Multimodal UNcommonsense: From Odd to Ordinary and Ordinary to Odd (2025.findings-emnlp)
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| Challenge: | Multimodal UNcommonsense (MUN) is a benchmark designed to evaluate models’ ability to handle scenarios that deviate from typical visual or contextual expectations. |
| Approach: | They propose a retrieval-based in-context learning framework that transfers reasoning capabilities from larger models to smaller ones without additional training. |
| Outcome: | The proposed method improves on baseline ICL methods by 8.3% over previous methods. |
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| Challenge: | In-context learning (ICL) enables models to adapt to new tasks via inference-time demonstrations. |
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Interleaved Latent Visual Reasoning with Selective Perceptual Modeling (2026.acl-long)
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