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arXiv · 2602.02834

What Structural Inductive Bias Helps Transformers Reason Over Knowledge Graphs? A Study with Tabula RASA

Jonas Petersen·Camilla Mazzoleni·Gian-Alessandro Lombardi·Federico Martelli·Riccardo Maggioni
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Abstract

What structural inductive bias helps transformers reason over knowledge graphs? Through controlled ablations of a minimal transformer modification with four independently removable components (sparse adjacency masking, edge-type biases, query scaling, value gating), we isolate which structural signals drive multi-hop reasoning. Our finding is sharp: sparse adjacency masking alone accounts for the dominant share of improvement over unmasked transformers (+72.5pp on 3-hop MetaQA, +45.5pp on WebQSP, +53.9pp on CWQ), while learned relation parameters add only modest refinement and can actively hurt without structural guidance. A zero-shot experiment provides architecturally independent corroboration: masking-based attention degrades 4.0x less than relation-specific weights when edge types are held out. The useful inductive bias for multi-hop KGQA is predominantly topological, not relational.

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Jonas Petersen, Camilla Mazzoleni, Gian-Alessandro Lombardi, Federico Martelli, Riccardo Maggioni. 2026-06-03. What Structural Inductive Bias Helps Transformers Reason Over Knowledge Graphs? A Study with Tabula RASA. https://arxiv.org/abs/2602.02834

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