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arxiv: 2606.19898 · v1 · pith:ZLIR4YH4 · submitted 2026-06-18 · cs.DB · cs.IR

Query-aware Routing for Filtered Approximate Nearest Neighbors Search

Qianqian Xiong , Mengxuan Zhang This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-26 15:28 UTCgrok-4.3pith:ZLIR4YH4record.jsonopen to challenge →

classification cs.DB cs.IR
keywords filtered ANNapproximate nearest neighbor searchquery routingvector databaserecall predictionQPS optimizationmachine learning for databases
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The pith

A lightweight model routes each filtered ANN query to the method with the best recall-QPS tradeoff for that query.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

No single filtered ANN method performs best across all queries, even within one dataset and predicate type. The authors therefore train a regression model on six datasets to predict recall for each candidate method from three query features, then consult an offline table of measured recall and QPS values to pick the method with the strongest tradeoff. The router is applied without retraining to five unseen validation datasets under three predicates and records better overall recall-QPS balance than any fixed baseline while adding negligible latency. A reader would care because modern vector databases and retrieval-augmented generation systems depend on fast filtered vector search, and query-dependent selection removes the need to commit to one suboptimal method in advance.

Core claim

No single categorical filtered ANN method dominates across datasets and predicates, and the best method for a given query can change even within a single dataset and predicate. The query-aware router therefore uses a lightweight regression model, narrowed by ablation to three features, to predict each method's recall on the incoming query; it then consults a precomputed table that records the actual recall and QPS of every method-parameter pair and selects the pair offering the best recall-QPS tradeoff. The model is trained on six real-world datasets and evaluated on five held-out validation datasets, where the router matches or exceeds the recall-QPS frontier of all existing filtered ANN ba

What carries the argument

Query-aware routing framework that combines a three-feature regression recall predictor with an offline benchmark table mapping each method and parameter setting to its measured recall and QPS.

If this is right

  • Vector databases can replace a single fixed filtered ANN method with dynamic per-query selection without rebuilding indexes.
  • The same router design improves the recall-QPS operating point on every one of the five validation datasets tested.
  • Only three query features are required to keep prediction accurate enough for the router to outperform static baselines.
  • Adding the router introduces negligible latency overhead while still achieving state-of-the-art balance on unseen data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The routing approach could be applied to other query-time choices in databases, such as selecting between different index structures or join algorithms.
  • If the three-feature model continues to generalize, production systems could reduce manual parameter tuning for filtered ANN workloads.
  • The technique mirrors cost-model-driven operator selection in traditional query optimizers, suggesting a broader pattern for adaptive retrieval pipelines.

Load-bearing premise

The lightweight regression model trained on six datasets can accurately predict recall for each candidate method on queries from five unseen validation datasets using only the final three features.

What would settle it

Measure whether the router's chosen methods on a sixth unseen dataset actually deliver higher recall at the same QPS than the single best fixed baseline across the same queries.

Figures

Figures reproduced from arXiv: 2606.19898 by Mengxuan Zhang, Qianqian Xiong.

Figure 1
Figure 1. Figure 1: Recall-QPS Pareto curves of filtered ANN methods [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Recall–QPS comparison of all benchmarked filtered ANN methods across datasets and predicate types. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Query-aware ML routing pipeline. Algorithm 2 Per-Query ML Routing Require: query 𝑞, dataset context ds, deployment threshold 𝑇 Ensure: (method 𝑚∗ , parameter setting ps∗ ) 1: x ← ExtractFeatures(𝑞, ds) // Section 4.2 2: for each 𝑚 ∈ M do 3: 𝑟ˆ𝑚 ← 𝑓𝑚 (x) // five MLP forwards 4: end for 5: P ← {𝑚 ∈ M : 𝑟ˆ𝑚 ≥ 𝑇 } // 𝑇 -threshold filter 6: if P ≠ ∅ then 7: for each 𝑚 ∈ P do 8: ps𝑚 ← arg maxps QPS(𝐵[ds, pt,𝑚, p… view at source ↗
Figure 4
Figure 4. Figure 4: MLP-Reg validation recall vs. feature count. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Recall–QPS Pareto on all combinations of dataset and predicate type. [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
read the original abstract

