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Meng Yang

Publications and source records attributed to Meng Yang.

At least 19 recordsLinked to original sources
arXiv2026

Macroscopic Poincaré Inequalities and Weak-Type Characterizations of $p$-Energy Forms

For a class of regular strongly local $p$-energy forms on complete doubling metric measure spaces, $1<p<\infty$, we derive a macroscopic Poincaré inequality from a Poincaré inequality and a capacity upper bound at a doubling scale $Ψ$. The macroscopic inequality compares $L^p$-oscillations at any two scales and yields a uniform two-scale comparison $J_s\lesssim J_r$, $0<r<s$, for the Korevaar-Schoen functionals, strengthening their usual weak monotonicity property. Conversely, under a density assumption on compactly supported functions, the macroscopic inequality yields a regular strongly local Korevaar-Schoen $p$-energy satisfying a $Ψ$-Poincaré inequality. We also show that the macroscopic inequality forces lower $p$-scaling of $Ψ$ on scales not exceeding $\mathrm{diam} X$; together with doubling, this gives two-sided power bounds. Finally, we establish a weak-type characterization of the energy domain of Brezis-Van Schaftingen-Yung type.

math.MG↗
arXiv2026

SpatialOPSD: Self-Distilling Spatial Intelligence from Verified Coding Agent Traces

Spatial coding agents significantly improve spatial reasoning in Multimodal Large Language Models (MLLMs) by using external tools to generate verified execution traces. However, this paradigm inherently suffers from prohibitive inference-time overhead and external dependencies. In this paper, we explore whether an MLLM can internalize this agentic capability to operate entirely tool-free. We begin with a simple observation: prompting an MLLM with summarized execution traces of a spatial coding agent naturally unlocks the model's internal spatial Chain-of-Thought (CoT). Motivated by this, we introduce SpatialOPSD, an on-policy self-distillation framework that internalizes spatial reasoning into a standalone MLLM by formulating verified agent traces as privileged information. To mitigate privileged-information leakage during distillation, we introduce Repetition-Aware Distillation, which combines repetition masking with unlikelihood regularization. Experiments across multiple benchmarks demonstrate that self-distilling SpatialOPSD achieves higher average accuracy than SFT and GRPO on both spatial and OOD datasets, exhibiting superior performance and generalization.

cs.LG↗
arXiv2026

Hidden Risks of Jev: An Empirical Study of Security, Privacy, and Dual Use

Jev turns natural-language questions into typed answers and probabilities with low latency and cost, enabling applications to route requests and select tools. While this interface allows Jev to integrate naturally into application workflows as a decision layer, the security and privacy implications of this emerging use remain largely unexplored. To address this gap, we conduct the first systematic study of these implications using the official Jev API and NanoJev, a local model with controllable training data and updates, focusing on three research questions: (1) What security threats arise when Jev is deployed as an application decision layer? (2) What private information can Jev reveal despite returning constrained typed outputs? (3) How can Jev's general-purpose decision capability be used for beneficial purposes or misused? Jev's decisions depend on application state and may be influenced by user-provided inputs. We therefore adapt prompt injection and adversarial suffixes to manipulate its decisions. Open-source Jev distribution and updates introduce supply-chain risks, which we examine by implanting backdoors in NanoJev through training data poisoning. Since Jev's outputs reflect both application state and information learned during training, we further adapt membership, private attribute, and internal knowledge inference attacks to recover sensitive information despite its constrained output format. Finally, Jev can serve as a general-purpose decision oracle for defensive and malicious workflows. We examine this dual use through four detection tasks covering prompt injection, jailbreak inputs, harmful content, and AI-generated text, alongside misuse scenarios involving jailbreak and model extraction. Our empirical evaluation shows that Jev remains vulnerable to the examined security and privacy threats, while its decision capability can support beneficial and malicious uses.

