| Challenge: | Existing vision-language models lack fine-grained object-level understanding and grounding . existing models implicitly align text tokens with image patch tokens, which is ineffective for embedding alignment at the same granularity and introduces noisy spurious background features. |
| Approach: | They propose a method to prompt large language models with in-context visual object vectors . this method allows for controllable object-level reasoning . |
| Outcome: | The proposed method achieves competitive referring object classification and captioning performance while offering zero-shot generalization and robustness to visually challenging contexts. |
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| Challenge: | Multimodal Large Language Models (MLLMs) have seen remarkable progress in providing general instruction-following ability, but struggle with critical problems when required to provide a detailed and accurate response to a visual instruction. |
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Finer: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models (2024.emnlp-main)
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| Challenge: | Recent advances in instruction-tuned Large Vision-Language Models (LVLMs) have imbued the models with the ability to generate high-level, image-grounded explanations with ease. |
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Out-of-Context Reasoning in Large Language Models (2025.findings-emnlp)
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| Challenge: | a lightweight technique trains only new token embeddings on axioms and evaluates them on unseen tasks. |
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| Challenge: | Large Language Models lack visual grounding on visual reasoning, despite training on text alone. |
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Probing Contextual Language Models for Common Ground with Visual Representations (2021.naacl-main)
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| Challenge: | Contextual language models have attracted great interest in probing what is encoded in their representations. |
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| Challenge: | Existing studies have shown that language models learn from surface form to learn from more grounded evidence. |
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Look Less, Reason More: Rollout-Guided Adaptive Pixel-Space Reasoning (2026.acl-long)
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Matteo Nulli, Vladimir Orshulevich, Tala Bazazo, Christian Herold, Michael Kozielski, Marcin Mazur, Szymon Tuzel, Cees G. M. Snoek, Seyyed Hadi Hashemi, Omar Javed, Yannick Versley, Shahram Khadivi
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CARES: Context-Aware Resolution Selector for VLMs (2026.acl-long)
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| Challenge: | Large vision–language models process images at native or high resolution to remain effective across tasks. |
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