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To find semantic connections across multiple notes, you can use cross-note reasoning to identify relationships, cluster ideas by core topics, and return a structured map of actionable insights.

Clustering scattered ideas and discovering recurring themes involves analyzing a corpus to group notes by core topics, naming clusters, and tracking the evolution of long-term patterns across time.

Yes, you can analyze notes with inconsistent formats for idea clustering and pattern discovery, as the system gracefully handles various input formats and minor inconsistencies during cross-note reasoning.

Generating connection hypotheses between different notes requires cross-note reasoning to propose plausible links between ideas and provide the rationale behind each proposed semantic relationship.

When surfacing hidden note connections, you get structured outputs suitable for analysis, including named semantic clusters, proposed connection hypotheses with rationale, and tracked long-term theme evolutions.