Reranker Optimization via Geodesic Distances on k-NN Manifolds
arXiv:2602.15860v1 Announce Type: new Abstract: Current neural reranking approaches for retrieval-augmented generation (RAG) rely on cross-encoders or large language models (LLMs), requiring substantial computational resources and exhibiting latencies of 3-5 seconds per query. We propose Maniscope, a geometric reranking method that computes geodesic distances on k-nearest neighbor (k-NN) manifolds constructed over retrieved document candidates. This approach combines global cosine similarity with local manifold geometry to capture semantic structure that flat Euclidean metrics miss. Evaluating on eight BEIR benchmark datasets (1,233 queries), Maniscope outperforms HNSW graph-based baseline on the three hardest datasets (NFCorpus: +7.0%, TREC-COVID: +1.6%, AorB: +2.8% NDCG@3) while being 3.2x faster (4.7 ms vs 14.8 ms average). Compared to cross-encoder rerankers, Maniscope achieves within 2% accuracy at 10-45x lower latency. On TREC-COVID, LLM-Reranker provides only +0.5% NDCG@3 impro
arXiv:2602.15860v1 Announce Type: new Abstract: Current neural reranking approaches for retrieval-augmented generation (RAG) rely on cross-encoders or large language models (LLMs), requiring substantial computational resources and exhibiting latencies of 3-5 seconds per query. We propose Maniscope, a geometric reranking method that computes geodesic distances on k-nearest neighbor (k-NN) manifolds constructed over retrieved document candidates. This approach combines global cosine similarity with local manifold geometry to capture semantic structure that flat Euclidean metrics miss. Evaluating on eight BEIR benchmark datasets (1,233 queries), Maniscope outperforms HNSW graph-based baseline on the three hardest datasets (NFCorpus: +7.0%, TREC-COVID: +1.6%, AorB: +2.8% NDCG@3) while being 3.2x faster (4.7 ms vs 14.8 ms average). Compared to cross-encoder rerankers, Maniscope achieves within 2% accuracy at 10-45x lower latency. On TREC-COVID, LLM-Reranker provides only +0.5% NDCG@3 improvement over Maniscope at 840x higher latency, positioning Maniscope as a practical alternative for real-time RAG deployment. The method requires O(N D + M^2 D + M k log k) complexity where M << N , enabling sub-10 ms latency. We plan to release Maniscope as open-source software.
Executive Summary
The article introduces Maniscope, a novel geometric reranking method designed to enhance retrieval-augmented generation (RAG) systems. Unlike traditional cross-encoders or large language models (LLMs), Maniscope leverages geodesic distances on k-nearest neighbor (k-NN) manifolds to capture semantic structure more effectively. This approach demonstrates superior performance on challenging datasets while significantly reducing latency, making it a practical alternative for real-time RAG deployment. The method's efficiency and accuracy position it as a promising solution for applications requiring rapid and precise information retrieval.
Key Points
- ▸ Maniscope uses geodesic distances on k-NN manifolds for reranking.
- ▸ It outperforms HNSW graph-based baselines on challenging datasets.
- ▸ Maniscope achieves comparable accuracy to cross-encoders with much lower latency.
- ▸ The method is computationally efficient, enabling sub-10 ms latency.
- ▸ Planned open-source release will facilitate wider adoption.
Merits
Superior Performance
Maniscope demonstrates significant improvements in accuracy on challenging datasets, outperforming existing baselines and achieving near-parity with more resource-intensive methods.
Computational Efficiency
The method's low latency and computational complexity make it suitable for real-time applications, addressing a critical need in RAG systems.
Innovative Approach
By combining global cosine similarity with local manifold geometry, Maniscope captures semantic structure more effectively than flat Euclidean metrics.
Demerits
Limited Dataset Scope
The evaluation is conducted on a specific set of datasets, which may not fully represent the diverse range of real-world applications.
Complexity in Implementation
The method's reliance on k-NN manifolds and geodesic distances may introduce complexity in implementation and deployment.
Potential Generalization Issues
The performance gains observed on specific datasets may not generalize to all types of retrieval tasks or domains.
Expert Commentary
The introduction of Maniscope represents a significant advancement in the field of retrieval-augmented generation. By leveraging geodesic distances on k-NN manifolds, the method effectively captures the semantic structure of data, addressing a critical limitation of traditional Euclidean metrics. The substantial performance improvements on challenging datasets, coupled with the method's computational efficiency, position Maniscope as a viable alternative to more resource-intensive approaches. However, the method's complexity and potential generalization issues warrant further investigation. The planned open-source release will be instrumental in facilitating broader adoption and validation of Maniscope's capabilities. Overall, this work contributes valuable insights to the ongoing efforts to optimize information retrieval systems for real-time applications.
Recommendations
- ✓ Further validation on a broader range of datasets to assess generalization.
- ✓ Development of user-friendly tools and documentation to simplify implementation.
- ✓ Exploration of potential applications in other domains beyond RAG systems.
