Holographic Memory for Zero-Shot Compositional Reasoning in Knowledge Graphs: A Mechanistic Study of Where and Why It Fails
arXiv:2606.24948v1 Announce Type: new Abstract: Knowledge graph embedding (KGE) models predict single-hop links well but have no mechanism for zero-shot compositional queries: multi-hop questions whose relation chains never appeared during training. Holographic Reduced Representations (HRR), which bind and unbind symbols via circular convolution, are a theoretically attractive candidate, since binding is approximately invertible and associative. We test whether this promise holds. We study two holographic memory variants, real-valued HRR and phase-only Fourier HRR (FHRR), each with a modern Ho
The paper addresses a critical limitation of current knowledge graph embedding models by exploring holographic memory for zero-shot compositional reasoning, pushing the boundaries of AI capabilities.
Improving compositional reasoning in AI enables more sophisticated and adaptive intelligent systems, particularly for tasks requiring understanding of unobserved relational patterns.
This research suggests a potential pathway for AI models to move beyond single-hop predictions towards more complex, multi-hop question answering without prior training on those specific chains.
- · AI research institutions
- · Developers of knowledge graph applications
- · AI algorithm developers
- · Traditional KGE models without compositional reasoning
AI systems will become more capable of understanding and generating novel relational insights from knowledge graphs.
This could lead to breakthroughs in complex reasoning tasks in fields like scientific discovery, drug development, and legal analysis.
More robust and adaptable AI agents capable of generalized reasoning across diverse, dynamic knowledge bases may emerge.
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Read at arXiv cs.LG