Papers with dMLLMs
Diffusion-CAM: Faithful Visual Explanations for dMLLMs (2026.acl-long)
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| Challenge: | Existing Class Activation Mapping methods are ill-suited for interpreting non-autoregressive behaviors of diffusion-based architectures. |
| Approach: | They propose to use a method to generate parallel activation maps by probing intermediate representations in the transformer backbone to capture latent features and their class-specific gradients. |
| Outcome: | Experiments show that Diffusion-CAM significantly outperforms SoTA methods in localization accuracy and visual fidelity. |
Fixing Semantic Blind Spots in Anchor Tokens of dMLLMs (2026.findings-acl)
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| Challenge: | Autoregressive models (ARMs) are prone to hallucinations due to their sequential text generation and high latency. |
| Approach: | They propose a training-free decoding strategy that augments the attention key space with a static, distance-aware matrix to reduce the attention sink effect on semantic anchors. |
| Outcome: | The proposed method reduces the attention sink effect on semantic anchors while enhancing their ability to aggregate global visual information. |