao Fei, Guozhong Li, Chenxi Liu +1
Long-context LLMs demand accurate inference at low latency, yet decoding becomes primarily constrained by KV cache as context grows. Prior pruning methods are largely context-agnos…
Chatty-KG: A Multi-Agent AI System for On-Demand Conversational Question Answering over Knowledge Graphs
Reham Omar, Abdelghny Orogat, Ibrahim Abdelaziz +3
Conversational Question Answering over Knowledge Graphs (KGs) combines the factual grounding of KG-based QA with the interactive nature of dialogue systems. KGs are widely used in…
LLMComp: A Language Modeling Paradigm for Error-Bounded Scientific Data Compression (Technical Report)
Guozhong Li, Muhannad Alhumaidi, Spiros Skiadopoulos +1
The rapid growth of high-resolution scientific simulations and observation systems is generating massive spatiotemporal datasets, making efficient, error-bounded compression increa…
Comprehending Spatio-temporal Data via Cinematic Storytelling using Large Language Models
Panos Kalnis. Shuo Shang, Christian S. Jensen
Spatio-temporal data captures complex dynamics across both space and time, yet traditional visualizations are complex, require domain expertise and often fail to resonate with broa…
LLMs Meet Cross-Modal Time Series Analytics: Overview and Directions
Chenxi Liu, Hao Miao, Cheng Long +3
Large Language Models (LLMs) have emerged as a promising paradigm for time series analytics, leveraging their massive parameters and the shared sequential nature of textual and tim…
GraphComp: Extreme Error-bounded Compression of Scientific Data via Temporal Graph Autoencoders
Guozhong Li, Muhannad Alhumaidi, Spiros Skiadopoulos +2
The generation of voluminous scientific data poses significant challenges for efficient storage, transfer, and analysis. Recently, error-bounded lossy compression methods emerged d…
RED: Effective Trajectory Representation Learning with Comprehensive Information
Silin Zhou, Shuo Shang, Lisi Chen +2
Trajectory representation learning (TRL) maps trajectories to vectors that can then be used for various downstream tasks, including trajectory similarity computation, trajectory cl…
Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling
Hussein Abdallah, Waleed Afandi, Panos Kalnis +1
A Knowledge Graph (KG) is a heterogeneous graph encompassing a diverse range of node and edge types. Heterogeneous Graph Neural Networks (HGNNs) are popular for training machine le…