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Yiyan Wang

Publications and source records attributed to Yiyan Wang.

At least 19 recordsLinked to original sources
arXiv2026

Gaussians Can Subtract: Neural Dissolve Fields for Hybrid Scene Rendering

Hybrid Mesh-Gaussian rendering combines compact textured surfaces with flexible 3D Gaussian primitives, offering an efficient alternative to purely Gaussian scene representations for structured environments. Existing scene-reconstruction hybrids commonly follow an additive formulation, where Gaussian residuals compensate for errors left by the mesh. However, not every Mesh-Gaussian conflict is caused by missing content: residual geometry errors, occlusion mismatch, texture-projection artifacts, and view-dependent appearance can make the same mesh content consistent in one view but inconsistent in another. We therefore formulate hybrid rendering as a multi-view inconsistency problem and present Gaussolve, a subtract-and-refill framework that selectively dissolves inconsistent mesh content before residual Gaussians refill the revealed regions. We introduce a lightweight Neural Dissolve Field, parameterized by scalar spherical-harmonic Gaussians, to learn where and from which viewing directions mesh content should be dissolved. A continuous transmittance fusion rule progressively exposes residual Gaussians behind the dissolved mesh, preserving their reconstruction gradients and avoiding hard visibility discontinuities. Experiments on structured and semi-structured scenes demonstrate higher visual fidelity with fewer Gaussian primitives and faster rendering. Project page: https://ooosn.github.io/gaussians-can-subtract/

cs.CV↗
arXiv2026

MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement

Reinforcement learning (RL) is the central training paradigm for advancing large foundation models towards self-improvement. This report introduces the MiMo-V2.6 series, an omni-modal family that pushes the frontier of model intelligence by scaling RL compute. Prior to RL, we conduct mid-training on a broad multimodal corpus to provide ample exploration space, and build a solid infrastructure on the pretrained hybrid-SWA architecture to support subsequent scale-up. We scale RL compute along three dimensions: (1) larger batches and higher throughput, with an asynchronous training that consumes 1,568 samples and 2.7-3.7B tokens per step at context lengths of up to 1M; (2) more diverse and complex environments, spanning code, general, visual, and cyber domains under a mixture of agent harnesses; and (3) more grader compute, via groupwise agentic grading that yields more accurate reward signals for long-horizon tasks and steers the model towards shorter, more token-efficient solutions. To keep training stable at scale, we freeze the MoE router and establish a multi-layer defense against reward hacking. We further build infrastructure for mixed-task agentic RL, including a unified trajectory representation, high-concurrency multi-framework rollout, decoupled control and data planes, and training-inference consistency. We open-source the training dynamics, RL environments, and RL framework to facilitate reproduction and further research on scaled RL and model self-improvement.

cs.CL↗
arXiv2026

Real-axis least-squares bath discretization for real-time transport in quantum-dot Josephson junctions

Efficient real-time methods are essential for resolving transient coherence and nonlinear transport in driven superconducting nanostructures. Such simulations remain challenging because finite representations of the BCS continuum generate recurrences, while the damping used to suppress them smears gap-edge structure and artificially shortens coherent lifetimes; finite-broadening regularization of the gap-edge singularity likewise imposes a lifetime-resolution floor. Here we extend the driven Liouville--von Neumann approach to superconducting reservoirs---a noninteracting quantum dot with Bogoliubov--de Gennes leads---building an explicit-Hamiltonian framework that hosts a family of bath discretizations (uniform and Gaussian baselines and a new real-axis least-squares recipe, with mode energies, spectral weights, and mode-resolved dampings optimized jointly under gap-aware constraints)---making the broadening an independently calibrated numerical parameter whose leading long-time effect extrapolates controllably along the joint ($γ\to0$, $N_b\to\infty$) refinement path while cutting computational cost by over an order of magnitude versus the high-resolution DLvN reference. The compressed baths recover established current-phase relations, multiple-Andreev-reflection structure, quasiparticle-trapping oscillations, and integer and fractional Shapiro locking; the lifetime analysis yields the parameter-free leading-order relation $κ\simeq w_{\mathrm{bath}}γ_{\mathrm{eff}}$ and resolves damping-induced decay rates below the resolution scale of a previous finite-broadening calculation. As an explicit Hamiltonian reservoir representation, the framework generalizes naturally to multiterminal normal--superconducting geometries and offers a route toward interacting impurity solvers, while finite-gap strong-drive locking amplitudes mark its present quantitative boundary.

