Discovering what drives an outcome from data is central to science and often requires deciding what is worth investigating before a specific hypothesis is known. Yet most existing hypothesis generation settings substantially narrow this search by providing a research question, target relationship, or curated context. W...
Sanchit Kabra, Nurendra Choudhary, Jiang-Shu Du et al.· 0 citations
The Second Workshop on SciSoc Agents & LLMs: Agentic AI for Scientific and Societal Advances aims to explore the emerging paradigm of agentic artificial intelligence, where Large Language Models (LLMs) are combined with autonomous agents, tools, memory, and multi-step reasoning to drive scientific discovery and address...
Jia-Yuan Ding, Wei Jin, Carl Yang et al.· Proceedings of the 32nd ACM...· 0 citations
A novel quantitative framework to assess GNN expressiveness for link prediction is introduced, and the edge automorphism ratio (EAR) is introduced, a scalar metric that quantifies a GNN's ability to distinguish links in a given graph.
Shao-Wei Chen, Donald Loveland, Tobias Käfer et al.· 0 citations
This work proposes a simple, parameter-free intervention that initializes SID token embeddings directly from their corresponding centroids in the semantic embedding space, and shows that preserving SID geometry, beyond shared-prefix structure, provides a simple and effective semantic prior for LLM-based GR.
Donald Loveland, Liam Collins, B. Kumar et al.· 0 citations
This work proposes Graph Layer for Inference in Dynamic En- vironments (GLIDE), a novel GNN layer enhanced by two theoretically grounded design mechanisms that significantly improve learning under dynamic topology while preserving robustness in static scenarios.
Chen Shao, Yue Wang, Zhen-Yi Zhu et al.· Lecture notes in computer sc...· 1 citation
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