Papers by Weidong Cai
Multimodal Causal Reasoning Benchmark: Challenging Multimodal Large Language Models to Discern Causal Links Across Modalities (2025.findings-acl)
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| Challenge: | Existing MLLMs lack robustness in multimodal causal reasoning compared to their performance in textual settings. |
| Approach: | They propose a novel multimodal chain-of-thought (CoT) reasoning benchmark that leverages siamese images and text pairs to challenge MLLMs. |
| Outcome: | The proposed benchmark leverages siamese images and text pairs to challenge MLLMs. |
Enhancing Advanced Visual Reasoning Ability of Large Language Models (2024.emnlp-main)
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| Challenge: | Recent advances in Vision-Language (VL) research have sparked new benchmarks for complex visual reasoning, challenging models’ advanced reasoning ability. |
| Approach: | They propose a novel multi-modal in-context learning methodology to enhance LLMs’ contextual understanding and reasoning. |
| Outcome: | The proposed model achieves SOTA performance among all visual reasoning tasks and achieves a 'higher level of accuracy' than previous models. |
Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding (2026.acl-long)
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| Challenge: | Existing vision-language models suffer from visual hallucination, where the generated responses contain inaccuracies that are not grounded in the visual input. |
| Approach: | They propose a visual decoding method that iteratively isolates relevant visual tokens based on attention and magnifies the corresponding regions. |
| Outcome: | The proposed method reduces language biases and amplifies weights of visual embedding during decoding, while still preserving strong reasoning capabilities. |
RWKV-CLIP: A Robust Vision-Language Representation Learner (2024.emnlp-main)
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| Challenge: | Using large image-text datasets, large-scale image-data sets have been used for visionlanguage pre-training. |
| Approach: | They propose a framework that leverages Large Language Models to combine and refine information from web-based image-text pairs, synthetic captions, and detection tags. |
| Outcome: | The proposed framework can combine and refine information from web-based image-text pairs, synthetic captions, and detection tags. |