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[2602.14065] REAL: Resolving Knowledge Conflicts in Knowledge-Intensive Visual Question Answering via Reasoning-Pivot Alignment
Summary
The paper presents the REAL framework, which addresses knowledge conflicts in Knowledge-Intensive Visual Question Answering (KI-VQA) by introducing Reasoning-Pivot Alignment for improved accuracy and performance.
Why It Matters
As KI-VQA systems increasingly rely on open-domain retrieval, resolving knowledge conflicts is crucial for enhancing their reliability and effectiveness. The REAL framework offers a novel approach that could significantly advance the field, making it relevant for researchers and practitioners in AI and machine learning.
Key Takeaways
- Introduces the REAL framework to resolve knowledge conflicts in KI-VQA.
- Utilizes Reasoning-Pivot Alignment to enhance reasoning processes.
- Demonstrates improved discrimination accuracy and state-of-the-art performance.
- Constructs a dedicated REAL-VQA dataset to support the framework.
- Integrates innovative strategies like RPA-SFT and RPGD for conflict mitigation.
Computer Science > Artificial Intelligence arXiv:2602.14065 (cs) [Submitted on 15 Feb 2026] Title:REAL: Resolving Knowledge Conflicts in Knowledge-Intensive Visual Question Answering via Reasoning-Pivot Alignment Authors:Kai Ye, Xianwei Mao, Sheng Zhou, Zirui Shao, Ye Mo, Liangliang Liu, Haikuan Huang, Bin Li, Jiajun Bu View a PDF of the paper titled REAL: Resolving Knowledge Conflicts in Knowledge-Intensive Visual Question Answering via Reasoning-Pivot Alignment, by Kai Ye and 8 other authors View PDF HTML (experimental) Abstract:Knowledge-intensive Visual Question Answering (KI-VQA) frequently suffers from severe knowledge conflicts caused by the inherent limitations of open-domain retrieval. However, existing paradigms face critical limitations due to the lack of generalizable conflict detection and intra-model constraint mechanisms to handle conflicting evidence. To address these challenges, we propose the REAL (Reasoning-Pivot Alignment) framework centered on the novel concept of the Reasoning-Pivot. Distinct from reasoning steps that prioritize internal self-derivation, a reasoning-pivot serves as an atomic unit (node or edge) in the reasoning chain that emphasizes knowledge linkage, and it typically relies on external evidence to complete the reasoning. Supported by our constructed REAL-VQA dataset, our approach integrates Reasoning-Pivot Aware SFT (RPA-SFT) to train a generalizable discriminator by aligning conflicts with pivot extraction, and employs Reasoning-Piv...
