Ruigang Li | Searcharxiv

arXiv2026

Excitonic-Superconducting Coexistence and Emergent Nematic Superconductivity Driven by Spontaneous Symmetry Breaking

Excitonic insulating (EI) and superconducting (SC) orders are generally regarded as mutually exclusive electronic instabilities. Within a self-consistent microscopic theory, we study electronic systems hosting an EI phase in the presence of SC pairing and show that an intrinsic mismatch between electron and hole Fermi surfaces fundamentally reshapes this competition. This mismatch stabilizes FFLO-like electron-hole pairing and drives spontaneous symmetry breaking of the EI state. The resulting symmetry breaking reconstructs the pairing phase space for SC and EI state, such that different regions of the Fermi surface complementarily support either EI or SC correlations, leading to a natural coexistence of the two orders. Notably, the emergent SC state consequently breaks rotational symmetry and develops intrinsic nematic superconductivity, even in the absence of explicit symmetry-breaking fields (such as magnetic fields, spin-orbit coupling, or bare band-structure anisotropy). Our results suggest that candidate materials such as monolayer 1T$'$-MoTe$_2$ and the square-net semimetal NaAlSi may provide promising platforms for observing this phenomenon. More broadly, these findings reveal a unique mechanism by which competing many-body orders generate electronic nematicity, suggesting a broader route toward spontaneous anisotropic electronic states in correlated quantum materials.

cond-mat.supr-con↗

arXiv2025

A Lightweight Two-Branch Architecture for Multi-Instrument Transcription via Note-Level Contrastive Clustering

Existing multi-timbre transcription models struggle with generalization beyond pre-trained instruments, rigid source-count constraints, and high computational demands that hinder deployment on low-resource devices. We address these limitations with a lightweight model that extends a timbre-agnostic transcription backbone with a dedicated timbre encoder and performs deep clustering at the note level, enabling joint transcription and dynamic separation of arbitrary instruments given a specified number of instrument classes. Practical optimizations including spectral normalization, dilated convolutions, and contrastive clustering further improve efficiency and robustness. Despite its small size and fast inference, the model achieves competitive performance with heavier baselines in terms of transcription accuracy and separation quality, and shows promising generalization ability, making it highly suitable for real-world deployment in practical and resource-constrained settings.

cs.SD↗