cs.CR↗
arXiv2026

Mind the Perspective: Let's Reason Recursively for Theory of Mind

Theory of Mind (ToM) reasoning requires inferring agents' beliefs from partial and asymmetric observations, which remains an open challenge for LLMs. Existing prompting-based approaches improve ToM reasoning through observable-event filtering or temporal belief chains, without explicitly modeling nested beliefs. We introduce RecToM, an inference-time framework for ToM reasoning that models nested beliefs via recursive perspective construction. RecToM constructs each character perspective from the preceding character perspective along the character chain specified by the question, reducing higher-order belief questions to actual-world questions within the final constructed perspective. We further provide a KD45 analysis showing that RecToM's perspective construction induces a well-formed belief modality beyond simple event filtering. Experiments on ToM benchmarks, including Hi-ToM, Big-ToM, and FanToM, across multiple LLM backbones show that RecToM consistently outperforms recent advanced approaches, achieving state-of-the-art performance. Notably, RecToM reaches 100\% accuracy on Hi-ToM with GPT-5.4 and Qwen3.5, a benchmark requiring higher-order ToM reasoning. Our code is available at https://github.com/you68681/rectom.

cs.AI↗
arXiv2026

Vortex-mediated spin current injection into two-dimensional superconductor NbSe2

Injection of pure spin current into superconductors remains a major challenge in superconducting spintronics. Previous studies have primarily focused on spin-polarized quasiparticles and spin-triplet supercurrents, while vortices, ubiquitous topological defects in type-2 superconductors, have been theoretically proposed as alternative carriers of spin angular momentum, yet direct experimental evidence is still lacking. Here, we report the vortex-mediated spin current injection in NbSe2/LiAl2Fe3O8(LAFO) bilayer, an Ising superconductor/ferrimagnetic insulator heterostructure. Under an out-of-plane temperature gradient and an in-plane magnetic field, the NbSe2/LAFO bilayer shows a pronounced thermoelectric peak near the upper critical magnetic field, which has the opposite sign to the conventional vortex Nernst signal observed in a single-layer NbSe2. The sign reversal suggests that the vortex flow induced by spin current injection is opposite to the flow driven by the temperature gradient, which is consistent with theoretical mechanisms including spin-vorticity transmutation and the inverse vortex spin Hall effect. By mapping the field temperature phase diagram, we reveal that the spin current injection occurs exclusively in the vortex liquid phase of NbSe2. Absence of the thermoelectric signal above the superconducting transition temperature further rules out the quasiparticle contribution. Our results establish vortices as efficient carriers of spin information in superconductors, opening a new route towards vortex-mediated superconducting spintronic devices.

cond-mat.supr-con↗
arXiv2026

Observation of Dyakonov-Perel-type magnon spin relaxation in uniaxial antiferromagnetic insulators

Long-distance transport of magnon spin currents in antiferromagnetic (AFM) insulators has attracted tremendous attention recently, however, the AFM magnon spin relaxation mechanisms remain elusive. Here, we report that the Dyakonov-Perel-type magnon spin relaxation mechanism governs the spin current transport along the easy axis in two prototypical uniaxial AFM insulators, Cr2O3 and alpha-Fe2O3. First, an over 450% enhancement of the first-harmonic nonlocal signal induced by a magnetic field is observed prior to the spin-flop transition, which can be well-interpreted by our model incorporating Dyakonov-Perel-type magnon spin relaxation. Secondly, we find that the magnon spin diffusion length in both crystals increases with magnetic field and saturates at fields above 0.8 T, consistent with our model. Finally, the temperature dependence of the zero-field magnon spin diffusion length in both AFM insulators can be qualitatively explained through our model. These findings are valuable for the development of low-dissipation antiferromagnetic spintronic devices.

cond-mat.mes-hall↗
arXiv2026

Lower central dark matter densities in nearby galaxies than predicted by simulations

Baryonic feedback in hydrodynamical simulations is typically invoked to alleviate the core--cusp problem in dwarf galaxies. Yet baryonic processes also induce adiabatic contraction of dark matter, producing overly steep density profiles and excessively high dark matter fractions in the inner regions of massive galaxies. The dark matter distribution of galaxies across a wide stellar-mass range is therefore a critical test for such simulations, but a comprehensive benchmark has remained absent. Here, we consistently measure the dark matter distribution from galaxy centres out to radii of 20--50 kpc for 136 nearby galaxies that together span the local mass--size relation over the stellar mass interval $10^9$--$10^{11.5}\,M_{\odot}$. We identify central regions with lower dark matter densities relative to $Λ$CDM simulation expectations---whose extent grows from about 10 kpc to $>50$ kpc as stellar mass increases from $10^{10} M_{\odot}$ to $10^{11.5} M_{\odot}$. Although their physical origin remains unclear, these low--dark matter regions are clearly indicated by the data. Our results provide an important observational benchmark for future hydrodynamical simulations that explore alternative dark matter models and feedback processes.