cond-mat.mes-hall↗
arXiv2026

A Data-Driven Multimodal Method for Early Detection of Coordinated Abnormal Behaviors in Live-Streaming Platforms

With the rapid growth of live-streaming e-commerce and digital marketing, abnormal marketing behaviors have become increasingly concealed and coordinated across heterogeneous modalities, challenging platform governance and early risk identification. We propose MM-FGDNet, a data-driven multimodal framework for detecting abnormal behavior in large-scale live-streaming environments from complementary temporal-evolution and group-structure perspectives. A cross-modal temporal alignment module maps video, text, audio, and user behavior into a unified temporal semantic space. A temporal fraud-pattern module captures the progression from weak early signals to abrupt outbreaks, while a cooperative manipulation module identifies coordinated interactions among organized user groups and automated accounts. Experiments on real-world multi-platform live-streaming e-commerce datasets show that MM-FGDNet outperforms representative baselines, achieving an AUC of 0.927, F1 of 0.847, precision of 0.861, recall of 0.834, and an Early Detection Score of 0.689, while reducing false alarms. Ablation studies validate the contribution of each module, and cross-domain experiments demonstrate stable generalization to new streamers, product categories, and platforms. These results indicate that MM-FGDNet provides an effective and scalable solution for proactive detection of coordinated abnormal behavior in live-streaming systems.

cs.SI↗
arXiv2026

Emergence of Double-Dome Superconductivity in the Pressurized Dirac Semimetal BaMg2Bi2

Dirac semimetal BaMg2Bi2 is reported to be a unique topological material that manifests surface superconductivity that coexistswith bulk band topology at ambient pressure. Here, we present a comprehensive investigation of high-pressure superconductingproperties in BaMg2Bi2 single crystal. Significantly, a pressure-driven double-dome superconducting behavior was revealed, withthe superconducting transition temperature Tc approaching the maximum values of 6.67 K at 4.5 GPa and 7.22 K at 10.4 GPafor the first and second superconducting domes, respectively. The combination of high-pressure X-ray diffraction, Hall resistivitymeasurements, and theoretical calculations demonstrates that, the first superconducting regime is closely related to the pressure-modulated Lifshitz transition, whereas the second superconducting phase emerges concurrently with a structural transition fromthe ambient-pressure P3m1 phase to a high-pressure Pnma phase.

cond-mat.supr-con↗
arXiv2026

Unsupervised Single-Channel Speech Separation with Diffusion under Speaker-Embedding Guidance

Speech separation is a fundamental task in audio processing, typically addressed with fully supervised systems trained on paired mixtures. While effective, such systems typically rely on synthetic data pipelines, which may not reflect real-world conditions. Instead, we revisit the source-model paradigm, training a diffusion generative model solely on anechoic speech and formulating separation as a diffusion inverse problem. However, unconditional diffusion models lack speaker-level conditioning, they can capture local acoustic structure but produce temporally inconsistent speaker identities in separated sources. To address this limitation, we propose Speaker-Embedding guidance that, during the reverse diffusion process, maintains speaker coherence within each separated track while driving embeddings of different speakers further apart. In addition, we propose a new separation-oriented solver tailored for speech separation, and both strategies effectively enhance performance on the challenging task of unsupervised source-model-based speech separation, as confirmed by extensive experimental results. Audio samples and code are available at https://runwushi.github.io/UnSepDiff_demo.

eess.AS↗
arXiv2026

Exceptional Points in a Parallel Double-Quantum-Dot Josephson Junction Coupled to a Ferromagnetic Reservoir

We investigate exceptional points (EPs) in a parallel double-quantum-dot Josephson junction coupled to a dissipative reservoir. By integrating out the leads, we obtain a non-Hermitian Bogoliubov-de Gennes description wherein the superconducting phase difference and orbital flux govern the complex Andreev spectrum. For spin-independent dissipation, the second-order EPs identified within the infinite superconducting gap limit are eliminated when the finite superconducting gap is properly incorporated. In contrast, spin-dependent dissipation originating from a ferromagnetic reservoir, in conjunction with magnetic flux, gives rise to second-order EPs that persist in superconducting leads with finite gap. Moreover, flux tuning enables the coalescence of two second-order EPs into a third-order EP, whose eigenvalue splitting exhibits cubic-root scaling behavior. A many-body parity analysis establishes the connection between the contrasting finite-gap behavior and the spectral relationship between the even- and odd-parity sectors. Finally, Josephson currents calculated from both the free-energy derivative and the surrogate-model density matrix demonstrate consistency and remain continuous across the EPs. These findings establish spin-selective dissipation and interferometric flux as effective control parameters for robust non-Hermitian singularities in superconducting nanostructures.

