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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.
It is shown that once denoising, PRM scoring, and outcome reward model (ORM) scoring are charged in the same budget of forward passes, its deterministic form loses to a much simpler baseline.
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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