astro-ph.GA↗
arXiv2026

The GECKOS survey: Assembly history of the lenticular galaxy NGC 3957

We analyse the assembly history of the edge-on lenticular galaxy NGC 3957 using deep integral-field spectroscopic MUSE data from the GECKOS survey. By applying a dust-corrected Multi-Gaussian Expansion and a population-orbit superposition model, we disentangle the galaxy's stellar kinematics, age, and metallicity. We dynamically decompose the galaxy and identify three distinct components: a dynamically-cold main disc, a compact Nuclear Stellar Disc (NSD), and a hot component. The NSD emerges as the youngest and most metal-rich component ($t = 6.9 \pm 0.4$ Gyr; $[Z/H] = 0.49 \pm 0.06$ dex), implying that the stellar bar is a long-lived structure that formed at least $\sim 7$ Gyr ago. The main stellar disc is dynamically cold ($σ_z \sim 20-30$ km/s), precluding any significant mergers over the last $\sim 8$ Gyr, and exhibits a strong positive age gradient (younger inside, older outside) beyond the bar radius. Synthesising these dynamical fossil records, NGC 3957 likely evolved as a `faded spiral' in a small-to-medium group environment. Its outer disc might passively fade due to mild gas starvation, while the bar fuelled prolonged central star formation. Comparison with S0s in the Fornax cluster reveals that this combination of internal secular evolution and mild starvation produces `outside-in' fading signatures that could mimic the environmental stripping typically seen in dense clusters.

astro-ph.GA↗
arXiv2026

3DGBGS: 3D Granular Ball Gaussian Splatting for Compact Novel View Synthesis

Three-dimensional Gaussian Splatting (3DGS) enables high-quality real-time novel-view synthesis through explicit Gaussian primitives and differentiable rasterization. 3DGS and Granular Ball Computing (GBC), proposed in 2019, share a natural compatibility in adaptive representation. The efficiency of 3DGS partly stems from a coarse-to-fine and on-demand refinement process that draws on the generation principle of GBC. This connection motivates us to further introduce adaptive granular ball organization into anchor-based 3DGS. Existing anchor-based methods typically construct anchors from sparse SfM point clouds through fixed voxelization, which cannot adequately adapt to spatially non-uniform point distributions and leads to a trade-off among anchor count, model compactness, and rendering quality. To address this issue, we propose 3DGBGS (3D Granular Ball Gaussian Splatting), a compact anchor-based framework for novel-view synthesis. 3DGBGS adaptively partitions SfM point clouds into 3D granular balls, using larger balls to compactly represent smooth and redundant regions and smaller balls to preserve complex geometry and local details. Based on this representation, Granular Ball Anchor Initialization (GBAI) uses granular ball centers to initialize compact anchor positions, while the Granular Ball Scale Prior (GBSP) exploits granular ball radii to provide local scale priors for Gaussian generation. Experiments on four benchmarks show that 3DGBGS reduces initial and final anchors by 37.1% and 10.0%, respectively, and model storage by 9.8% on average, while maintaining comparable rendering quality.

cs.CV↗
arXiv2026

Joint Lossless Compression and Steganography for Medical Images via Large Language Models

