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New Geodesic Semantic Search method improves citation graph navigation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Geodesic Semantic Search (GSS), a novel retrieval system that navigates citation graphs using learned, node-specific Riemannian metrics. Unlike traditional methods relying on fixed Euclidean distances, GSS learns local metrics to enable geometry-aware semantic search. This approach demonstrated a 23% relative improvement in citation prediction recall on a benchmark of 169,000 arXiv papers compared to existing baselines. AI

IMPACT Introduces a novel geometry-aware semantic search method for citation graphs, potentially improving academic literature discovery.

RANK_REASON This is a research paper detailing a new retrieval system with experimental results.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Geodesic Semantic Search method improves citation graph navigation

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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Brandon Yee, Lucas Wang, Kundana Kommini ·

    Geodesic Semantic Search: Cartographic Navigation of Citation Graphs with Learned Local Riemannian Maps

    arXiv:2602.23665v4 Announce Type: replace-cross Abstract: We present Geodesic Semantic Search (GSS), a retrieval system that learns node-specific Riemannian metrics on citation graphs to enable geometry-aware semantic search. Unlike standard embedding-based retrieval that relies …

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