How Specific Do I Need to Be? Effects of User Query Underspecification on Deep Research Agents

Key points

Of 203 initial-final query pairs collected from real conversations between researchers and an agent, 176 contained information absent from the initial query, and the authors derived the missing-information types from these pairs.
The three missing information types of scope, terminology, and preference cover 94% of the 176 pairs, appearing in 45%, 37%, and 33% of them respectively.
Across the three agents DR Tulu, Tongyi-DR, and GPT-5.4, removing or obscuring even scope or terminology alone significantly reduced rubric-based scores, and scores declined further as queries became more underspecified.
Citation precision showed no statistically significant change even under full underspecification, so underspecification hurt alignment with the user's information needs rather than increasing hallucination.
Most agents answered without recognizing the ambiguity, and in three of four conditions, runs that stated assumptions exhibited larger answer-quality losses than unaware runs.
In search behavior, exploration transitions increased, the share of tokens traceable to the user query decreased while untraced tokens increased, and retrieving more new documents did not translate into greater use of retrieved content in later searches.

External research summaries. These are not HDATF publications or measured product results.