Recently, large language models (LLMs) have driven promising progress in lossless image compression. However, directly adopting existing paradigms for medical images suffers from an unsatisfactory trade-off between compression performance and efficiency. Moreover, existing LLM-based compressors often overlook the security of the compression process, which is critical in modern medical scenarios. To this end, we propose a novel joint lossless compression and steganography framework. Inspired by bit plane slicing (BPS), we find it feasible to securely embed privacy messages into medical images in an invisible manner. Based on this insight, an adaptive modalities decomposition strategy is first devised to partition the entire image into two segments, providing global and local modalities for subsequent dual-path lossless compression. During this dual-path stage, we innovatively propose a segmented message steganography algorithm within the local modality path to ensure the security of the compression process. Coupled with the proposed anatomical priors-based low-rank adaptation (A-LoRA) fine-tuning strategy, extensive experimental results demonstrate the superiority of our proposed method in terms of compression ratios, efficiency, and security. The source code will be made publicly available.

eess.IV↗
arXiv2026

A Self-Evolving Agentic System for Automated Generation and Execution of Biological Protocols

Autonomous wet-lab experimentation requires more than plausible protocol text: biological intent, quantitative procedures, device constraints and experimental feedback must remain aligned from protocol and SOP design to code and physical execution. We developed ProtoPilot, a self-evolving multi-agent system, together with an expert-grounded benchmark and evaluation framework for testing this conversion as an experimental automation problem. The framework spans 294 synthetic-biology and molecular-biology tasks derived from 98 gold-standard protocols, wet-lab expert rubrics, device-level validity gates and real experimental tests. ProtoPilot incorporates layer-wise verifiability, multi-agent orchestration and a runtime-updated skill library to generate protocols, expand SOPs, synthesize SDK-compliant code and revise workflows from wet-lab feedback. It achieved a Top@3 expert-preference rate of 90.2%, an overall protocol-to-code gate pass rate of 89.5% and an Opentrons pass rate of 88.24%, compared with 32.35% for OpenTrons-AI. Wet-lab validation produced interpretable readouts, Sanger-confirmed products and feedback-corrected PCA-assembled DNA targets, establishing a verifiable route to autonomous experimentation. Together, these results show that the evaluation framework captures execution-relevant requirements for autonomous wet-lab automation, and that ProtoPilot can meet them by converting protocol and code generation into validated execution and feedback-guided revision.

cs.AI↗
arXiv2026

AnyMatch: Supercharging Universal Multi-Modal Image Matching with Large-Scale Single-View Images

Multi-modal image matching is essential for visual localization and multi-sensor fusion, but it is hindered by the scarcity of large-scale training data with precise geometric annotations. Existing real-world datasets suffer from prohibitive costs, limited scene diversity, and errors in SfM-MVS pipelines, while synthetic methods struggle to maintain 3D geometric consistency or achieve photorealistic appearance. To address this, we propose AnyMatch, a novel framework that leverages abundant, easily accessible single-view images at minimal cost to generate rich multi-modal training data. AnyMatch integrates monocular depth estimation, 3D reprojection, diffusion-based inpainting, and crossmodal image translation to synthesize multi-view, multi-modal image pairs with 3D geometric fidelity. Crucially, our method provides annotations that strictly adhere to 3D geometric consistency through explicit 3D reprojection, avoiding SfM-MVS error accumulation. Furthermore, AnyMatch offers strong scalability, enabling controllable scene diversity and annotation difficulty via adjustable input and camera parameters. We construct Any-syn, a large-scale synthetic multi-modal dataset using AnyMatch. Experimental results show that matching networks (e.g., LoFTR, EDM, RoMa) fine-tuned on Any-syn achieve substantial performance gains on multi-modal benchmarks, exhibiting superior generalization and robustness compared to models trained on existing data.

cs.CV↗
arXiv2026

Uncovering the dark matter distribution by combining stellar kinematics and integrated HI spectra: Method validation

