Formalizing and Mitigating Structural Distortion in LLM Attention for Zero-Shot Graph Reasoning
arXiv:2606.15633v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown promise for reasoning over Text-Attributed Graphs (TAGs). However, applying LLMs to graphs requires linearizing their structure into sequences, introducing distortion rooted in the graph bandwidth problem. While this distortion has been shown to degrade performance, it is often attributed to prompt design or model scale, leaving the underlying mechanism unclear. In this work, we show \textit{how} rotary positional embeddings turn graph linearization into bandwidth-dependent attention decay, suppressing atte
This research provides a deeper, formalized understanding of a known limitation in LLM performance on graph data, offering a pathway to mitigation at a time when LLM capabilities are rapidly expanding.
Understanding and mitigating structural distortions in LLMs for graph reasoning is crucial for unlocking advanced AI applications in complex networked data, which pervades many strategic domains.
This work shifts the understanding of LLM limitations from general prompt design issues to a specific, quantifiable problem related to structural distortion and positional embeddings, enabling targeted solutions.
- · AI researchers
- · LLM developers
- · Graph AI companies
- · Industries relying on complex data analysis
- · Companies with suboptimal LLM graph reasoning approaches
- · Those ignoring fundamental architectural limitations
Improved performance of LLMs on graph-structured data, leading to more accurate analyses and predictions.
Accelerated development of LLM applications in areas like drug discovery, social network analysis, and supply chain optimization.
Enhanced AI 'reasoning' capabilities as LLMs become more adept at handling non-sequential, relational information crucial for higher-order intelligence.
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Read at arXiv cs.LG