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Structural Preservation and the Logical Expressiveness of Graph Neural Networks

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1 SIGNALFIRST DETECTED 17 June 2026
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25 May
Expressive Power of Deep Homomorphism Networks over Relational Databases

arXiv:2605.22852v1 Announce Type: cross Abstract: The expressive limitations of message-passing Graph Neural Networks (GNNs) have motivated a wide range of more powerful graph

REPORT · WHAT · ArXiv CS.AIOPEN STORY
2 Apr
Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents

arXiv:2604.00555v1 Announce Type: new Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce

REPORT · WHAT · ArXiv CS.AIOPEN STORY
13 Apr
GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback

arXiv:2604.08553v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic

REPORT · WHERE · ArXiv CS.AIOPEN STORY
28 Jul
On the Depth Scalability of Logic Gate Networks

arXiv:2607.21633v1 Announce Type: cross Abstract: Logic Gate Networks (LGNs) implement computation through compositions of Boolean operations, yet unlike classical Boolean

REPORT · WHEN · ArXiv CS.AIOPEN STORY
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