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

arXiv: https://arxiv.org/abs/2601.03605 · HTML: https://mlsanity.com/paper/2601.03605/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 25 of its 44 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 25 of 25)

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- [MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://mlsanity.com/paper/2404.10774/) (2024)
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- [WiCE: Real-World Entailment for Claims in Wikipedia](https://mlsanity.com/paper/2303.01432/) (2023)
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- [HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification](https://mlsanity.com/paper/2504.07069/) (2025)
- [FactLens: Benchmarking Fine-Grained Fact Verification](https://mlsanity.com/paper/2411.05980/) (2024)
- [Generative or Discriminative? Revisiting Text Classification in the Era of Transformers](https://mlsanity.com/paper/2506.12181/) (2025)
- [A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT](https://mlsanity.com/paper/2312.11870/) (2023)
- [Learning to Verify Summary Facts with Fine-Grained LLM Feedback](https://mlsanity.com/paper/2412.10689/) (2024)

## Abstract (arXiv)

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.