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Ran Li

Publications and source records attributed to Ran Li.

At least 37 records · Page 2Linked to original sources
arXiv2026

A Dark-matter Origin of Little Red Dots: Early Seeding and Super-Bondi Accretion

The "Little red dots" (LRDs) are a population of accreting supermassive black holes (SMBHs) in the early Universe which often exhibit undermassive or even undetectable stellar hosts. Their early emergence, high space density, and extremely large black-hole-to-stellar mass ratios pose a serious challenge to conventional seeding scenarios that rely on baryon for both the formation and growth of black holes. Here we demonstrate that the above anomalies can be naturally resolved if dark matter is self-interacting. We apply a fully relativistic, non-equilibrium halo-evolution model, first developed in our earlier work, to trace the complete gravothermal evolution of self-interacting dark matter (SIDM) halos, from the initial collapse into BH seeds to the subsequent accretion of dark matter. We find that in highly concentrated halos assembled before reionization, gravothermal collapse efficiently produces stellar-mass black-hole seeds within a few hundred million years. Remarkably, and contrary to standard expectations for dark-matter accretion, heat conduction in SIDM then sustains a prolonged super-Bondi inflow that drives these seeds to supermassive scale by the LRD epoch, without baryonic assistance. The halo conditions required for completing these processes, together with the probability of avoiding major mergers that disrupt gravothermal evolution, result in an SMBH population consistent with the observed abundance and redshift distribution of LRDs. Our findings establish a pathway in which SMBHs are seeded and assembled primarily from dark matter, well before substantial galaxies form around them, thereby offering both a compelling physical explanation for LRDs and a new observational probe of dark-matter microphysics.

astro-ph.CO↗
arXiv2026

Vidu S2: Real-Time Interactive, Editable, and Spatial Video Generation

We present Vidu S2, which comprises Vidu S2-Avatar, a real-time interactive digital-character model, and Vidu S2-Editing, a real-time video editing model. Moreover, we explore the feasibility of real-time spatial video generation for both Vidu S2-Avatar and Vidu S2-Editing. Compared with Vidu S1, Vidu S2-Avatar supports real-time 720p video generation, generation with dynamic references that can be updated at any moment, and stronger instruction following, such as dancing. Vidu S2-Editing supports editing a video stream in real time, including style rendering, clothing replacement, character replacement, and background replacement. Experiments show that Vidu S2 outperforms all baselines. A playable online demo is available at https://vidu.com/vidu-stream.

cs.CV↗
arXiv2026

PeroMAS: A Multi-agent System of Perovskite Material Discovery

As a pioneer of the third-generation photovoltaic revolution, Perovskite Solar Cells (PSCs) are renowned for their superior optoelectronic performance and cost potential. The development process of PSCs is precise and complex, involving a series of closed-loop workflows such as literature retrieval, data integration, experimental design, and synthesis. However, existing AI perovskite approaches focus predominantly on discrete models, including material design, process optimization,and property prediction. These models fail to propagate physical constraints across the workflow, hindering end-to-end optimization. In this paper, we propose a multi-agent system for perovskite material discovery, named PeroMAS. We first encapsulated a series of perovskite-specific tools into Model Context Protocols (MCPs). By planning and invoking these tools, PeroMAS can design perovskite materials under multi-objective constraints, covering the entire process from literature retrieval and data extraction to property prediction and mechanism analysis. Furthermore, we construct an evaluation benchmark by perovskite human experts to assess this multi-agent system. Results demonstrate that, compared to single Large Language Model (LLM) or traditional search strategies, our system significantly enhances discovery efficiency. It successfully identified candidate materials satisfying multi-objective constraints. Notably, we verify PeroMAS's effectiveness in the physical world through real synthesis experiments.

