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Can classic large language models develop brain-like exportation without augmentations?

Determine whether classic large language models trained on text alone, without external augmentations or architectural changes, can develop brain-like exportation mechanisms that transfer information from linguistic representations to functionally specialized subsystems.

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Background

The authors distinguish shallow, language-statistics-based processing from deep understanding that involves exporting information to extra-linguistic systems. Some AI systems explicitly add such mechanisms via augmentations (e.g., vision models, physics engines), whereas standard LLMs may or may not develop analogous internal mechanisms.

Evidence suggests some units in standard LLMs appear functionally specialized, but the causal importance of these units is debated, leaving open whether unaugmented models can instantiate exportation-like processes analogous to those hypothesized in the brain.

References

There is also some evidence that individual units within a standard LLM—without any additional augmentations or architectural changes—can become functionally specialized [134-135]; however, the causal importance of these functionally-specific units remains debated (e.g., [136]), leaving open the question of whether classic LLMs can develop brain-like exportation.

— What does it mean to understand language? (2511.19757 - Casto et al., 24 Nov 2025) in Box 2: Shallow vs. deep understanding in large language models (LLMs)