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arXiv · 2601.03605

AEScorer: An Agentic Evidence-Grounded Framework for Graded Factuality Verification

Hui Huang·Muyun Yang·Yuki Arase
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Abstract

Despite the significant advancements of Large Language Models (LLMs), their factuality remains a critical challenge, creating a growing need for more nuanced factuality verification. Existing factuality verification methods do not capture graded judgments, even though factuality is better understood as a spectrum rather than a binary of right and wrong. To bridge this gap, we focus on graded factuality verification and propose AEScorer, an agentic evidence-grounded framework with two stages: agentic evidence acquisition and graded scoring. AEScorer first gathers and refines external evidence through agentic search, and then predicts a scalar factuality score to distinguish nuanced differences in factual correctness. We further construct GradedVeriBench, a benchmark for graded factuality verification spanning both general and multi-hop question answering. Experimental results on GradedVeriBench show that AEScorer substantially outperforms existing methods across both settings, demonstrating the value of coupling targeted evidence acquisition with graded scoring.

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Hui Huang, Muyun Yang, Yuki Arase. 2026-01-07. AEScorer: An Agentic Evidence-Grounded Framework for Graded Factuality Verification. https://arxiv.org/abs/2601.03605

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