MIRAGE-ANNS: Mixed Approach Graph-based Indexing for Approximate Nearest Neighbor Search
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Approximate nearest neighbor search (ANNS) on high dimensional vectors is important for numerous applications, such as search engines, recommendation systems, and more recently, large language models (LLMs), where Retrieval Augmented Generation (RAG) is used to add context to an LLM query. Graph-based indexes built on these vectors have been shown to perform best but have challenges. These indexes can either employ refinement-based construction strategies such as K-Graph and NSG, or increment-based strategies such as HNSW. Refinement-based approaches have fast construction times, but worse search performance and do not allow for incremental inserts, requiring a full reconstruction each time new vectors are added to the index. Increment-based approaches have good search performance and allow for incremental inserts, but suffer from slow construction. This work presents MIRAGE-ANNS ( M ixed I ncremental R efinement A pproach G raph-based E xploration for Approximate Nearest Neighbor Search) that constructs the index as fast as refinement-based approaches while retaining search performance comparable or better than increment-based ones. It also allows incremental inserts. We show that MIRAGE achieves state of the art construction and query performance, outperforming existing methods by up to 2x query throughput on real-world datasets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it