# Progressive Disclosure for LLM-Maintained Wiki Knowledge Bases: a Preregistered Ablation

arXiv: https://arxiv.org/abs/2607.04576 · HTML: https://mlsanity.com/paper/2607.04576/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 6 Jul 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 12 of 12)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [LLM Evaluators Recognize and Favor Their Own Generations](https://mlsanity.com/paper/2404.13076/) (2024)
- [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024)
- [LiveBench: A Challenging, Contamination-Limited LLM Benchmark](https://mlsanity.com/paper/2406.19314/) (2024)
- [Language Models Learn to Mislead Humans via RLHF](https://mlsanity.com/paper/2409.12822/) (2024)
- [Measuring what Matters: Construct Validity in Large Language Model Benchmarks](https://mlsanity.com/paper/2511.04703/) (2025)
- [The Leaderboard Illusion](https://mlsanity.com/paper/2504.20879/) (2025)
- [Single-Round Vector RAG vs an LLM-Compiled Wiki: A Preregistered Comparison on a Small Multi-Domain Research Corpus](https://mlsanity.com/paper/2605.18490/) (2026)

## Abstract (arXiv)

LLM agents now often answer questions from knowledge bases they help maintain. A common intuition says progressive disclosure should make this cheaper. Instead of loading one large index, the agent reads a compact catalog and one-line page summaries, then opens only the pages it needs. We tested that intuition in a preregistered study on a real 709-page markdown knowledge base maintained by an LLM. We retrofitted it for progressive disclosure and built four versions that differ only in how the agent reaches the pages. The pages themselves are identical in every version, so any difference comes from the access structure alone. Each version was tested three ways, with the agent following a set protocol, choosing its own path, or made to load the catalog first. A judge from a different model family graded the answers blind against verified reference answers. A preparatory pilot changed the question. A capable agent never loaded the large index at all. It worked out from the question where a page was and read it directly. The saving we set out to measure did not exist for such an agent, so we made answer quality the primary outcome. Quality held. Answers from the retrofitted knowledge base were as good as answers from the original, within a margin we set in advance. Two limits apply. Our human rater and the model judge agreed far less than the plan required, so the quality result rests on the judge, backed by sensitivity checks. Quality was also not shown to hold when the agent was forced to load the catalog first, or on the two most reliably graded criteria under a stricter test. Cost fell clearly in every condition we tested, and the retrofitted version cited fewer pages and took fewer tool turns per answer.