[Submitted on 13 Jul 2026]
Title:Relation Before Entity: Deferred Commitment in Language Model Factual Recall
View a PDF of the paper titled Relation Before Entity: Deferred Commitment in Language Model Factual Recall, by Divyansh Agarwal
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Abstract:We ask whether relation-type information (e.g., capital-of) and entity-specific information (e.g., France to Paris) become causally active at the final-token position at the same depth during recall. Using four complementary causal diagnostics across four decoder-only models and eight prompt families, we find a robust temporal asymmetry: relation information becomes generation-controlling before entity information does. Relation onset precedes entity onset by 10-16 tested layers (31-44% of network depth) at threshold 0.4, with the ordering holding across all 16 model-threshold combinations for thresholds 0.2-0.5. Critically, entity information is not absent early: entity-token patching succeeds at 90-100% in early layers. Instead, entity commitment to generation is deferred: entity information is available at the entity-token position but becomes generation-controlling at the final token only after being routed there.
Comments: 8 pages, 4 figures. Accepted at the Mechanistic Interpretability Workshop at the 43rd International Conference on Machine Learning (ICML 2026). Code available at this https URL
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2609.17537 [cs.CL]
(or arXiv:2609.17537v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.17537
arXiv-issued DOI via DataCite
Submission history
From: Divyansh Agarwal [view email] [v1] Mon, 13 Jul 2026 22:34:40 UTC (214 KB)
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