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原文摘要

Inference in LLMs is conventionally a fixed-depth, fixed-order forward pass through every layer, regardless of how difficult the input is. The human brain does not work this way. Using the thalamus as a central hub, it routes information flexibly to all regions of the cortex according to demand. Li et al. (2026) recently showed, with program-of-layers (PoLar), that transformers can be given an analogous flexibility if their layers are treated as a library of functions rather than a fixed sequence. Performance improves over the standard forward pass when each input is dynamically routed through an adaptive sequence of skipped or repeated contiguous layer blocks. Reconstructing PoLar's diagnostic Monte Carlo Tree Search in more detail across 6 models, we reproduce several of its findings: skipping outperforms the standard pass, repeating outperforms skipping, and combining both outperforms either alone. Shorter programs suffice for easier questions, while harder questions require more layer repeats. For their main claim, the learned router's gain, we find what drives it. Under their setup, the router gains by repairing the format of the model's answers, which a single fixed program, selected on validation questions, does better. In our setup, with a prompt that already fixes the format, a larger data split and our search, its top prediction collapses to the standard pass on every model, although its top-5 programs together still gain. Beyond reproduction, we find in our setup that a small fixed menu of programs solves far more questions than the standard pass, and that programs which correct errors are brittle: undoing a single edit typically breaks the correction. PoLar mirrors principles of thalamo-cortical coordination between cortical-area-like transformer layers, although a working thalamus analogue, a router that picks a better program per input, remains to be found.