cs.MA↗
arXiv2026

Evidence for the transformation from lenticular to spiral galaxies

It is widely accepted that late-type galaxies, such as spirals, evolve into early-type systems, including elliptical and lenticular galaxies, through galaxy mergers and violent disk instability processes. Throughout this morphological transformation, star formation is typically suppressed by quenching mechanisms whose detailed nature remains the subject of active investigation. Here, we present compelling evidence for an evolutionary pathway that proceeds in the reverse direction. Using the integral field unit observations, we identify a population of spiral galaxies hosting quenched central cores (QCCs). These galaxies exhibit bimodal distributions in both their stellar population properties and their dynamical properties, along with sharp changes in radial gradients near the QCC boundary. These results indicate that the QCCs and the surrounding outer disks formed at distinct cosmic epochs and through different physical processes. Remarkably, QCCs closely resemble quiescent early-type galaxies, particularly lenticular galaxies, in their mass-size and mass-velocity dispersion scaling relations, as well as in their stellar population demographics and internal kinematics. These findings provide strong support for a rejuvenation scenario in which spiral disks are reassembled around pre-existing quiescent lenticular or early-type systems. Moreover, we show that such rejuvenation, accompanied by a reverse morphological transformation from early- to late-type appearance, is quite common. This indicates that quenching in galaxies is not invariably a terminal state and can be reversed under appropriate conditions.

astro-ph.GA↗
arXiv2026

FlowEvo: Self-Evolving Agents through the Co-Evolution of Workflows and Executable Skills

Large language model agents can adapt to complex tasks by constructing workflows at inference time, but procedures discovered in one episode are usually discarded after execution. Existing skill libraries provide reusable executable routines, but are typically assembled offline and do not grow from the agent's own workflows. We introduce FlowEvo, a training-free framework in which workflows and skills co-evolve at inference time. FlowEvo compiles successful workflows into callable skills, stores them in a persistent bank, and uses retrieved skills either through direct execution or as context for constructing new workflows. It also tracks each skill's downstream utility and suppresses skills that cause negative transfer. Using a shared GPT-4o-mini backbone, FlowEvo achieves the highest accuracy among 8 baselines on the full standard splits of ALFWorld, HumanEval, MBPP, GSM8K, and MATH-500. On ALFWorld, it reaches 85.6%, 26.4 points above the strongest baseline, while using roughly one third as many tokens. Across 10 base models spanning 7B to 671B parameters, FlowEvo outperforms ExpeL in 49 of 50 model-dataset comparisons. Code is available at https://github.com/DEFENSE-SEU/FlowEvo.

cs.AI↗
arXiv2026

JieZi: A Large-Scale Expert-Audited Dataset and Benchmark for Ancient Chinese Character Exegesis

The scholarly exegesis of ancient Chinese characters demands integrating visual observation, linguistic analysis, and historical context. However, existing computational approaches focus narrowly on subtasks such as character recognition and retrieval, lacking the structured datasets and benchmarks required for comprehensive scholarly analysis. To address this limitation, we introduce Ancient Chinese Character Exegesis (ACCE), a vision-language question answering (VQA) task that models the scholarly exegesis process. ACCE is organized into four progressive levels: basic character identification, glyph-form analysis, meaning exegesis, and diachronic evolution analysis. To support this task, we construct two complementary resources. JieZi-Dataset is the first large-scale, expert-audited VQA training dataset for ACCE, comprising over 500K QA pairs. It is constructed via a pipeline that reduces factual errors by constraining generation with expert-designed templates and source-text references. Human verification is further applied at each key stage to ensure scholarly accuracy. JieZi-Bench is an evaluation benchmark aligned with the exegesis process, constructed and verified by human experts to ensure evaluation reliability. It consists of four levels with reference answers curated from authoritative lexicographic works held separate from the training data. Experiments on multimodal large language models show that current models perform well on basic identification but struggle with glyph analysis, semantic reasoning, and diachronic understanding. Fine-tuning on JieZi-Dataset substantially improves performance across all four levels. Code and dataset are available at https://github.com/Ran00w/JieZi.

cs.CV↗
arXiv2026

Terahertz-based longitudinal phase space diagnostics of laser wakefield accelerated electron beams

