arXiv subjects
Publications and source records attributed to Xin Zhang.
We prove that every $n$-vertex simple planar graph with no copy of $C_8$ has at most \[ \frac{69}{25}(n-2) \] edges, for every $n\ge 8$. This improves the best known bound \[ \frac{323}{108}n-6 \qquad \text{for every } n\ge 27. \]
Recent baryon acoustic oscillation measurements from DESI provide important new clues for reassessing whether the standard $Λ$CDM model offers a sufficient description of the late-time expansion history of the Universe. When combined with cosmic microwave background and type Ia supernova data, these measurements show an apparent departure from the $Λ$CDM model, commonly described as dynamical dark energy (DDE) with equation of state crossing the phantom divide (i.e., quintom behavior). This review examines the current status of the DESI-motivated indications for DDE and their possible implications for physics beyond $Λ$CDM. We discuss how the strength of the preference for DDE depends on the adopted parametrization and dataset combination, and how residual systematics or internal tensions among datasets may affect its interpretation. At the background level, several mechanisms beyond $Λ$CDM can produce similar expansion histories. We therefore further discuss how the same effective departure from $w=-1$ may arise from physically distinct scenarios, including interacting dark energy, non-minimally coupled gravity, and non-standard dark matter. Meanwhile, these different new-physics interpretations may have different implications for current cosmological tensions, especially those involving $H_0$, $S_8$, and $\sum m_ν$. In conclusion, the question posed by DESI is not merely whether dark energy evolves with time, but rather how, within the framework of precision cosmology, to disentangle new physics scenarios from systematic errors.
Recent DESI baryon acoustic oscillation data reveal deviations from $Λ$CDM cosmology, conventionally attributed to dynamical dark energy (DE). We demonstrate that these deviations are equally, if not better, explained by interactions between dark matter and dark energy (IDE), without requiring a time-varying DE equation of state. Using a unified framework, we analyze two IDE models - coupled quintessence and coupled fluid - against the latest CMB (Planck, ACT, SPT), DESI BAO, and SN (including DES-Dovekie recalibrated) data. Both IDE scenarios show robust evidence for non-vanishing interactions at the 3-5$σ$ level, with marginalized constraints significantly deviating from the $Λ$CDM limit. This preference persists even under DES-Dovekie SN recalibration, which weakens dynamical DE evidence. Crucially, for the same number of free parameters, IDE models provide fits to low- and high-redshift data that match or exceed the performance of the CPL dynamical DE parametrization. Our results establish IDE as a physically motivated alternative to dynamical DE, highlighting the necessity of future cosmological perturbation measurements (e.g., weak lensing, galaxy clustering) to distinguish between these paradigms.
Automating analog circuit topology design is essential to reduce the extensive manual effort required to meet increasingly diverse and customized application demands. Recent advances have applied sequence-to-sequence fine-tuning on pretrained language models to directly generate circuit topologies from user specifications in a single pass. However, these one-shot generation methods failed to generate complex circuits due to their exponentially growing search spaces and limited training datasets. In this paper, we present EXPLORE, a search-enhanced framework that integrates simulator-guided Monte Carlo Tree Search (MCTS) with transformer-based decoding to enable test-time scaling for analog topology generation. By leveraging language-model priors and bypassing high-confidence structural tokens, EXPLORE allocates expensive simulator budget primarily toward topology-altering decisions during search. On a 6-component benchmark at a tight tolerance of 0.01, EXPLORE raises the success rate from 12% for one-shot generation and 33% for a sampling-and-filter baseline to 65%, and lowers MSE by over 20% relative to sampling-and-filter under the same search budget. These results establish EXPLORE as the first framework to integrate structured test-time search with LM decoding for analog topology generation, and a practical step toward scaling LLM-driven design automation.
Reusable agent skills are emerging as a service-oriented capability layer for Large Language Model (LLM) agents. Unlike plain retrieval items, a skill exposes functional capabilities, input-output assumptions, tool dependencies, context cost, and risk metadata. Selecting skills is particularly challenging for small LLM agents, which can load only a few capability units under restricted context, tool availability, and risk tolerance. Existing fixed Top-k methods rank skills by textual relevance and overlook requirement satisfaction, deliverability, and operational constraints. We present SkillSelect-Serve, a QoS-aware, budget-constrained Skill Service recommendation framework. Raw skills are profiled as structured Skill Services, the task is converted into a structured requirement object, and candidates discovered from a large-scale registry are ranked by a calibrated task-conditioned suitability estimator and packed by a constrained projection enforcing token-budget, aggregated-risk, and tool-availability constraints, using only deployment-observable features. On a registry of 35,353 skills with pooled multi-positive relevance judgments verified by two independent assessors, the unconstrained top-5 recommendation fits a realistic 4,000-token context for only 9.1% of tasks; the constrained projection restores 100% deliverability at a cost of only 1.14 points of hit rate, outperforming retrieve-and-rerank, budget truncation, and diversity-based selection under identical budgets. The same mechanism halves delivered risk exposure and eliminates the 44-81% tool-violation rates of tool-agnostic recommendation. At an identical three-service budget, hit rate improves from 0.8864 to 0.9091 over fixed Top-3 retrieval. The results support managing reusable agent skills as discoverable, comparable, and constraint-aware service units instead of plain retrievable documents.