cond-mat.mes-hall↗
arXiv2026

EntSQL: A Benchmark for Grounding Text-to-SQL in Long-Context Enterprise Knowledge

Text-to-SQL enables natural language access to databases, and recent LLMs have substantially advanced its capabilities. Existing benchmarks such as Spider, BIRD, and Spider~2.0 evaluate schema generalization, large-scale databases, and realistic workflows, but largely overlook enterprise scenarios where SQL generation depends on private business knowledge, such as internal metrics, reporting conventions, and organizational rules. We introduce EntSQL, an enterprise-oriented Text-to-SQL benchmark for evaluating long-context grounding over proprietary business documents. EntSQL contains 1,066 aligned Chinese-English semantic examples across five business domains, with most examples requiring domain knowledge beyond the question and schema and involving complex SQL structures. On English inputs, the best evaluated system reaches only 15.9\% when long-form documents are provided, highlighting the difficulty of grounding SQL generation in enterprise knowledge.

cs.CL↗
arXiv2026

MatPhys: Learning Material-Aware Physics Parameters for Deformable Object Simulation from Videos

Reconstructing simulation-ready deformable objects is important for vision, graphics, and robotics. Existing physics-driven methods can recover physical digital twins from videos, but they suffer from two fundamental limitations: they typically assume a homogeneous material across the whole object, and their scene-specific inverse optimization, combined with the inherent ambiguity of monocular observation, yields inconsistent parameters for the same material across different scenes or interactions. We propose MatPhys, a material-aware feed-forward framework that predicts spring-mass parameters from a single-view video, addressing these two issues with two coupled designs. To relax the homogeneous material assumption, we use DINO features to decompose the object into semantically meaningful parts and to query a part-level material prior, assigning each part its own physical behavior. To enforce cross-scene consistency, we introduce a learned material codebook of shared material embeddings as the bridge between appearance and physics, and further use the part-level prior as a reference distribution that constrains the decoder so that the same material yields consistent parameters across scenes and interactions. Together, these designs turn an under-constrained monocular problem into feed-forward inference grounded on shared, reusable material concepts. Experiments show that our method matches per-scene optimization baselines in reconstruction and future prediction, while achieving stronger generalization to unseen interactions and objects with more consistent physical parameters.

cs.CV↗
arXiv2026

Can Large Language Models be a Cardinality Estimator? An Empirical study

Cardinality estimation (CardEst) still remains a challenging problem for DBMS. Recent years have witnessed the success of ML-based cardinality estimators in outperforming traditional methods. However, these solutions suffer from poor generalizability to new data or query distribution, inability to handle complex queries, and substantial data preparation overhead, thus preventing their wide adoption in the real-world DBMS. Some recent efforts have been dedicated to addressing some but not all of these issues. We notice that the recent emerging Large Language Models (LLMs) have shown their remarkable generalizability to unseen tasks, capabilities to understand complex programs, and power to perform data-efficient fine-tuning. In light of this, we propose to leverage LLMs to mitigate the above issues. Specifically, we carefully craft prompts, and subsequently perform fine-tuning and self-correction during inference with LLMs for CardEst task. We then extensively evaluate LLMs' in-distribution and out-of-distribution generalizability, feasibility to support complex queries, and training data efficiency during fine-tuning LLMs on pre-training datasets. The results suggest that LLMs outperform the state-of-the-art in almost all settings, thus indicating their potential for the CardEst task. We further measure the end-to-end query execution time in DBMS by using the estimated cardinalities of LLMs in some practical settings, which suggests that the inference overhead of LLMs can be outweighed by the benefits brought by LLMs for CardEst.

cs.DB↗
arXiv2025

Large Language Models' Complicit Responses to Illicit Instructions across Socio-Legal Contexts

