Papers with DiVE
Enhancing Agent Learning through World Dynamics Modeling (2024.findings-emnlp)
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| Challenge: | Existing methods assume that large language models have a complete understanding of their environment, overlooking potential gaps in their grasp of actual world dynamics. |
| Approach: | They propose a framework that discovers world dynamics from a small number of demonstrations, verifies the correctness of these dynamics, and evolves new, advanced dynamics tailored to the current situation. |
| Outcome: | The proposed framework discovers, verifies, and evolves world dynamics from a small number of demonstrations, and compares the automatically generated dynamics with human-annotated world dynamics. |
DiVE: Decoupling Intra-layer Visual Evidence for Mitigating Hallucinations in Large Vision-Language Models (2026.acl-long)
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| Challenge: | Existing decoding-based approaches do not explicitly decouple visual evidence from mixed vision–language representations. |
| Approach: | They propose to decouple visual evidence from mixed vision–language representations by dynamically identifying layers enriched with visual information and performing intra-layer decoupling to extract aggregated visual evidence. |
| Outcome: | Experiments show that DiVE achieves state-of-the-art performance on multiple benchmarks. |