arXiv subjects

Xusheng Wang

Publications and source records attributed to Xusheng Wang.

9 recordsLinked to original sources

arXiv2026

Universal Spectral Mirage Gaps in Superconductors with Time-Reversal-Symmetric Spin-Orbit Coupling

Spectral mirage gaps, regarded as evidence of finite-energy pairing correlations, have so far been mainly studied in superconductors with Ising spin-orbit coupling (SOC). Here, we show that superconductors with any time-reversal-symmetric SOC can generate mirage gaps near the SOC energy scale when the applied magnetic field has a component perpendicular to the SOC texture, whereas the parallel component produces Zeeman-split spectral features near the superconducting gap. We demonstrate this general principle in superconductors with Rashba and Rashba-Ising SOC. These universal field-dependent signatures establish superconducting spectroscopy as a powerful probe of SOC textures and strengths.

cond-mat.supr-con↗

arXiv2026

First-Order Transitions in Weak Ising Spin-Orbit-Coupled Superconductors

Ising spin-orbit coupling (ISOC) can strongly protect superconductivity against exchange-field-induced depairing, typically leading to critical fields far exceeding the Pauli limit and continuous (second-order) phase transitions. Here, using a free-energy approach, we demonstrate that first-order transitions can emerge in superconductors with weak ISOC under large exchange fields. In this regime, conventional theoretical approaches based on the gap equation fail to determine the thermodynamic critical field and instead yield only the supercooling field. Moreover, we identify two pronounced in-gap coherence peaks in the quasiparticle spectra, which represent the weak-ISOC manifestation of the previously reported mirage-gap states. Our results establish the importance of free-energy analysis in describing the first-order phase transitions in Ising superconductors and reveal distinct spectroscopic signatures of the weak-ISOC regime.

cond-mat.supr-con↗

arXiv2026

Hindsight Preference Optimization for Financial Time Series Advisory

Time series models predict numbers; decision-makers need advisory -- directional signals with reasoning, actionable suggestions, and risk management. Training language models for such predictive advisory faces a fundamental challenge: quality depends on outcomes unknown at prediction time. We bridge two ideas from reinforcement learning -- using information unavailable during execution to retrospectively generate training signal, and preference alignment -- and propose Hindsight Preference Optimization: observed outcomes let an LLM judge rank candidate advisories on dimensions that scalar metrics cannot capture, producing preference pairs for DPO without human annotation. We apply this to Vision-Language-Model-based predictive advisories on S&P 500 equity time series, demonstrated by a 4B model outperforming its 235B teacher on both accuracy and advisory quality.

cs.LG↗

arXiv2025

Temperature-Induced Superconductivity Enhancement under Large Exchange Field

Through a comprehensive free energy analysis, we demonstrate that finite temperature can simultaneously weaken superconductivity and mitigate spin polarization induced depairing, leading to potential non-monotonic temperature-dependent behaviors in superconductors subjected to large exchange fields. Remarkably, superconductivity can be counterintuitively enhanced by temperature when the Zeeman energy exceeds the superconducting order parameter, owing to the competition between thermal and magnetic effects. We propose that multiband effect offers one possible microscopic route for this temperature-induced enhancement and demonstrate it explicitly within a two-band superconducting model. A detailed parameter analysis identifies the conditions under which this phenomenon emerges, suggesting that temperature-enhanced superconductivity may be observable in materials such as MgB2 and FeSe through transport and tunneling measurements.

cond-mat.supr-con↗

arXiv2025

RAG Meets Temporal Graphs: Time-Sensitive Modeling and Retrieval for Evolving Knowledge

Knowledge is inherently time-sensitive and continuously evolves over time. Although current Retrieval-Augmented Generation (RAG) systems enrich LLMs with external knowledge, they largely ignore this temporal nature. This raises two challenges for RAG. First, current RAG methods lack effective time-aware representations. Same facts of different time are difficult to distinguish with vector embeddings or conventional knowledge graphs. Second, most RAG evaluations assume a static corpus, leaving a blind spot regarding update costs and retrieval stability as knowledge evolves. To make RAG time-aware, we propose Temporal GraphRAG (TG-RAG), which models external corpora as a bi-level temporal graph consisting of a temporal knowledge graph with timestamped relations and a hierarchical time graph. Multi-granularity temporal summaries are generated for each time node to capture both key events and broader trends at that time. The design supports incremental updates by extracting new temporal facts from the incoming corpus and merging them into the existing graph. The temporal graph explicitly represents identical facts at different times as distinct edges to avoid ambiguity, and the time hierarchy graph allows only generating reports for new leaf time nodes and their ancestors, ensuring effective and efficient updates. During inference, TG-RAG dynamically retrieves a subgraph within the temporal and semantic scope of the query, enabling precise evidence gathering. Moreover, we introduce ECT-QA, a time-sensitive question-answering dataset featuring both specific and abstract queries, along with a comprehensive evaluation protocol designed to assess incremental update capabilities of RAG systems. Extensive experiments show that TG-RAG significantly outperforms existing baselines, demonstrating the effectiveness of our method in handling temporal knowledge and incremental updates.

