渐进式披露在智能体中不具扩展性

Progressive disclosure in agents doesn't scale.

And its benefits seems agent harness dependent.

(bookmark this one)

Finally there is a proper study on using agent skills and the effect of progressive disclosure.

Progressive disclosure is the agent skills pattern where you hand an agent a document path and let it decide what to read, from a short description down to specific passages.

Practitioners adopted it fast for book-length tasks, purely on vibes.

Researchers ran it across three agent harnesses and three model families on InfiniteBench.

On a single book:

> the gain is harness-dependent, > large when the agent navigates raw documents poorly, > near zero when a strong harness already retrieves on its own.

Scale to many books and raw navigation falls apart while one level of disclosure pulls ahead. A second routing level never helps and sometimes breaks accuracy.

It feels like progressive disclosure buys context, but not intelligence. It is redundant while a strong agent can find the passage itself, and decisive once the corpus is too large to read.

Paper: https://arxiv.org/abs/2607.17598

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