7 papers
HEPTv2: End-to-End Efficient Point Transformer for Charged Particle Reconstruction
Siqi Miao, Shitij Govil, Jack P. Rodgers +5
Charged-particle tracking -- reconstructing trajectories from sparse detector measurements -- is a fundamental high-energy-physics inference problem and a canonical example of lear…
Can LLM Agents Simulate Dynamic Networks? A Case Study on Email Networks with Phishing Synthesis
Siqi Miao, Ziyang Chen, Yuhong Luo +4
While Large Language Model (LLM) multi-agent systems (MAS) offer a transformative approach to simulating human behavior in complex systems, it remains largely unexplored whether th…
Towards A Universal Graph Structural Encoder
Jialin Chen, Haolan Zuo, Haoyu Peter Wang +3
Recent advancements in large-scale pre-training have shown the potential to learn generalizable representations for downstream tasks. In the graph domain, however, capturing and tr…
Graph-KV: Breaking Sequence via Injecting Structural Biases into Large Language Models
Haoyu Wang, Peihao Wang, Mufei Li +4
Modern large language models (LLMs) are inherently auto-regressive, requiring input to be serialized into flat sequences regardless of their structural dependencies. This serializa…
Locality-Sensitive Hashing-Based Efficient Point Transformer for Charged Particle Reconstruction
Shitij Govil, Jack P. Rodgers, Yuan-Tang Chou +9
Charged particle track reconstruction is a foundational task in collider experiments and the main computational bottleneck in particle reconstruction. Graph neural networks (GNNs)…
Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation
Deyu Zou, Yongqiang Chen, Mufei Li +5
Graph-based retrieval-augmented generation (RAG) enables large language models (LLMs) to ground responses with structured external knowledge from up-to-date knowledge graphs (KGs)…