Repeated query failures on a topic are a behavioral signature of a knowledge gap

When an information-seeking system repeatedly receives queries about a topic it cannot resolve well, that clustering pattern is a behavioral signal of absent knowledge — not a property of any single query, but a diagnostic emergent from the query log. Medhat et al. (Georgia Institute of Technology) demonstrate this by mapping conversational AI teaching-assistant questions onto a curriculum prerequisite graph and finding that "conversational AI interaction logs, mapped onto curriculum structure, carry actionable signals about topic-level knowledge gaps." The key insight is that the signal is behavioral rather than per-question: "a student asking many questions about a topic, or asking about it weeks after it was taught, is exhibiting a behavioral signature of difficulty."

Quantitative validation: question frequency correlated with student self-reported difficulty at ρ = 0.491, p = 0.008, across 1,340 questions from 164 students.

The mechanism generalizes beyond tutoring. Any information-seeking agent that logs its queries can analyze that log for clustering. Clusters mark where the agent needed information it didn't have — which is, by definition, a gap. The signal is retrospective: the log must exist before it can be read. This distinguishes it from the structural method in claim-obligatory-attributes-as-gap-signal, which can be run at any time over existing notes without any behavioral trace.

Applied to SeekVault: each research batch run is a query session. When the batch agent searches the vault and returns no matching notes — or when the same topic recurs across multiple Further-leads sections without ever being promoted — those recurrences are behavioral signatures of gaps. A systematic log of unresolved vault searches would constitute the "interaction log" that Medhat et al. show to be gap-diagnostic. Currently SeekVault has no such log; implementing one would make this mechanism operational.

Together with claim-obligatory-attributes-as-gap-signal and claim-llm-explicit-implicit-gap-detection, this method covers a distinct failure mode: entire topics that never made it to notes at all, rather than gaps within existing notes. See question-gap-detection for the detection taxonomy these mechanisms collectively address.