Pulsar timing arrays (PTAs) are limited in localizing nanohertz continuous gravitational waves (CGWs) by uncertainties in pulsar distances. We introduce a method to infer pulsar distances in two dimensions, using phase information from the pulsar terms of multiple CGW sources. Our approach can enhance distance precision and, in some cases, achieve order-of-magnitude improvements relative to existing one-dimensional distance-inference methods. Using simulations of an SKA-era PTA with realistic parallax-based distance priors, we demonstrate that pulsars at $\sim 1$ kpc can achieve sub-parsec distance precision with only a few CGW sources. Such improvements in pulsar-distance precision have important implications for CGW host-galaxy identification and multimessenger observational prospects.
Cross-lingual retrieval-augmented generation (RAG) is often deployed in an English-evidence regime, where users query in diverse languages but retrieved passages remain English. In this setting, generation can fail despite strong base models: English evidence induces language drift (English or code-switching outputs) and models use evidence unreliably when producing non-English answers. We attribute these failures to two post-training challenges: (i) errors are prefix-dependent, so fixed-trajectory supervision suffers from prefix mismatch; and (ii) sequence-level (partly discrete / judge-based) rewards yield noisy credit assignment and high-variance updates. We propose TR-RAG, a teacher-regularized RL recipe that couples reward optimization with on-policy distillation on student-visited prefixes. A compact student samples on-policy answers, while a stronger frozen teacher is queried only on those prefixes and provides a prefix-wise student-to-teacher reverse-KL anchor. We further introduce a reward decomposition for English-evidence multilingual generation, combining language consistency, character 3-gram recall, and an LLM-judge score for evidence-grounded correctness. Across three benchmarks (BioASQ-ENKB5, Hotpot-ENKB5, and naturally multilingual MKQA) and two backbones, TR-RAG improves the composite of language adherence and evidence-grounded correctness over strong baselines. Crucially, the teacher anchor acts as a safety net: on in-domain languages it prevents the large language-consistency collapses (up to ~27 percentage points) that reward-only RL can suffer by drifting below even the base model, while on distant out-of-distribution languages, where reward-only RL stalls at the base model's ceiling, it still improves evidence grounding; and on character 3-gram recall the compact student sometimes surpasses its 70B teacher.
Three-dimensional magnetic solitons retain topological protection only while their spin field remains continuous. Here we show that chemical inhomogeneity can break this protection in a controlled way, converting a hopfion-like toroidal texture into an effectively two-dimensional skyrmion string. Tilt-dependent Lorentz transmission electron microscopy, electron energy-loss spectroscopy, and micromagnetic simulations of pristine, uniformly Y-doped, and nonuniformly Y-doped disordered TiO2 nanoparticles embedded in a FeCrNiMn host reveal two regimes. Uniform Y doping enriches Ti3+/oxygen-vacancy localization centers and stabilizes closed rings with Hopf invariant QH approximately 1. Nonuniform Y doping forms a Ti4+-rich, vacancy-depleted boundary that creates a sharp q=D/(2A) gradient and a weak-moment leakage channel. This coupled mismatch torque and continuity leakage split the ring, leaving a skyrmion-string remnant and a field-sensitive helicity texture.
We investigate the behavior of black hole scalarization induced by a charged scalar field in the extended phase space of the asymptotic AdS spacetime with three distinct horizon topologies. The results indicate that in all three cases, the charged black hole spacetime undergoes scalarization at low temperatures. Notably, the spherical topology is unique in that its domain of scalarization theoretically extends to much higher temperatures under low pressure in the extended phase space. Moreover, the scalarization process in the spherical case exhibits complex phase transition behaviors without additional non-linear terms, which are similar to those in the planar and hyperbolic topologies with the assistance of non-linear terms. With increasing pressure in the extended phase space, the condensate of the scalarization in all three cases undergoes a transition from the first-order style to a cave-of-wind style. This study provides deeper insight into the zeroth-order phase transition during black hole scalarization and reveals the complete phase structure of black holes in the extended phase space.