Femtosecond relativistic electron beams are key probes of ultrafast dynamics, and their pulse duration directly limits the achievable temporal resolution. Laser wakefield acceleration (LWFA) provides a compact source of such beams, but the injection-induced energy spread makes bunch compression sensitive to nonlinear longitudinal transport, motivating direct longitudinal phase space (LPS) measurements. Here, a terahertz transverse-deflecting cavity (THz-TDC) combined with a dipole magnet is used to reconstruct the nonlinear LPS of LWFA electron bunches compressed in a double-bend achromat (DBA), resolving a characteristic C-shaped distribution associated with higher-order longitudinal transport. At an average energy of approximately 4.55 MeV, the diagnostic achieves a temporal resolving power of 1.8 fs and an energy resolution of 6.0 keV, corresponding to a relative energy resolution of 0.13%. For comparable energy spreads of approximately 2.9%, shifting the transmitted energy-window center from 4.574 MeV to 4.532 MeV moves the selected beam away from a low-slope region of the nonlinear LPS and increases the root-mean-square bunch length from 26 fs to 42 fs; with the window center held near 4.553 MeV, increasing the energy spread from 2.0% to 4.4% lengthens the bunch from 27 fs to 44 fs. These results show that the final bunch duration is governed by both the position and width of the transmitted energy window within the nonlinear LPS, establishing an LPS-guided strategy for optimizing DBA-compressed LWFA electron bunches and providing a basis for future higher-order phase-space correction.

physics.acc-ph↗
arXiv2026

G2VD: Generalizable AI-Generated Video Detection via Counterfactual Intervention and Causal Disentanglement

Rapid advances in AI video generation pose increasing security risks and call for reliable detectors with strong cross-domain generalization. Although existing methods perform well under in-domain evaluation, their performance degrades substantially on unseen generators. A key reason is shortcut learning, where detectors rely on domain-specific bias rather than intrinsic forensic cues. To address this issue, we propose G2VD, a generalizable AI-generated video detection framework based on counterfactual intervention and causal disentanglement. First, G2VD introduces a counterfactual intervention pipeline (CFIPipeline) that constructs counterfactual samples through VAE-based reconstruction and subsequent frequency-domain and pixel-domain alignment, thereby weakening spurious correlations between domain-specific bias and authenticity labels. Building on this intervention, we further design a causal disentanglement classifier that combines two domain-anchored branches with complementary objectives and a constraint based on the Hilbert-Schmidt Independence Criterion (HSIC), encouraging the causal and non-causal representations to capture intrinsic forensic cues and domain-specific bias, respectively. Experiments across four public datasets demonstrate strong cross-domain performance and consistent gains over baseline methods. In the challenging GenVidBench setting, G2VD achieves over 90\% overall ACC, with improvements of 0.194 in F1 and 0.104 in AUC over comparable state-of-the-art methods, while using only 10\% of the available training data. Code is available at https://github.com/DMOSCAR-98/G2VD.

cs.CV↗
arXiv2026

Note on the local calculation of decoherence of quantum superposition in the static black holes

We investigate the decoherence of a quantum spatial superposition of a static particle in Schwarzschild and Reissner-Nordström black holes. By treating the particle as a localized classical source coupled to a quantum scalar field, we reformulate the decoherence process in the Danielson-Satishchandran-Wald (DSW) gedankenexperiment through coherent state generation and derive the local expression for the decoherence functional in terms of the Wightman function. In the long-time limit, the decoherence rate is shown to be characterized by the low-frequency behavior of the Wightman function. We then employ the asymptotic matching method to calculate the analytical expressions of the Wightman functions in the Boulware, Unruh, and Hartle-Hawking vacua. We show that the decoherence behavior depends on the quantum state of the environmental field. While the Boulware vacuum gives vanishing decoherence for a static superposition, the thermal effects associated with Hawking radiation in the Unruh and Hartle-Hawking vacua can induce nonvanishing decoherence.

