@inproceedings{vo-2026-cover,
title = "{COVER}-{KBC}: Evidence-Centric Adaptive Inference for Closed-Book Knowledge Base Construction",
author = "Vo, Quoc-Linh",
editor = "Kalo, Jan-Christoph and
Nakashole, Ndapa and
Razniewski, Simon and
Suchanek, Fabian M. and
Vlachos, Andreas and
McCallum, Andrew",
booktitle = "Proceedings of the 11th Workshop on Automated Knowledge Base Construction ({AKBC} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.akbc-1.37/",
pages = "480--487",
ISBN = "979-8-89176-464-4",
abstract = "Large Language Models (LLMs) encode substantial factual knowledge, but reliably extracting complete knowledge-base entries remains challenging when relations differ in cardinality, semantics, and missingness. We present COVER-KBC, our system for the AKBC Shared Task 2026, which formulates closed-book knowledge extraction as relation-typed, evidence-centric inference. Each relation is compiled into an inference program that controls elicitation, verification, stopping, and final selection. Model observations are accumulated in a provenance-aware evidence state and scored using independent support, calibrated verification, contradiction, and disagreement. To distinguish candidate correctness from answer-space completeness, COVER-KBC estimates residual search need and uses a budget-aware controller to adaptively acquire further evidence when necessary. Relation-specific finalization handles heterogeneous output structures and difficult numeric or nullable cases. Using a single Mistral-Small-3.2-24B checkpoint without additional training or external retrieval, COVER-KBC achieves a Macro-F1 of 0.5878 on the hidden test set. Our implementation is available at https://github.com/vquclinh/cover-kbc."
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<abstract>Large Language Models (LLMs) encode substantial factual knowledge, but reliably extracting complete knowledge-base entries remains challenging when relations differ in cardinality, semantics, and missingness. We present COVER-KBC, our system for the AKBC Shared Task 2026, which formulates closed-book knowledge extraction as relation-typed, evidence-centric inference. Each relation is compiled into an inference program that controls elicitation, verification, stopping, and final selection. Model observations are accumulated in a provenance-aware evidence state and scored using independent support, calibrated verification, contradiction, and disagreement. To distinguish candidate correctness from answer-space completeness, COVER-KBC estimates residual search need and uses a budget-aware controller to adaptively acquire further evidence when necessary. Relation-specific finalization handles heterogeneous output structures and difficult numeric or nullable cases. Using a single Mistral-Small-3.2-24B checkpoint without additional training or external retrieval, COVER-KBC achieves a Macro-F1 of 0.5878 on the hidden test set. Our implementation is available at https://github.com/vquclinh/cover-kbc.</abstract>
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%0 Conference Proceedings
%T COVER-KBC: Evidence-Centric Adaptive Inference for Closed-Book Knowledge Base Construction
%A Vo, Quoc-Linh
%Y Kalo, Jan-Christoph
%Y Nakashole, Ndapa
%Y Razniewski, Simon
%Y Suchanek, Fabian M.
%Y Vlachos, Andreas
%Y McCallum, Andrew
%S Proceedings of the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-464-4
%F vo-2026-cover
%X Large Language Models (LLMs) encode substantial factual knowledge, but reliably extracting complete knowledge-base entries remains challenging when relations differ in cardinality, semantics, and missingness. We present COVER-KBC, our system for the AKBC Shared Task 2026, which formulates closed-book knowledge extraction as relation-typed, evidence-centric inference. Each relation is compiled into an inference program that controls elicitation, verification, stopping, and final selection. Model observations are accumulated in a provenance-aware evidence state and scored using independent support, calibrated verification, contradiction, and disagreement. To distinguish candidate correctness from answer-space completeness, COVER-KBC estimates residual search need and uses a budget-aware controller to adaptively acquire further evidence when necessary. Relation-specific finalization handles heterogeneous output structures and difficult numeric or nullable cases. Using a single Mistral-Small-3.2-24B checkpoint without additional training or external retrieval, COVER-KBC achieves a Macro-F1 of 0.5878 on the hidden test set. Our implementation is available at https://github.com/vquclinh/cover-kbc.
%U https://aclanthology.org/2026.akbc-1.37/
%P 480-487
Markdown (Informal)
[COVER-KBC: Evidence-Centric Adaptive Inference for Closed-Book Knowledge Base Construction](https://aclanthology.org/2026.akbc-1.37/) (Vo, AKBC 2026)
ACL