# SwiftRepertoire: Few-Shot Immune-Signature Synthesis via Dynamic Kernel Codes

arXiv: https://arxiv.org/abs/2602.01051 · HTML: https://mlsanity.com/paper/2602.01051/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 1 Feb 2026.

## Builds on (most cited first, 7 of 7)

- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification](https://mlsanity.com/paper/2207.09519/) (2022)
- [BiFormer: Vision Transformer with Bi-Level Routing Attention](https://mlsanity.com/paper/2303.08810/) (2023)
- [ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling](https://mlsanity.com/paper/2402.10635/) (2024)
- [Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning](https://mlsanity.com/paper/2310.11670/) (2023)
- [PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction](https://mlsanity.com/paper/2310.00614/) (2023)
- [Multiple Instance Neural Networks Based on Sparse Attention for Cancer Detection using T-cell Receptor Sequences](https://mlsanity.com/paper/2208.04524/) (2022)

## Abstract (arXiv)

Repertoire-level analysis of T cell receptors offers a biologically grounded signal for disease detection and immune monitoring, yet practical deployment is impeded by label sparsity, cohort heterogeneity, and the computational burden of adapting large encoders to new tasks. We introduce a framework that synthesizes compact task-specific parameterizations from a learned dictionary of prototypes conditioned on lightweight task descriptors derived from repertoire probes and pooled embedding statistics. This synthesis produces small adapter modules applied to a frozen pretrained backbone, enabling immediate adaptation to novel tasks with only a handful of support examples and without full model fine-tuning. The architecture preserves interpretability through motif-aware probes and a calibrated motif discovery pipeline that links predictive decisions to sequence-level signals. Together, these components yield a practical, sample-efficient, and interpretable pathway for translating repertoire-informed models into diverse clinical and research settings where labeled data are scarce and computational resources are constrained.