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About this paper
Jiawei Guo, Yu Chen, Xiang Wang, Shuai Li, Xinpei Zhao, Huaxing Liu, Shuai Dong, Feifei Zhai and 1 more
Research diagnostically disassembles latent tokens in multimodal language models, finding that visual reasoning performance gains do not primarily stem from latent slots storing recoverable visual memory, but rather from boundary markers functioning as control signals and focused visual attention, even when models achieve comparable accuracy via distinct internal mechanisms.
Key insights
- Latent slots do not primarily function as recoverable visual memory, showing weak causal influence and limited portability of specific visual content.
- Boundary markers are crucial learned control signals that manage the entry into and exit from latent generation, rather than mere placeholders.
- Different MLLMs can achieve comparable end-task accuracy by employing widely divergent underlying mechanisms, emphasizing the necessity of mechanistic evaluation beyond overall performance metrics.
Summary by alphaXiv.
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