The standard $Λ{\rm CDM}$ model has encountered serious challenges and the $H_0$ tension has become more significant with increasingly precise cosmological observations. Meanwhile, inconsistencies in measurements of the curvature parameter $Ω_\mathrm{K}$ between different datasets also have emerged. In this work, we employ two global and cosmic age-based parameterizations, PAge and MAPAge, to perform model-independent measurements of the Hubble constant $H_0$ and $Ω_\mathrm{K}$ by utilizing the inverse distance ladder (IDL). To construct the PAge-improved IDL, we utilize the strong gravitational lensing (SGL), cosmic chronometers (CC), and gamma ray bursts (GRB) data to calibrate the latest DESI DR2 baryon acoustic oscillation and DESY5 or DES-Dovekie type Ia supernova data. Our analysis indicates that DESI+DES-Dovekie+SGL+CC+GRB gives $H_0=72.20\pm 1.00\,{\rm km}~{\rm s}^{-1}~{\rm Mpc}^{-1}$ in the MAPAge model, reducing the $H_0$ tension to the $0.6σ$ level. Extending to the MAPAge$+Ω_{\rm K}$ model, we obtain $Ω_\mathrm{K}=0.005\pm 0.037$, which suggests that current late-time data are consistent with a flat universe. Finally, the Bayesian analysis indicates that the present late-universe data provide weak to moderate evidence in favor of PAge and MAPAge relative to $Λ{\rm CDM}$.
Large language models have made research ideation increasingly accessible, yet effective idea development requires more than generating candidate directions. Researchers must ground a problem in current literature, identify meaningful bottlenecks, differentiate from existing solutions, and evaluate risks before committing to implementation. We present ResearchStudio-Idea as a reusable skill suite for this first mile of research ideation. The suite includes Paper-Search, a standalone multi-source literature search skill; Scoop-Check, a standalone prior-art collision checker for novelty claims; and IdeaSpark, the end-to-end skill that composes evidence grounding, pattern-guided generation, collision retrieval, audit, and idea-card rendering into one workflow. IdeaSpark is constructed from a corpus of 1,947 machine learning conference papers collected from ICLR, ICML, and NeurIPS between 2021 and 2025, including Oral papers, a separately tracked high-citation subset, and rejected submissions. Analysis of these outcomes reveals 31 recurring ideation sub-patterns, consolidated into 15 reusable ideation patterns. Each pattern is operationalized as a structured card containing research contexts, bottleneck types, differentiation strategies, supporting precedents, and common failure modes. Given a research problem and an evidence bundle, IdeaSpark evaluates evidence readiness, reconstructs the surrounding research context, identifies unresolved bottlenecks, selects relevant patterns, instantiates one candidate direction, retrieves potentially conflicting prior work, and performs outcome-informed auditing. This workflow transforms reusable ideation patterns into traceable research proposals. Blind automated-judge evaluations show that IdeaSpark consistently produces stronger research proposals than no-skill and generic-skill baselines while maintaining competitive novelty.
Polarization is fundamental degree of freedom of light, crucial for applications from imaging to quantum optics. Although numerous methods can spatially manipulate the polarization orientation, e.g., for generating vector beam, achieving simultaneous, spatially resolved control of both the orientation (ψ) and ellipticity (\c{hi}) remains challenging. Here, we present an efficient and compact platform to generate spatially variant arbitrary polarization states, e.g., optical skyrmions, in free space, using cascaded spatially variant waveplates in a single silica glass plate via ultrafast laser direct writing. We propose two configurations: (1) a spatially variant half-waveplate (S-HWP) and followed by a quarter-waveplate (S-HWP); and (2) two cascaded spatially variant quarter-waveplates (S-QWPs). Design rules linking the target polarization parameters (orientation ψ and ellipticity \c{hi}) to the fast-axis distributions enable spatially resolved arbitrary polarization control. Using these devices, we realize Néel-, Bloch-, and anti-skyrmions of different orders (e.g., second and fourth) and n-π textures (2π and 3π), with polarization-resolved measurements in excellent agreement with simulations. We further demonstrate 3 by 3 arrays comprising either identical or hybrid skyrmions. This approach enables the realization of spatially variant arbitrary polarization state and scalable skyrmion lattices.
