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Appeared in: 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2026)
Abstract:
The Hypencoder is a recently-proposed retrieval approach that encodes queries as shallow neural networks ("Q-Nets") that estimate relevance over pre-computed document embeddings. Inspired by Matryoshka Representation Learning, we show that the Hypencoder can be extended to support multiple sizes of Q-Nets, allowing trade-offs between effectiveness and efficiency when deployed. We find that this "Matryoshka Hypencoder" achieves comparable in-domain effectiveness with approximately 7x fewer active parameters in-domain and half as many active parameters out-of-domain, which corresponds to a 1.6-3.4x increase in scoring throughput. This work paves the way for practical deployment of Hypencoders.
BibTeX @inproceedings{alkawaas:sigir2026-hypencoder, author = {Alkawaas, Majd and MacAvaney, Sean}, title = {The Matryoshka Hypencoder}, booktitle = {49th International ACM SIGIR Conference on Research and Development in Information Retrieval}, year = {2026}, url = {https://arxiv.org/abs/2607.17457}, doi = {10.1145/3805712.3809980} }