The study was submitted to arXiv on February 19 2026 by Rong Fu and collaborators.
It addresses two key bottlenecks in large‑scale immune repertoire analysis: near‑quadratic pairwise affinity costs and dataset imbalance.
SubQuad combines MinHash prefiltering, a differentiable gating module, GPU‑accelerated affinity kernels, and fairness‑constrained clustering.
The pipeline achieves significant speed‑up and memory savings while preserving accuracy and subgroup equity.
It is positioned as a scalable, bias‑aware tool for vaccine target prioritization and biomarker discovery.
📖 Full Retelling
Rong Fu and six colleagues (Zijian Zhang, Wenxin Zhang, Kun Liu, Jiekai Wu, Xianda Li, Simon Fong) presented their research titled "SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework" on the preprint server arXiv on February 19, 2026. The paper tackles two practical impediments that limit large-scale comparative analyses of adaptive immune repertoires: the near‑quadratic computational cost of pairwise affinity evaluations and the dataset imbalances that hide clinically relevant minority clonotypes. By introducing the SubQuad pipeline, the authors propose a joint solution that blends antigen‑aware sub‑quadratic retrieval, GPU‑accelerated affinity kernels, learned multimodal fusion, and fairness‑constrained clustering. The system incorporates compact MinHash prefiltering, a differentiable gating module for adaptive weighting of alignment and embedding channels, and an automated calibration routine that enforces proportional representation of rare antigen‑specific subgroups. Experiments on extensive viral and tumor repertoires demonstrate that SubQuad improves throughput and peak memory usage while maintaining or enhancing recall@k, cluster purity, and subgroup equity. The authors argue that this co‑design of indexing, similarity fusion, and equity‑aware objectives yields a scalable, bias‑aware platform useful for downstream translational tasks such as vaccine target prioritization and biomarker discovery.
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Deep Analysis
Why It Matters
SubQuad tackles the near-quadratic cost of pairwise affinity evaluations and dataset imbalances that obscure minority clonotypes, enabling faster and fairer immune repertoire analysis for vaccine target prioritization and biomarker discovery.
Context & Background
Pairwise affinity evaluations in immune repertoire studies are computationally expensive, scaling near-quadratically with dataset size.
Imbalanced datasets hide clinically important minority clonotypes, limiting the effectiveness of downstream translational tasks.
The SubQuad framework is expected to be adopted by researchers conducting large viral and tumor repertoire studies, integrated into vaccine design pipelines, and serve as a foundation for future equity-aware machine learning models in immunology.
Frequently Asked Questions
What is SubQuad?
SubQuad is an end-to-end pipeline that combines near-subquadratic retrieval, GPU-accelerated affinity kernels, multimodal fusion, and fairness-constrained clustering to efficiently analyze adaptive immune repertoires.
How does SubQuad improve performance and fairness?
It uses MinHash prefiltering to reduce candidate comparisons, a differentiable gating module to adaptively weight alignment and embedding channels, and a calibration routine that enforces proportional representation of rare subgroups, resulting in higher throughput, lower memory usage, and improved recall, cluster purity, and subgroup equity.
Original Source
--> Computer Science > Machine Learning arXiv:2602.17330 [Submitted on 19 Feb 2026] Title: SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework Authors: Rong Fu , Zijian Zhang , Wenxin Zhang , Kun Liu , Jiekai Wu , Xianda Li , Simon Fong View a PDF of the paper titled SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework, by Rong Fu and 6 other authors View PDF HTML Abstract: Comparative analysis of adaptive immune repertoires at population scale is hampered by two practical bottlenecks: the near-quadratic cost of pairwise affinity evaluations and dataset imbalances that obscure clinically important minority clonotypes. We introduce SubQuad, an end-to-end pipeline that addresses these challenges by combining antigen-aware, near-subquadratic retrieval with GPU-accelerated affinity kernels, learned multimodal fusion, and fairness-constrained clustering. The system employs compact MinHash prefiltering to sharply reduce candidate comparisons, a differentiable gating module that adaptively weights complementary alignment and embedding channels on a per-pair basis, and an automated calibration routine that enforces proportional representation of rare antigen-specific subgroups. On large viral and tumor repertoires SubQuad achieves measured gains in throughput and peak memory usage while preserving or improving recall@k, cluster purity, and subgroup equity. By co-designing indexing, similarity fusion, and equity-aware objectives, SubQuad offers a scalable, bias-aware platform for repertoire mining and downstream translational tasks such as vaccine target prioritization and biomarker discovery. Comments: 27 pages, 9 figures Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.17330 [cs.LG] (or arXiv:2602.17330v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.17330 Focus to learn more arXiv-issued ...