We determined the dark matter (DM) distribution in galaxies by jointly modelling stellar kinematics from integral field unit (IFU) observations and the gaseous kinematics encoded in a single integrated HI spectrum. The stellar kinematics are described by a triaxial orbit-superposition Schwarzschild model, while the HI gas is described by an idealised disc model; both are governed by the same gravitational potential. The potential comprises the stellar mass, a generalised Navarro-Frenk-White (gNFW) DM halo, and a central black hole. We validated the method on 58 simulated galaxies generated from the TNG50 cosmological simulation. For each galaxy, we created two versions of mock data with azimuthal angles viewed side-on and end-on, thus yielding 116 mock observations in total. Our model recovers the total mass, stellar mass, and DM mass profiles within the data range; the median DM mass of the 58 simulated galaxies is recovered with a relative systematic bias smaller than 20% across all radii from 2--20 kpc. The statistical uncertainties on the DM masses within 5 kpc remain similar to those found with the model constrained by IFU data only. In contrast, the relative uncertainty on the DM mass in the outer regions decreases when the HI spectrum is included; at 20 kpc, it drops markedly, from about 85% to roughly 30%. The DM density slope defined explicitly in the gNFW model is systematically underestimated and thus does not yield a reliable quantity from observations using our approach. Instead, we introduce density slopes evaluated between 2 and 20 kpc, which are statistically well recovered for both the total mass and the DM mass. We demonstrate the reliability of this method in uncovering the DM distribution and emphasise its promise for application to large samples of observed galaxies.

astro-ph.GA↗
arXiv2026

Local and non-local $p$-energies on metric measure spaces

For $p>1$, we study subordination phenomena for local and non-local regular $p$-energies on metric measure spaces. Under suitable geometric assumptions, we show that if a local regular $p$-energy satisfies a Poincaré inequality and a cutoff Sobolev inequality with scaling function $Ψ$, then any non-local $p$-form induced by a jumping kernel with scaling function $Υ$, where $Υ$ lies strictly above $Ψ$ at small scales, defines a regular $p$-energy satisfying a non-local Poincaré inequality and a non-local cutoff Sobolev inequality. The corresponding scaling function $Ξ$ is explicitly determined by $Ψ$ and $Υ$. Our results also cover examples whose jumping kernels have light polynomial tails at infinity. These results provide a nonlinear extension of the classical subordination principle beyond the Dirichlet form framework.

math.AP↗
arXiv2026

Elliptic Harnack inequality and Poincaré inequality for $p$-energies on metric measure spaces

For $p>1$ and a $p$-energy on a volume doubling metric measure space, we prove the Poincaré inequality under the elliptic Harnack inequality, two-sided capacity bounds, and additional geometric and analytic assumptions. Combining this with our previous results, we obtain, under the same structural assumptions, the equivalence between the elliptic Harnack inequality together with two-sided capacity bounds and the conjunction of the Poincaré inequality and the cutoff Sobolev inequality.

math.AP↗
arXiv2026

Energy inequalities for cutoff functions of $p$-energies on metric measure spaces

For $p>1$, and for a $p$-energy on a volume doubling metric measure space, we provide several geometric and functional conditions for the validity of the cutoff Sobolev inequality. In particular, we prove that the elliptic Harnack inequality, two-sided capacity bounds, and some additional geometric and analytic assumptions imply the cutoff Sobolev inequality, without assuming the Poincaré inequality. Conversely, we show that the Poincaré inequality together with the cutoff Sobolev inequality recovers the analytic inputs used in the first implication. Moreover, in a lower-dimensional regime, the Poincaré inequality and the capacity upper bound imply the cutoff Sobolev inequality. As an application, we prove that the $p$-energy measure is singular with respect to the Hausdorff measure on the Sierpiński carpet for any $p>1$, thereby resolving a problem posed by Murugan and Shimizu [Comm. Pure Appl. Math. 78 (2025), no. 9, 1523--1608].

math.FA↗
arXiv2026

On singularity of $p$-energy measures on metric measure spaces

For $p>1$, we prove that, for a $p$-energy on a volume doubling metric measure space, the Poincaré inequality and the cutoff Sobolev inequality, both with $p$-walk dimension strictly larger than $p$, imply that the associated $p$-energy measure is singular with respect to the underlying measure. Under the slow volume regularity condition, we further prove that these two inequalities are equivalent to the resistance estimate; in particular, as part of the proof, we give a simple and direct derivation of the cutoff Sobolev inequality from the Poincaré inequality and the capacity upper bound. As a direct corollary, for a large family of fractals and metric measure spaces, including the Sierpiński gasket and the Sierpiński carpet, the $p$-energy measure is singular with respect to the underlying measure for any $p$ strictly greater than the Ahlfors regular conformal dimension.

math.FA↗