gr-qc↗
arXiv2026

TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection

This paper proposes a two-stage pseudo anomaly-guided anomaly detection method (\textbf{T}wo-stage \textbf{P}seudo \textbf{A}nomaly-guided \textbf{A}nomaly \textbf{D}etection, \textbf{TPA-AD}) for axle-box bearing time-series anomaly detection (time series anomaly detection, TSAD) under the setting where only normal samples are available for training. The method first generates pseudo-anomalous windows near the normal boundary using a reconstruction model and per-feature target-error control. It then learns anomaly-sensitive representations through contrastive learning between normal and pseudo-anomalous windows, and finally produces window-level and point-level anomaly scores using k-nearest neighbors (KNN). Compared with existing methods that rely on known fault categories, real anomaly priors, or random anomaly injection, TPA-AD improves the separability of the normal boundary by constructing pseudo-anomalies in boundary neighborhoods and can jointly handle continuous and discrete features in mixed-variable scenarios. The main experiments are conducted on bearing fault detection datasets and degradation-process datasets, with an additional exploratory extension on $13$ public TSAD datasets. The results show that the proposed method yields relatively stable anomaly responses, is sensitive to degradation evolution, and demonstrates a certain degree of broader applicability on public TSAD benchmarks and real high-speed-train-related bearing data.

cs.LG↗
arXiv2026

Enhanced evidence of $X(7200)$ and improved measurements of $X(6900)$ parameters from a combined LHCb-ATLAS-CMS analysis

We report stronger evidence for the $X(7200)$ state and markedly improved measurements of the $X(6900)$ resonance parameters based on a combined analysis of the di-$J/ψ$ mass spectrum using published data from LHCb, ATLAS, and CMS. Through simultaneous fits to the datasets from all three experiments, we observe the $X(6900)$ with overwhelming significance ($>12σ$) and determine its mass and width with improved precision. For the $X(7200)$, we find consistent signals across multiple interference models, with significances ranging from $3.7σ$ to $6.6σ$; in the best-fit model (the CMS three-resonance scheme), the significance reaches $6.6σ$, providing substantially stronger evidence for this state. Our results underscore the essential role of interference effects in fully charmed tetraquark spectroscopy and offer new constraints on their production mechanisms at the LHC.

hep-ex↗
arXiv2026

CPMobius: Iterative Coach-Player Reasoning for Data-Free Reinforcement Learning

Large Language Models (LLMs) have demonstrated strong potential in complex reasoning, yet their progress remains fundamentally constrained by reliance on massive high-quality human-curated tasks and labels, either through supervised fine-tuning (SFT) or reinforcement learning (RL) on reasoning-specific data. This dependence renders supervision-heavy training paradigms increasingly unsustainable, with signs of diminishing scalability already evident in practice. To overcome this limitation, we introduce CPMöbius (CPMobius), a collaborative Coach-Player paradigm for data-free reinforcement learning of reasoning models. Unlike traditional adversarial self-play, CPMöbius, inspired by real world human sports collaboration and multi-agent collaboration, treats the Coach and Player as independent but cooperative roles. The Coach proposes instructions targeted at the Player's capability and receives rewards based on changes in the Player's performance, while the Player is rewarded for solving the increasingly instructive tasks generated by the Coach. This cooperative optimization loop is designed to directly enhance the Player's mathematical reasoning ability. Remarkably, CPMöbius achieves substantial improvement without relying on any external training data, outperforming existing unsupervised approaches. For example, on Qwen2.5-Math-7B-Instruct, our method improves accuracy by an overall average of +4.9 and an out-of-distribution average of +5.4, exceeding RENT by +1.5 on overall accuracy and R-zero by +4.2 on OOD accuracy. Our codebase has been released at https://github.com/thunlp/CPMobius.

