Causally-Grounded Dual-Path Attention Intervention for Object Hallucination Mitigation in LVLMs

• URL: http://arxiv.org/abs/2511.09018v1
• Date: Thu, 13 Nov 2025 01:26:20 GMT
• Title: Causally-Grounded Dual-Path Attention Intervention for Object Hallucination Mitigation in LVLMs
• Authors: Liu Yu, Zhonghao Chen, Ping Kuang, Zhikun Feng, Fan Zhou, Lan Wang, Gillian Dobbie,
• Abstract summary: We propose a framework that models hallucination process via a structural causal graph.<n>We introduce VTACR, a novel metric that quantifies the modality contribution imbalance during decoding.<n>We design a fine-language attention intervention mechanism that dynamically adjusts token- and layer-wise attention.
• Score: 26.144870818163387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object hallucination remains a critical challenge in Large Vision-Language Models (LVLMs), where models generate content inconsistent with visual inputs. Existing language-decoder based mitigation approaches often regulate visual or textual attention independently, overlooking their interaction as two key causal factors. To address this, we propose Owl (Bi-mOdal attention reWeighting for Layer-wise hallucination mitigation), a causally-grounded framework that models hallucination process via a structural causal graph, treating decomposed visual and textual attentions as mediators. We introduce VTACR (Visual-to-Textual Attention Contribution Ratio), a novel metric that quantifies the modality contribution imbalance during decoding. Our analysis reveals that hallucinations frequently occur in low-VTACR scenarios, where textual priors dominate and visual grounding is weakened. To mitigate this, we design a fine-grained attention intervention mechanism that dynamically adjusts token- and layer-wise attention guided by VTACR signals. Finally, we propose a dual-path contrastive decoding strategy: one path emphasizes visually grounded predictions, while the other amplifies hallucinated ones -- letting visual truth shine and hallucination collapse. Experimental results on the POPE and CHAIR benchmarks show that Owl achieves significant hallucination reduction, setting a new SOTA in faithfulness while preserving vision-language understanding capability. Our code is available at https://github.com/CikZ2023/OWL

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