Filtered ANN search, which combines vector similarity with attribute predicates, is a core primitive in modern vector databases and retrieval-augmented generation. We benchmark all major categorical filtered ANN methods across multiple datasets under three predicates and find that no single method dominates. Moreover, even within a single dataset and predicate type, the best method for a query can vary. Therefore, we propose a query-aware routing framework. A lightweight ML model predicts each candidate method's recall on the query, and the router consults an offline benchmark table that maps every method and parameter setting to its measured recall and QPS, then selects the method with the best recall--QPS trade-off. Our ablation study narrows 22 candidate features to a minimal set of three and we adopt regression rather than classification as the prediction target to sharpen accuracy. Our model is trained on six real-world datasets and applied to five unseen validation datasets. The final result shows that our router achieves state-of-the-art recall and QPS balance across all five validation datasets compared to existing filtered ANN baselines, while incurring negligible latency overhead.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper benchmarks major categorical filtered ANN methods across datasets and predicates, finding no single method dominates and that the best method can vary even within a dataset. It proposes a query-aware router that trains a lightweight regression model (reduced via ablation from 22 to 3 features) on six datasets to predict per-query recall for candidate methods, then uses an offline table of measured recall/QPS values to select the method-parameter pair with the best trade-off. The model is applied to five unseen validation datasets, with the claim that the router achieves SOTA recall-QPS balance versus baselines at negligible latency cost.

Significance. If the reported generalization holds, the framework addresses a practical limitation in filtered ANN search for vector databases by enabling dynamic, query-specific method selection without significant overhead. The choice of regression over classification and the feature ablation are constructive steps toward deployable systems; the offline table plus predictor approach is a reasonable engineering pattern when the predictor is shown to be reliable.

major comments (3)
  1. [Abstract] Abstract: the SOTA recall-QPS claim on the five validation datasets is load-bearing on the three-feature regression model's accuracy, yet the manuscript reports no per-dataset prediction error (e.g., MAE, R²), correlation, or ablation-retention metrics on the held-out validation split to confirm that the feature reduction preserves predictive power for unseen data.
  2. [Experimental results] The experimental results section (and abstract) states consistent gains on five validation sets after training on six others, but provides no error bars, exact dataset statistics, or full experimental protocol (training procedure, hyper-parameters, query sampling), leaving the magnitude and reliability of the gains difficult to assess.
  3. [Query-aware routing framework] The router's selection logic relies on an external offline benchmark table; it is unclear from the description whether this table was constructed independently for the validation datasets or whether any form of data leakage exists between the table construction and the validation queries.
minor comments (2)
  1. [Ablation study] Notation for the three retained features after ablation should be defined explicitly (e.g., what the final three features represent) rather than left as 'the final three features.'
  2. The manuscript would benefit from a table or figure showing the distribution of selected methods across the validation queries to illustrate that routing is indeed query-dependent.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment below. Where the comments identify missing details or clarity issues, we will revise the manuscript to incorporate the requested information and clarifications.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the SOTA recall-QPS claim on the five validation datasets is load-bearing on the three-feature regression model's accuracy, yet the manuscript reports no per-dataset prediction error (e.g., MAE, R²), correlation, or ablation-retention metrics on the held-out validation split to confirm that the feature reduction preserves predictive power for unseen data.

    Authors: We agree that explicit per-dataset metrics on the validation split would better substantiate the generalization claim. In the revision we will add a table reporting MAE, R², Pearson correlation, and ablation-retention statistics for the three-feature model on each of the five held-out validation datasets individually. revision: yes

  2. Referee: [Experimental results] The experimental results section (and abstract) states consistent gains on five validation sets after training on six others, but provides no error bars, exact dataset statistics, or full experimental protocol (training procedure, hyper-parameters, query sampling), leaving the magnitude and reliability of the gains difficult to assess.

    Authors: The current version indeed omits these details. We will expand the experimental results section to include error bars on all reported recall-QPS figures, exact per-dataset statistics (size, dimensionality, predicate distributions), and a complete protocol subsection specifying training procedure, hyper-parameters, cross-validation folds, and query sampling methodology. revision: yes

  3. Referee: [Query-aware routing framework] The router's selection logic relies on an external offline benchmark table; it is unclear from the description whether this table was constructed independently for the validation datasets or whether any form of data leakage exists between the table construction and the validation queries.

    Authors: The offline table for each validation dataset was built using a disjoint query workload sampled independently from the queries used to evaluate the router. No queries or embeddings from the validation evaluation set were used to populate the table. We will revise the framework description to state this separation explicitly and add a short paragraph confirming the absence of leakage. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation uses independently measured table and held-out validation data

full rationale

The paper's router selects methods via an offline table of measured QPS/recall values plus a regression model trained on six datasets (with three features from ablation) and applied to five unseen validation datasets. No step reduces a reported gain to a quantity defined by the same fitted parameters, no self-citation is load-bearing, and the central claim rests on external measurements plus generalization to held-out data rather than any self-definitional or fitted-input reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the ML regression weights are implicitly fitted but their count and values are not stated.

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discussion (0)

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