arXiv AI
Oct 15

Graph-free Retrieval with Graph-Modified Embeddings

arXiv:2610.18410v1 Announce Type: cross Abstract: Dense vector retrieval can miss evidence connected through intermediate entities, while direct graph retrieval requires access to the graph at query...

By Federica Santi (Cognee Inc), Vasilije Markovi\'{c} (Cognee Inc), Daniel Scott (Cognee Inc), Lazar Obradovi\'{c} (Cognee Inc)
arXiv Machine Learning
Oct 5

Learning Query Encoders Can Be Hard Even When Vector Retrieval Is Geometrically Easy

The paper investigates the geometric capacity of vector retrieval systems, focusing on the maximum recall achievable with a fixed document index. It demonstrates that, on real-world benchmarks, single-vector query encoders often underperform relative to the index’s potential. The authors provide theoretical evidence that learning such encoders can be computationally hard, constructing a task where a simple neural network can achieve perfect recall while any statistical-query learner would need exponentially many queries to surpass random chance.

By Anders Wikum, Nina Mishra, Amin Saberi, Tal Wagner
arXiv Computation and Language
Aug 27

Align Then Adapt: Label-Efficient Adapter Learning for Asymmetric Dense Retrieval

The paper introduces Efficient Retrieval Adapter (ERA), a query‑side adapter framework that enables dense retrieval systems to adapt to asymmetric query–document scenarios without re‑indexing. ERA first aligns the embedding spaces of a powerful query embedder and a lightweight document embedder using unlabeled documents, then fine‑tunes the aligned query representation with a small set of labeled query‑document pairs. In experiments on 126 MAIR retrieval tasks across six domains, ERA boosts average nDCG@10 by up to 8.2 points in symmetric settings and over 12 points in asymmetric settings while requiring far fewer labels than fully supervised adapter training.

By Seiji Maekawa, Moin Aminnaseri, Pouya Pezeshkpour, Estevam Hruschka
arXiv Computation and Language
Oct 8

Finding the Right Balance: Relevance and Diversity in LLM Retrieval

arXiv:2610.09412v1 Announce Type: cross Abstract: Retrieval diversification is widely available in retrieval-augmented generation (RAG) frameworks, yet prior studies disagree on whether it improves r...

By Guillaume Brouillette (Universit\'e du Qu\'ebec \`a Trois-Rivi\`eres, Trois-Rivi\`eres, Canada), Faustin Kagabo (Universit\'e du Qu\'ebec \`a Trois-Rivi\`eres, Trois-Rivi\`eres, Canada), Usef Faghihi (Universit\'e du Qu\'ebec \`a Trois-Rivi\`eres, Trois-Rivi\`eres, Canada), Nadia Ghazzali (Universit\'e du Qu\'ebec \`a Trois-Rivi\`eres, Trois-Rivi\`eres, Canada)