MoLE applies sparse visual routing per decoder layer to assign visual tokens to subsets of latent visual experts, and uses Expert-Specific Value Adapters (ESVAs) to transform evidence differently.
Specializing latent tokens as visual experts improves vision-language reasoning
MoLE forces latent tokens to extract complementary visual evidence, boosting accuracy across five benchmarks without predefined roles
Chinese Tech
Yingcheng Liu · Tianyi Jiang · Yujuan Ding · jiangbo Ai · Xun Jiang · Guoqing Wang · +2 more
Tongji University · Hong Kong Polytechnic University · Alibaba Group · University of Electronic Science and Technology of China
Research Digest··2 min read
The authors introduce MoLE, a framework that transforms latent tokens into specialized visual experts by controlling both the visual evidence each token receives and how it transforms that evidence.
Why this paper
From Alibaba Group and 3 others
In one line
In latent visual reasoning, specializing latent experts to complementary evidence improves performance more than increasing the number of latent tokens.
What we could check
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- ✓Reports numbers on named benchmarks
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