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Cost-Aware Query Routing in RAG: Empirical Analysis of Retrieval Depth Tradeoffs

Announce Type: new Abstract: Retrieval-augmented generation (RAG) faces a fundamental three-way tension: deeper retrieval improves factual grounding but inflates token costs and end-to-end latency. Static retrieval configurations cannot resolve this tension across heterogeneous query workloads -- simple definitional queries waste budget on unnecessary context, while complex analytical prompts are underserved by shallow retrieval. This paper introduces \emph{Cost-Aware RAG} (CA-RAG), a...

arXiv CS 7d ago

Slipstream: Locality-Aware Graph Index Construction for Streaming Approximate Nearest Neighbor Search

new Abstract: Graph indexes are widely used for high-recall approximate nearest neighbor search (ANNS), but many real-time applications require streaming ANNS. In these real-time applications, continuously arriving embeddings must search the existing graph for candidate neighbors before updating graph edges, which makes repeated index construction a bottleneck for streaming ingestion workloads. We propose Slipstream, a new method that significantly reduces the computational cost of frequent...

arXiv CS 7d ago

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

arXiv:2602.23665v5 Announce Type: replace 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 on fixed Euclidean distances, \gss{} learns a low-rank metric tensor $\mL_i \in \R^{d \times r}$ at each node, inducing a local positive semi-definite metric $\mG_i = \mL_i \mL_i^\top + \eps \mI$.

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The Evolution of 'More Like This'

In many search scenarios, the user does not start from an empty query box, but from an existing result. A user opens an article and wants to find related material. A buyer views a product card and looks for close alternatives.

Hacker News 19h ago

ACRONYM: Accelerated Approximate Nearest Neighbor Search in Memory for Dynamic Vector Databases

arXiv:2606.03151v1 Announce Type: new Abstract: Vector database search with frequent updates is increasingly critical in applications such as retrieval augmented generation, recommendation systems, and large-scale embedding retrieval. Existing solutions, such as graph-based and partition-based approximate nearest neighbor search (ANNS), suffer from frequent index rebuilding due to data distribution-dependent indexing that impacts continuous deployment and causes long rebuilding latency.

arXiv CS 7d ago