Zhenxing Li | Searcharxiv

arXiv2026

Giant Magnetocaloric Effect in a High-Spin Shastry-Sutherland Dipolar Magnet

The Shastry-Sutherland lattice is a prototypical frustrated quantum magnet. It is notable for its exactly solvable dimer-singlet ground state and hosts a wealth of magnetic phenomena under external fields. Here, this work investigates the high-spin (S = 7/2) Eu-based magnet Eu2MgSi2O7 (EMSO) using low-temperature magnetothermal measurements and Monte Carlo simulations, revealing a giant magnetocaloric effect (MCE) in this Shastry-Sutherland compound. The entropy change peak value is found to be 55.0 J kg-1 K-1 under a field change of B = 0-4 T, approximately 1.5 times larger than the commercial Gd3Ga5O12 (GGG). Adiabatic demagnetization refrigeration achieves a lowest temperature of 151 mK, deeply into the sub-Kelvin regime. Furthermore, a distinctive cooling effect persists below about 1 T, a characteristic absent for conventional magnetic coolants. A dipolar Shastry-Sutherland model is introduced as a minimal model to describe this system; in particular, the experimentally revealed 1/3 magnetization pseudo-plateau can be ascribed to the presence of dipolar couplings between Eu2+ ions, further stabilized by the thermal fluctuations, explaining the persistent cooling effect. This work establishes EMSO as a novel platform for exploring the dipolar Shastry-Sutherland system and for sub-Kelvin adiabatic demagnetization refrigeration.

cond-mat.mtrl-sci↗

arXiv2025

EFPC: Towards Efficient and Flexible Prompt Compression

The emergence of large language models (LLMs) like GPT-4 has revolutionized natural language processing (NLP), enabling diverse, complex tasks. However, extensive token counts lead to high computational and financial burdens. To address this, we propose Efficient and Flexible Prompt Compression (EFPC), a novel method unifying task-aware and task-agnostic compression for a favorable accuracy-efficiency trade-off. EFPC uses GPT-4 to generate compressed prompts and integrates them with original prompts for training. During training and inference, we selectively prepend user instructions and compress prompts based on predicted probabilities. EFPC is highly data-efficient, achieving significant performance with minimal data. Compared to the state-of-the-art method LLMLingua-2, EFPC achieves a 4.8% relative improvement in F1-score with 1% additional data at a 4x compression rate, and an 11.4% gain with 10% additional data on the LongBench single-doc QA benchmark. EFPC's unified framework supports broad applicability and enhances performance across various models, tasks, and domains, offering a practical advancement in NLP.

cs.CL↗