In 8,133 real multi-turn AI conversations, the final user prompt contains only ~36% of the session's unique content vocabulary and reproduces the full detected request-state dimension set in only ~26% of dimension-bearing conversations.
Answer-Reconstruction Search Density: Measuring the Query and Source Work Compressed by Conversational Answers
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abstract
Conversational systems can collapse a visible sequence of web queries, result inspections, and source comparisons into a single synthesized answer. Existing retrieval metrics evaluate ranking, effort, or factual support, but they do not quantify the minimum conventional search work represented by a completed answer. We define answer-reconstruction search density (\ARSD): the minimum number of distinct query actions required, under a fixed and dated reconstruction policy, to support a target share of atomic retrievable answer units. A parallel page-density measure separates query compression from source compression.
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The Prompt Is Not the Query: How Request State Evolves Across Multi-Turn AI Conversations
In 8,133 real multi-turn AI conversations, the final user prompt contains only ~36% of the session's unique content vocabulary and reproduces the full detected request-state dimension set in only ~26% of dimension-bearing conversations.