V-ABS: Action-Observer Driven Beam Search for Dynamic Visual Reasoning
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913111031676928 |
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| author | Ning, Zhiwei Gao, Xuanang Cao, Jiaxi Zhang, Gengming Ma, Shengnan Tong, Wenwen Deng, Hanming Yang, Jie Liu, Wei |
| author_facet | Ning, Zhiwei Gao, Xuanang Cao, Jiaxi Zhang, Gengming Ma, Shengnan Tong, Wenwen Deng, Hanming Yang, Jie Liu, Wei |
| contents | Multimodal large language models (MLLMs) have achieved remarkable success in general perception, yet complex multi-step visual reasoning remains a persistent challenge. Although recent agentic approaches incorporate tool use, they often neglect critical execution feedback. Consequently, they suffer from the imagination-action-observer (IAO) bias, a misalignment between prior imagination and observer feedback that undermines reasoning stability and optimality. To bridge this gap, we introduce V-ABS, an action-observer driven beam search framework that enables deliberate reasoning through thinker-actor-observer iterations. We also propose an entropy-based adaptive weighting algorithm to mitigate the IAO bias by dynamically balancing the confidence scores between the policy priors and the observational feedback. Moreover, we construct a large-scale supervised fine-tuning (SFT) dataset comprising over 80k samples to guide the model to assign higher prior confidence to correct action paths. Extensive experiments across eight diverse benchmarks show that V-ABS achieves state-of-the-art performance, delivering an average improvement of 19.7% on the Qwen3-VL-8B baseline and consistent gains across both open-source and proprietary models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10172 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | V-ABS: Action-Observer Driven Beam Search for Dynamic Visual Reasoning Ning, Zhiwei Gao, Xuanang Cao, Jiaxi Zhang, Gengming Ma, Shengnan Tong, Wenwen Deng, Hanming Yang, Jie Liu, Wei Computer Vision and Pattern Recognition Computation and Language Multimodal large language models (MLLMs) have achieved remarkable success in general perception, yet complex multi-step visual reasoning remains a persistent challenge. Although recent agentic approaches incorporate tool use, they often neglect critical execution feedback. Consequently, they suffer from the imagination-action-observer (IAO) bias, a misalignment between prior imagination and observer feedback that undermines reasoning stability and optimality. To bridge this gap, we introduce V-ABS, an action-observer driven beam search framework that enables deliberate reasoning through thinker-actor-observer iterations. We also propose an entropy-based adaptive weighting algorithm to mitigate the IAO bias by dynamically balancing the confidence scores between the policy priors and the observational feedback. Moreover, we construct a large-scale supervised fine-tuning (SFT) dataset comprising over 80k samples to guide the model to assign higher prior confidence to correct action paths. Extensive experiments across eight diverse benchmarks show that V-ABS achieves state-of-the-art performance, delivering an average improvement of 19.7% on the Qwen3-VL-8B baseline and consistent gains across both open-source and proprietary models. |
| title | V-ABS: Action-Observer Driven Beam Search for Dynamic Visual Reasoning |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2605.10172 |