cs.CL↗
arXiv2026

Detection of a dark matter subhalo in the strongly lensed system PJ011646

We present a strong lensing analysis of the system PJ011646 using high-resolution ($\sim$0.1 arcsec) Atacama Large Millimeter/submillimeter Array (ALMA) dust-continuum observations to test for the presence of dark matter substructures. The lens mass distribution is modelled with an elliptical power law and third- and fourth-order multipoles (PL+MP; $m=3,4$), plus external shear. The multipoles have amplitudes of $\simeq$1.5 per cent of the convergence, consistent with nearby early-type galaxies, and improve the fit by $Δ\ln Z = 52.1$ relative to a pure PL model. Using this best-fitting macromodel, we perform a grid-based subhalo search in the image plane, parametrising the perturber as a spherical NFW. A subhalo in two locations in the image plane improves the fit by $Δ\ln Z>10$. Both correspond to the same location in the source plane, so they are partially degenerate; follow-up analysis suggests that only one is physically real. This is a subhalo of mass $M_{200} = {2.78}_{-0.66}^{+0.43} \times 10^{10} \, M_\odot$ and concentration $c_{200} = 30_{-7}^{+5}$, detected at $\sim$5.8$σ$ significance (relative to the PL+MP). This concentration is consistent with that expected for a typical tidally stripped Navarro-Frenk-White subhalo. The enclosed projected mass is most tightly constrained within a radius of 2 kpc, where we infer $M_{\rm sub} = {3.57}_{-0.14}^{+0.16}\times 10^9 \, M_\odot$. From grid cells consistent with no detection ($Δ\ln Z < 10$), we derive limits on the minimum subhalo mass that could have been detected at $3σ$ significance, finding $M_{200} \approx 8 \times 10^{8} \, M_\odot$ in the most sensitive regions of the lensed arcs. This demonstrates that ALMA continuum imaging at sub-arcsecond resolution can probe dark matter substructure in a mass regime where cold and warm dark matter models predict different abundances of subhalos.

astro-ph.GA↗
arXiv2026

Probing Unruh Effect from Enhanced Decoherence

We investigate the decoherence of an Unruh-DeWitt detector coupled to scalar, electromagnetic, and spinor fields in four-dimensional Minkowski spacetime. By employing the Schwinger-Keldysh influence functional formalism, we derive a universal scaling law relating the decoherence rate to the proper acceleration $a$ and the scaling dimension $Δ$ of the environmental field operator. By analyzing both sharp (top-hat) and smooth Gaussian switching functions, it is shown that the decoherence rate in the asymptotic long-time limit scales as $a^{2Δ-1}$. This scaling indicates that increasing scaling dimension of the coupling field operators can significantly enhance the decoherence, thereby providing a more sensitive probe of the Unruh effect.

gr-qc↗
arXiv2026

Non-Equilibrium Relativistic Core Collapse of Self-Interacting Dark Matter Halos -- Limits On Seed Black Hole Mass

Recent observations of supermassive black holes (SMBHs) at high redshifts pose challenges to standard seeding mechanisms. Among competing models, the collapse of self-interacting dark matter (SIDM) halos provide a plausible explanation for early SMBH formation. While previous studies on modeling the gravothermal collapse of SIDM halos have primarily focused on non-relativistic evolution under the assumption of hydrostatic equilibrium, We advance this framework by relaxing the equilibrium assumption and additionally incorporating general-relativistic effects. To this end, we introduce the Misner-Sharp formalism to the SIDM context for the first time. Our model reproduces the standard hydrostatic models in the early long-mean-free-path (LMFP) regime, but displays interesting distinct behavior in the late short-mean-free-path (SMFP) regime, where intense outward heat flux drives a rapid expansion of the outer envelope, removing mass from the core and significantly decelerating the collapse. Our general relativistic treatment enables us to follow halo evolution to the final stage when the apparent horizon forms. Our simulation yields a seed black hole mass of approximately $3\times10^{-8}$ of the halo mass at horizon formation, suggesting that additional mechanisms such as baryonic effects are critical for seeding black holes that are sufficiently massive to account for SMBHs in the early Universe.

astro-ph.CO↗
arXiv2026

Building Low-Altitude Communication Networks: A Digital Twin-Based Optimization Framework

