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SpecMRL: Redesign Matryoshka Representation Learning within Parametric Spectrum

Paper recorded by Signals 4 on 2026-10-19 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-19 on arXiv · recorded by Signals 4 on 2026-10-20

Category: cs.CL · 自然语言处理 · first seen 2026-10-20

Abstract

Matryoshka Representation Learning (MRL) is designed to support flexible representation sizes by jointly optimizing nested prefixes. While effective, this design lacks an explicit constraint to ensure that the leading portion provides a suitable compact encoding. To address this, we propose SpecMRL, which reorganizes the feature space to enable more effective prefix readout. Specifically, we exten

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#211 most recent of 660 cs.CL papers we have recorded · ↑ newer: KEEP-Bench: Evaluating Evidence Preservation in Context Compression · ↓ older: ASTOR: Benchmarking and Improving Autoformulation for Soft-Constraint
Cite this page: SpecMRL: Redesign Matryoshka Representation Learning within Parametric Spectrum: the #211 most recent of 660 cs.CL papers we have recorded (as of 2026-10-19). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/specmrl-redesign-matryoshka-representation-learning-within-parametric-spectrum.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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