Large language models (LLMs) are now deployed at unprecedented scale, assisting millions of users in daily tasks. However, the risk of these models assisting unlawful activities remains underexplored. In this study, we define this high-risk behavior as complicit facilitation - the provision of guidance or support that enables illicit user instructions - and present four empirical studies that assess its prevalence in widely deployed LLMs. Using real-world legal cases and established legal frameworks, we construct an evaluation benchmark spanning 269 illicit scenarios and 50 illicit intents to assess LLMs' complicit facilitation behavior. Our findings reveal widespread LLM susceptibility to complicit facilitation, with GPT-4o providing illicit assistance in nearly half of tested cases. Moreover, LLMs exhibit deficient performance in delivering credible legal warnings and positive guidance. Further analysis uncovers substantial safety variation across socio-legal contexts. On the legal side, we observe heightened complicity for crimes against societal interests, non-extreme but frequently occurring violations, and malicious intents driven by subjective motives or deceptive justifications. On the social side, we identify demographic disparities that reveal concerning complicit patterns towards marginalized and disadvantaged groups, with older adults, racial minorities, and individuals in lower-prestige occupations disproportionately more likely to receive unlawful guidance. Analysis of model reasoning traces suggests that model-perceived stereotypes, characterized along warmth and competence, are associated with the model's complicit behavior. Finally, we demonstrate that existing safety alignment strategies are insufficient and may even exacerbate complicit behavior.

cs.CY↗
arXiv2025

Magnetic flux controlled current phase relationship in double Quantum Dot Josephson junction

In this work, we study a Josephson junction with parallel-connected quantum dots (QDs) threaded by a magnetic flux in the central region. We discretize the superconducting (SC) electrode into three discrete energy levels and modify the tunneling coefficients to construct a finite-dimensional surrogate Hamiltonian. By directly diagonalizing this Hamiltonian, we compute the physical quantities of the system. Additionally, we employ a low-energy effective model to gain deeper physical insight. Our findings reveal that when only one QD exhibits Coulomb interaction, the system undergoes a phase transition between singlet and doublet states. The magnetic flux has a minor influence on the singlet state but significantly affects the doublet state. When both QDs have interactions, the system undergoes two phase transitions as the SC phase difference increases: the ground state evolves from a doublet to a singlet and finally into a triplet state at $ϕ= π$. Increasing the magnetic flux suppresses the doublet and triplet phases, eventually stabilizing the singlet state. In this regime, enhancing the interaction strength does not induce a singlet-doublet transition but instead drives a transition between upper and lower singlet states, leading to a critical current peak as $U$ increases. Finally, we examine the case where the tunneling coefficient $Γ$ exceeds the SC pairing potential $Δ$. Here, doublet states dominate, and the system only exhibits a phase transition between doublet and triplet states when $ϕ_B = 0$. In the presence of a magnetic flux, the three states converge, resulting in a triple point in the ($ϕ$, $ϕ_B$) parameter space.

cond-mat.mes-hall↗
arXiv2025

Quantum phase transition in a double quantum dot Josephson junction driven by electron-electron interactions

In this work, we employ a surrogate BCS model with discrete energy levels to investigate a hybrid system comprising two quantum dots (QD1 and QD2), where QD1 is tunnel-coupled to two superconducting leads. Through exact diagonalization of this system, we obtain numerically exact solutions that enable rigorous computation of key physical quantities. Our analysis reveals a rich phase diagram featuring multiple controllable phase transitions mediated by quantum dot interactions. Specifically, the system first undergoes an initial phase transition when tuning QD2's interaction strength while maintaining QD1 in the non-interacting regime. Subsequent adjustment of QD1's interaction induces a secondary phase transition, followed by a third transition arising from inter-dot coupling modulation. Furthermore, we demonstrate that parallel magnetic field application can drive reversible ferromagnetic-antiferromagnetic phase transitions under specific parameter conditions. Finally, we report the emergence of non-local magnetization phenomena when subjecting QD1 to weak magnetic fields. And our results demonstrate that the orientation of nonlocal magnetization can be precisely manipulated through systematic adjustment of the on-site interaction strength $U_2$ in QD2.

cond-mat.mes-hall↗
arXiv2024

UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence

Garment manipulation (e.g., unfolding, folding and hanging clothes) is essential for future robots to accomplish home-assistant tasks, while highly challenging due to the diversity of garment configurations, geometries and deformations. Although able to manipulate similar shaped garments in a certain task, previous works mostly have to design different policies for different tasks, could not generalize to garments with diverse geometries, and often rely heavily on human-annotated data. In this paper, we leverage the property that, garments in a certain category have similar structures, and then learn the topological dense (point-level) visual correspondence among garments in the category level with different deformations in the self-supervised manner. The topological correspondence can be easily adapted to the functional correspondence to guide the manipulation policies for various downstream tasks, within only one or few-shot demonstrations. Experiments over garments in 3 different categories on 3 representative tasks in diverse scenarios, using one or two arms, taking one or more steps, inputting flat or messy garments, demonstrate the effectiveness of our proposed method. Project page: https://warshallrho.github.io/unigarmentmanip.