Low-altitude communication networks (LACNs) serve as the critical infrastructure of the emerging low-altitude economy (LAE), supporting services such as drone delivery and infrastructure inspection. However, LACNs operate in highly dynamic three-dimensional (3D) environments characterized by high mobility and predominantly line-of-sight (LoS) propagation, creating strong coupling among key performance objectives including coverage, interference mitigation, handover management, and sensing capability. Isolated tuning of individual objectives cannot capture these cross-objective interactions, rendering conventional approaches based on experience-driven tuning and repeated field trials inefficient and costly. To address these challenges, we propose DT-MOO, a Digital Twin-based Multi-Objective Optimization framework for LACNs. By constructing a high-fidelity virtual replica that integrates realistic environmental models, electromagnetic (EM) propagation, and traffic dynamics within a unified environment, DT-MOO enables joint evaluation and systematic optimization of interdependent network parameters, scoring candidate configurations by their combined effect on multiple objectives. As the foundational validation of the framework, we report real-world experiments in a 5G-enabled LACN focusing on coverage-interference co-optimization, where DT-MOO increases the high-quality coverage rate from 14.0% to 52.9% across all evaluated altitudes compared to an operator-provisioned, experience-based baseline, while achieving a net SINR gain under stringent criteria despite local spatial trade-offs, confirming its ability to handle coupled objectives in practical LACN deployment.

eess.SP↗
arXiv2026

Light-controlled van der Waals tunnel junctions: mechanisms, architectures, functionalities, and opportunities

The phenomenon of electron tunneling has long been central to quantum transport and continues to provide a powerful framework for understanding and controlling electronic processes in solids. When combined with optical excitation, tunneling becomes a particularly rich platform for experiments, because light can drive nonequilibrium carrier populations and open transport pathways that are inaccessible without optical excitation. The emergence of van der Waals (vdW) materials has greatly expanded this opportunity by enabling atomically thin heterostructures with clean interfaces, engineered barriers, and highly tunable band alignment. In this review, we discuss the fundamental mechanisms of photo-assisted transport and the realization of vdW tunnel junctions, and show how they provide electrical access to nonequilibrium dynamics and collective excitations in quantum materials. We further examine emerging functionalities including photodetection, tunneling-driven light emission, sensing, and memory. Finally, we present a forward-looking perspective on new opportunities such as quantum-geometric probes, twist-resolved spectroscopy, moire ferroelectricity, and scalable architectures for computing and sensing.

cond-mat.mes-hall↗
arXiv2026

Decision-focused Conservation Voltage Reduction to Consider the Cascading Impact of Forecast Errors

Conservation Voltage Reduction (CVR) relies on the effective coordination of slow-acting devices, such as OLTCs and CBs, and fast-acting devices, such as SVGs and PV inverters, typically implemented through a hierarchical multi-stage Volt-Var Control (VVC) spanning day-ahead scheduling, intra-day dispatch, and real-time control. However, existing sequential methods fail to account for the cas-cading impact of forecast errors on multi-stage decision-making. This oversight results in suboptimal day-ahead schedules for OLTCs and CBs that hinder the ef-fective coordination with fast-acting SVGs and inverters, inevitably driving a trade-off between real-time voltage security and CVR efficiency. To improve the Pareto front of this trade-off, this paper proposes a novel bi-level multi-timescale forecasting (Bi-MTF) framework for multi-stage VVC optimization. By integrating the downstream multi-stage VVC optimization into the upstream forecasting mod-els training, the decision-focused forecasting models are able to learn the trade-offs across temporal horizons. To solve the computationally challenging bi-level for-mulation, a modified sensitivity-driven integer L-shaped method is developed. It utilizes a hybrid gradient feedback mechanism that integrates numerical sensitivity analysis for discrete variables with analytical dual information for continuous fore-cast parameters to ensure tractability. Numerical results on a modified IEEE 33-bus system demonstrate that the proposed approach yields superior energy savings and operational safety compared to conventional MSE-based sequential paradigms. Specifically, as the capacity of fast-acting devices increases, the energy savings of the proposed method rise from 2.74% to 3.41%, which is far superior to the 1.50% to 1.76% achieved by conventional MSE-based sequential paradigms.

eess.SY↗