Graph Memory for Literature Agents: Persistent Neighborhoods for Scientific Search
We show that persistent graph neighborhoods and operator-facing retrieval memory improve literature navigation across papers, people and opportunities.
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Builds collaboration tooling and graph-backed ranking loops for scholarly platforms.
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Applied Research Engineer
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We show that persistent graph neighborhoods and operator-facing retrieval memory improve literature navigation across papers, people and opportunities.
We extract actionable collaboration intent from follows, saves, reviews and topic overlap without collapsing trust into a single opaque metric.
We use graph signals and topical expertise to connect researchers with fellowships, residencies and collaboration openings.