LLMs and LVLMs encode latent positional count information in individual tokens or visual features, with an internal counter mechanism that updates per item and emerges progressively across layers, relying on structural cues like separators.
Figure 10 shows PCA projections of input tokens, and Figure 11 presents PCA of generated responses
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Understanding Counting Mechanisms in Large Language and Vision-Language Models
LLMs and LVLMs encode latent positional count information in individual tokens or visual features, with an internal counter mechanism that updates per item and emerges progressively across layers, relying on structural cues like separators.