Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise to the development of Graph Foundational Models (GFMs). However, current GFMs are challenged by the extreme heterogeneity of graph data, where each graph can possess a unique feature space, label set, and topology. To address this, two main paradigms have emerged. The first leverages Large Language Models (LLMs), but is fundamentally text-dependent, thus struggles to handle the numerical features in vast graphs. The second pre-trains a structure-based model, but the adaptation to new tasks typically requires a costly, per-graph tuning stage, creating a critical efficiency bottleneck. In this work, we move beyond these limitations and introduce \textbf{G}raph \textbf{I}n-context \textbf{L}earning \textbf{T}ransformer (GILT), a framework built on an LLM-free and tuning-free architecture. GILT introduces a novel token-based framework for in-context learning (ICL) on graphs, reframing classification tasks spanning node, edge and graph levels in a unified framework. This mechanism is the key to handling heterogeneity, as it is designed to operate on generic numerical features. Further, its ability to understand class semantics dynamically from the context enables tuning-free adaptation. Comprehensive experiments show that GILT achieves stronger few-shot performance with significantly less time than LLM-based or tuning-based baselines, validating the effectiveness of our approach. Our code is available at: https://github.com/yiming421/inductnode/.
Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning
Helios: A Foundational Language Model for Smart Energy Knowledge Reasoning and Application
College of Energy Engineering, Zhejiang University（浙江大学能源工程学院）；Polytechnic Institute, Zhejiang University（浙江大学 polytechnic 院）；Shanghai Institute for Advanced Study, Zhejiang University（浙江大学上海研究院）
Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models
Xi’an Jiaotong University（西安交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；National University of Singapore（新加坡国立大学）；Shanghai Jiao Tong University（上海交通大学）；Hong Kong University of Science and Technology（香港科技大学）
CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment
School of Software Technology, Zhejiang University（浙江大学软件学院）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Ningbo Global Innovation Center, Zhejiang University（浙江大学宁波全球创新中心）；Washington University in St. Louis（圣路易斯华盛顿大学）；University of Science and Technology of China（中国科学技术大学）；College of Computer Science and Technology, Zhejiang Normal University（浙江师范大学计算机科学与技术学院）；China Electronics Technology Design and Research Institute（中国电子技术设计与研究院）
Retrieval-Augmented Instruction Tuning for Automated Process Engineering Calculations : A Tool-Chaining Problem-Solving Framework with Attributable Reflection
Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression
Department of Computer Science, University of West Florida（计算机科学系，西佛罗里达大学）；Department of Mathematics and Statistics, University of West Florida（数学与统计学系，西佛罗里达大学）；Department of Intelligent Systems and Robotics, University of West Florida（智能系统与机器人系，西佛罗里达大学）；Institute of Human and Machine Cognition（人机认知研究所）；DEVCOM Army Research Laboratory（陆军研究实验室）
RLHF Fine-Tuning of LLMs for Alignment with Implicit User Feedback in Conversational Recommenders
Northeastern University（东北大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；Northwestern University（西北大学）；University of Nebraska - Lincoln（内布拉斯加大学林肯分校）；Columbia University（哥伦比亚大学）
