arXiv subjects

Xue Sun

Publications and source records attributed to Xue Sun.

7 recordsLinked to original sources

arXiv2026

Interplay of spin-orbit coupling, crystal field splitting and correlations: a ghost rotationally invariant slave boson treatment

We investigate the interplay of spin-orbit coupling, crystal field splittings, and electronic correlations in the $t_{2g}$ Hubbard-Kanamori model within the recently formulated ghost rotationally invariant slave-boson method (GRISB). In particular, we study a tight binding model of Sr$_2$RuO$_4$ with parameters extracted from density functional theory and linearized quasiparticle self-consistent GW (LQSGW) calculations; we study the behavior of different physical quantities as the number of ghosts increases to examine the convergence of GRISB to dynamical mean field theory (DMFT) and experimental results; and we leverage the ability of GRISB to investigate the model over a wide range of parameters at low temperature. In particular, we examine both static and dynamical observables driven by the spin orbit coupling (SOC) and study how they vary as a function of the Hubbard $U$ and Hund's coupling $J$. GRISB converges quickly for most of these observables, and the calculations reveal the following: $U$ enhances the spin-orbit coupling while $J$ suppresses it. We also study the shape of the Fermi surface within different methodologies, and examine the Lifshitz transition which takes place as a function of strain in this material.

cond-mat.str-el↗

arXiv2025

FlagEval Findings Report: A Preliminary Evaluation of Large Reasoning Models on Automatically Verifiable Textual and Visual Questions

We conduct a moderate-scale contamination-free (to some extent) evaluation of current large reasoning models (LRMs) with some preliminary findings. We also release ROME, our evaluation benchmark for vision language models intended to test reasoning from visual clues. We attach links to the benchmark, evaluation data, and other updates on this website: https://flageval-baai.github.io/LRM-Eval/

cs.CL↗

arXiv2025

RoboBrain 2.0 Technical Report

We introduce RoboBrain 2.0, our latest generation of embodied vision-language foundation models, designed to unify perception, reasoning, and planning for complex embodied tasks in physical environments. It comes in two variants: a lightweight 7B model and a full-scale 32B model, featuring a heterogeneous architecture with a vision encoder and a language model. Despite its compact size, RoboBrain 2.0 achieves strong performance across a wide spectrum of embodied reasoning tasks. On both spatial and temporal benchmarks, the 32B variant achieves leading results, surpassing prior open-source and proprietary models. In particular, it supports key real-world embodied AI capabilities, including spatial understanding (e.g., affordance prediction, spatial referring, trajectory forecasting) and temporal decision-making (e.g., closed-loop interaction, multi-agent long-horizon planning, and scene graph updating). This report details the model architecture, data construction, multi-stage training strategies, infrastructure and practical applications. We hope RoboBrain 2.0 advances embodied AI research and serves as a practical step toward building generalist embodied agents. The code, checkpoint and benchmark are available at https://superrobobrain.github.io.

cs.RO↗

arXiv2024

Charge self-consistent density functional theory plus ghost rotationally-invariant slave-boson theory for correlated materials

We present a charge self-consistent density functional theory combined with the ghost-rotationally-invariant slave-boson (DFT+gRISB) formalism for studying correlated materials. This method is applied to SrVO$_3$ and NiO, representing prototypical correlated metals and charge-transfer insulators. For SrVO$_3$, we demonstrate that DFT+gRISB yields an accurate equilibrium volume and effective mass close to experimentally observed values. Regarding NiO, DFT+gRISB enables the simultaneous description of charge transfer and Mott-Hubbard bands, significantly enhancing the accuracy of the original DFT+RISB approach. Furthermore, the calculated equilibrium volume and spectral function reasonably agree with experimental observations.

cond-mat.str-el↗

arXiv2022

A Modeling of TSRCG and Resource Optimization for Multi-task Delivery Guarantee Algorithm Based on CGR Strategy in LEO Satellite Network

With the reduction of satellite costs and the enhancement of processing capabilities, low earth orbit (LEO) satellite constellations can independently build inter-satellite networks without relying on traditional ground stations restricted by geographical distribution and can establish inter-satellite links (ISLs) and complete computing and routing on-board. The characteristics of frequent on-off ISLs, the highly dynamic network topology of satellite networks make it face the challenges of routing strategy design as a delay/interruption tolerant network (DTN). As a deterministic dynamic routing algorithm, contact graph routing (CGR) uses a contact plan to calculate the path and forward data, but it still has problems such as high computational overhead, low prediction accuracy caused by ignoring queue delay, and overbooked problem caused by limited cache. Therefore, we first start with the time-space resource contact graph (TSRCG) to accurately characterize the time-varying and predictable characteristics of the satellite network and the network resource parameters under multi-tasks. Then, we optimize the route-list computation and dynamic route computation process to ensure task delivery and reduce the consumption of various resources, such as contact capacity, computing resources, and storage resources. And the resource optimization for the multi-task delivery guarantee algorithm based on CGR (RMDG-CGR) strategy we propose is compared with standard CGR in ION 4.0.1. Finally, the simulation results show that the RMDG-CGR can achieve higher task delivery in advance and successful task delivery rate, save contact volume occupancy rate, computing and storage resource, and the above effects are more prominent, especially in the task scenario with critical bundles.

cs.NI↗

arXiv2022

Reinforcement Learning for Cognitive Delay/Disruption Tolerant Network Node Management in an LEO-based Satellite Constellation

In recent years, with the large-scale deployment of space spacecraft entities and the increase of satellite onboard capabilities, delay/disruption tolerant network (DTN) emerged as a more robust communication protocol than TCP/IP in the case of excessive network dynamics. DTN node buffer management is still an active area of research, as the current implementation of the DTN core protocol still relies on the assumption that there is always enough memory available in different network nodes to store and forward bundles. In addition, the classical queuing theory does not apply to the dynamic management of DTN node buffers. Therefore, this paper proposes a centralized approach to automatically manage cognitive DTN nodes in low earth orbit (LEO) satellite constellation scenarios based on the advanced reinforcement learning (RL) strategy advantage actor-critic (A2C). The method aims to explore training a geosynchronous earth orbit intelligent agent to manage all DTN nodes in an LEO satellite constellation scenario. The goal of the A2C agent is to maximize delivery success rate and minimize network resource consumption cost while considering node memory utilization. The intelligent agent can dynamically adjust the radio data rate and perform drop operations based on bundle priority. In order to measure the effectiveness of applying A2C technology to DTN node management issues in LEO satellite constellation scenarios, this paper compares the trained intelligent agent strategy with the other two non-RL policies, including random and standard policies. Experiments show that the A2C strategy balances delivery success rate and cost, and provides the highest reward and the lowest node memory utilization.

cs.AI↗

arXiv2019

Relativistic stars in mass-varying massive gravity

Mass-varying massive gravity allows the graviton mass to vary according to different environments. We investigate neutron star and white dwarf solutions in this theory and find that the graviton mass can become very large near the compact stars and settle down quickly to small cosmological values away the stars, similar to that of black holes in the theory. It is found that there exists a tower of compact star solutions where the graviton mass decreases radially to zero non-trivially. We compute the massive graviton effects on the mass-radius relations of the compact stars, and also compare the relative strengths between neutron stars and white dwarfs in constraining the parameter space of mass-varying massive gravity.

gr-qc↗