cs.CV↗
arXiv2023

Anisotropic in-plane heat transport of Kitaev magnet Na$_2$Co$_2$TeO$_6$

We report a study on low-temperature heat transport of Kitaev magnet Na$_2$Co$_2$TeO$_6$, with the heat current and magnetic fields along the honeycomb spin layer (the $ab$ plane). The zero-field thermal conductivity of $κ^a_{xx}$ and $κ^{a*}_{xx}$ display similar temperature dependence and small difference in their magnitudes; whereas, their magnetic field (parallel to the heat current) dependence are quite different and are related to the field-induced magnetic transitions. The $κ^a_{xx}(B)$ data for $B \parallel a$ at very low temperatures have an anomaly at 10.25--10.5 T, which reveals an unexplored magnetic transition. The planar thermal Hall conductivity $κ^a_{xy}$ and $κ^{a*}_{xy}$ show very weak signals at low fields and rather large values with sign change at high fields. This may point to a possible magnetic structure transition or the change of the magnon band topology that induces a radical change of magnon Berry curvature distribution before entering the spin polarized state. These results put clear constraints on the high-field phase and the theoretical models for Na$_2$Co$_2$TeO$_6$.

cond-mat.str-el↗
arXiv2022

Reasoning about Dependence, Preference and Coalitional Power

This paper presents a logic of preference and functional dependence (LPFD) and its hybrid extension (HLPFD), both of whose sound and strongly complete axiomatization are provided. The decidability of LPFD is also proved. The application of LPFD and HLPFD to modelling cooperative games in strategic and coalitional forms is explored. The resulted framework provides a unified view on Nash equilibrium, Pareto optimality and the core. The philosophical relevance of these game-theoretical notions to discussions of collective agency is made explicit. Some key connections with other logics are also revealed, for example, the coalition logic, the logic functional dependence and the logic of ceteris paribus preference.

cs.GT↗
arXiv2022

Electronic structure and open-orbit Fermi surface topology in isostructural semimetals NbAs$_2$ and W$_2$As$_3$ with extremely large magnetoresistance

In transition-metal dipnictides $TmPn_2$ ($Tm$ = Ta, Nb; $Pn$ = P, As, Sb), the origin of extremely large magnetoresistance (XMR) is yet to be studied by the direct visualization of the experimental band structures. Here, using angle-resolved photoemission spectroscopy, we map out the three-dimensional electronic structure of NbAs$_2$. The open-orbit topology contributes to a non-negligible part of the Fermi surfaces (FSs), like that of the isostructural compound MoAs$_2$, where the open FS is proposed to likely explain the origin of XMR. We further demonstrate the observation of open characters in the overall FSs of W$_2$As$_3$, which is also a XMR semimetal with the same space group of $C$12/$m$1 as $TmPn_2$ family and MoAs$_2$. Our results suggest that the open-orbit FS topology may be a shared feature between XMR materials with the space group of $C$12/$m$1, and thus could possibly play a role in determining the corresponding XMR effect together with the electron-hole compensation.

cond-mat.mtrl-sci↗
arXiv2019

Topological phase transition between distinctWeyl semimetal states in MoTe2

We present experimental evidence of an intriguing phase transition between distinct topological states in the type-II Weyl semimetal MoTe2. We observe anomalies in the Raman phonon frequencies and linewidths as well as electronic quasielastic peaks around 70 K, which, together with structural, thermodynamic measurements, and electron-phonon coupling calculations, demonstrate a temperature-induced transition between two topological phases previously identified by contrasting spectroscopic measurements. An analysis of experimental data suggests electron-phonon coupling as the main driving mechanism for the change of key topological characters in the electronic structure of MoTe2.We also find the phase transition to be sensitive to sample conditions distinguished by synthesis methods. These discoveries of temperature and material condition-dependent topological phase evolutions and transitions in MoTe2 advance the fundamental understanding of the underlying physics and enable an effective approach to tuning Weyl semimetal states for technological applications.

cond-mat.mtrl-sci↗