cs.IR↗

arXiv2025

Benchmarking Chinese Commonsense Reasoning with a Multi-hop Reasoning Perspective

While Large Language Models (LLMs) have demonstrated advanced reasoning capabilities, their comprehensive evaluation in general Chinese-language contexts remains understudied. To bridge this gap, we propose Chinese Commonsense Multi-hop Reasoning (CCMOR), a novel benchmark designed to evaluate LLMs' ability to integrate Chinese-specific factual knowledge with multi-step logical reasoning. Specifically, we first construct a domain-balanced seed set from existing QA datasets, then develop an LLM-powered pipeline to generate multi-hop questions anchored on factual unit chains. To ensure the quality of resulting dataset, we implement a human-in-the-loop verification system, where domain experts systematically validate and refine the generated questions. Using CCMOR, we evaluate state-of-the-art LLMs, demonstrating persistent limitations in LLMs' ability to process long-tail knowledge and execute knowledge-intensive reasoning. Notably, retrieval-augmented generation substantially mitigates these knowledge gaps, yielding significant performance gains.

cs.CL↗

arXiv2025

Unified Description for Reentrance and Tc Enhancement in Ferromagnetic Superconductors

Ferromagnetic superconductors, where ferromagnetism and superconductivity coexist despite their antagonism, exhibit strikingly diverse behaviors. Depending on the interplay between the ferromagnetic exchange field and the superconducting condensate, superconductivity may vary with temperature monotonically, non-monotonically, or even reentrantly, all of which can be tuned by external magnetic fields. Here, we present a unified theoretical framework and construct comprehensive phase diagrams that capture these regimes and predict new phenomena, including doublereentrant superconductivity. We further demonstrate how magnetic fields drive systematic shifts between distinct temperature-dependent behaviors, thereby explaining recent experimental results and predicting new transitions. In addition, we show that the field-induced enhancement of superconductivity, previously observed as a slight increase in Tc, can under certain conditions become dramatic.

cond-mat.supr-con↗

arXiv2024

Experimental discovery of Sarma state in atomically thick superconducting FeSe films under high magnetic fields

Many-body ground states of imbalanced Fermi gas have been studied both theoretically and experimentally for several decades because of their fundamental significance in condensed matter physics, cold atom physics and nuclear physics. The Sarma state, a gapless spin-polarized superfluid, is one of those long sought-after exotic ground states of spin imbalanced Fermi gas. Yet, an unambiguous experimental evidence of Sarma superfluid state has not been found. Here, we report the experimental discovery of the Sarma state in atomically thick FeSe films by a dilution-refrigerator scanning tunneling microscope under high magnetic fields. In the bilayer or trilayer FeSe films, we directly observe the key evidence of the entrance of the Sarma state: the inner Zeeman splitting coherence peaks cross the Fermi level under high in-plane magnetic fields. The angle dependent critical in-plane magnetic field of coherence peak crossing shows a two-fold symmetry due to the anisotropy of the in-plane g-factor of FeSe films. Moreover, in a superconducting FeSe monolayer of a lateral size of several hundred nanometers, the Sarma state can also be induced by strong out-of-plane magnetic fields. Our findings pave the way to explore the unusual physical properties and potential applications in superconducting spintronics of the spin-polarized Sarma superfluid state.

cond-mat.supr-con↗

arXiv2024

CrysToGraph: A Comprehensive Predictive Model for Crystal Materials Properties and the Benchmark

The ionic bonding across the lattice and ordered microscopic structures endow crystals with unique symmetry and determine their macroscopic properties. Unconventional crystals, in particular, exhibit non-traditional lattice structures or possess exotic physical properties, making them intriguing subjects for investigation. Therefore, to accurately predict the physical and chemical properties of crystals, it is crucial to consider long-range orders. While GNN excels at capturing the local environment of atoms in crystals, they often face challenges in effectively capturing longer-ranged interactions due to their limited depth. In this paper, we propose CrysToGraph ($\textbf{Crys}$tals with $\textbf{T}$ransformers $\textbf{o}$n $\textbf{Graph}$s), a novel transformer-based geometric graph network designed specifically for unconventional crystalline systems, and UnconvBench, a comprehensive benchmark to evaluate models' predictive performance on unconventional crystal materials such as defected crystals, low-dimension crystals and MOF. CrysToGraph effectively captures short-range interactions with transformer-based graph convolution blocks as well as long-range interactions with graph-wise transformer blocks. CrysToGraph proofs its effectiveness in modelling unconventional crystal materials in multiple tasks, and moreover, it outperforms most existing methods, achieving new state-of-the-art results on the benchmarks of both unconventional crystals and traditional crystals.

cond-mat.mtrl-sci↗