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In every base and instruction-tuned pair the authors test, instruction tuning strengthens the model's preference for reserved markers, and the gap persists on that channel.
Entropy-Valley (EV), a training-free length selector that scores candidate target canvases by mean predictive entropy from all-mask forward passes and selects the canvas the backbone is most prepared to fill, is introduced.
A descriptive model of agent-documentation interaction is derived as a two-lobed cycle rather than a linear journey, and it is shown that two widely assumed properties of"agent-friendly"documentation - actionability and verifiability - lack consistent behavioural support.
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