Gravitational lensing of gravitational waves can probe compact lenses, dark matter substructure, and cosmological distances, but identifying lensed events is difficult when unrelated binary mergers overlap in the same analysis window. We develop physics-informed posterior learning for ranking lensed multi-image signals against unrelated multiple-merger events. The method exploits the geometric-optics consistency that lensing can change amplitudes, arrival times, and Morse phase offsets while preserving the intrinsic phase evolution of the source. We infer a simulation-trained approximate posterior for the common detector-frame chirp mass and symmetric mass ratio, and fuse posterior samples with direct waveform features. Training uses generic multi-image simulations, while point-mass, singular-isothermal-sphere, singular-isothermal-ellipsoid, and shear-perturbed lenses are reserved for held-out lens-family evaluation. For the observationally motivated binary-black-hole population, the fusion ranking raises the detection efficiency from $20.8\%$ to $35.2\%$ at a $1\%$ reference false-positive-rate threshold calibrated on the corresponding unrelated multiple-merger sample. It lowers the network signal-to-noise ratio needed for $50\%$ detection efficiency from 45.3 to 33.5, which corresponds to a 1.35 times larger signal-to-noise-ratio-equivalent distance scale. The gain is limited by loud unrelated multiple-merger events that are partly source consistent, and by the need to calibrate the unrelated multiple-merger population. These results suggest that physical consistency can become a guiding principle for machine learning searches in dense gravitational-wave catalogs.
Diffusion models have become a dominant paradigm for conditional image generation, yet existing approaches generally follow two directions: task-specific designs that can improve performance but limit generalization, and training-free loss guidance that compresses rich conditions into scalar objectives and applies stepwise guidance, leading to information bottlenecks and error accumulation along the sampling trajectory. Given the urgent need for an effective unified framework across diverse conditional image generation tasks, we propose Data Injection and Contrastive Trajectory Refinement (DICT), a training-free inference method that enhances conditional image generation without introducing task-dependent architectures. DICT introduces Data Injection, where noise-perturbed conditional signals are integrated into early denoising stages; by performing guided denoising on these injected signals, DICT adaptively selects and distills task-salient information from the raw condition, effectively preserving spatial richness and ensuring precise condition-to-generation alignment. Furthermore, DICT applies Contrastive Trajectory Refinement across adjacent denoising states, enabling pairwise comparisons that progressively improve sample quality. These designs keep inference simple while improving cross-task transfer under a unified diffusion formulation. Extensive experiments on conditional image generation tasks (e.g., style transfer, image super-resolution, and image deblurring) show consistent gains in fidelity and perceptual quality over representative task-specific and loss-guided baselines.
World models are rapidly becoming a core infrastructure for embodied intelligence and interactive agents: they provide controllable simulators in which agents can perceive, act, forecast, and acquire scalable experience. Yet current video generation world models are still organized around isolated control interfaces, such as camera trajectories, robot actions, or hand-joint signals. This fragmentation is increasingly a scaling bottleneck. The central challenge is not the absence of controllable generators, but the lack of a unified and extensible learning framework that can absorb heterogeneous action supervision while preserving a shared model of world dynamics. In this work, we introduce Worldscape-MoE, a Mixture-of-Experts world model built on Diffusion Transformers for scalable heterogeneous action control. Our key observation is that different controls specify different interfaces to the same underlying world: although their representations differ, they constrain shared physical regularities, scene dynamics, and interaction semantics. Worldscape-MoE operationalizes this observation through modality-aware control injection, shared and control-specific experts, and a progressive MoE tuning strategy that supports continual extension to new action modalities. Experiments across locomotion, robotic manipulation, and egocentric hand control show that heterogeneous supervision improves rather than interferes with individual control capabilities. Worldscape-MoE achieves strong results on WorldArena, improves locomotion and hand-control metrics, exhibits robust out-of-distribution generalization, and demonstrates scaling behavior as additional control data and experts are integrated.
Recent DESI observations have posed new challenges to $Λ$CDM, showing a preference for dynamical dark energy and yielding neutrino mass constraints within $Λ$CDM that approach the lower bound allowed by neutrino oscillation experiments. In this work, we investigate cosmological constraints on the key neutrino parameters, $\sum m_ν$ and $N_{\rm eff}$, within the Schwarzschild-de Sitter black-hole dark energy (SdSDE) framework. We use cosmic microwave background (CMB) data from Planck and ACT DR6, baryon acoustic oscillation data from DESI DR2, and type Ia supernova data from DES-Dovekie and PantheonPlus. We find that SdSDE scenarios prefer a positive neutrino mass whenever $\sum m_ν$ is allowed to vary. Using CMB+DESI+DES-Dovekie data, we obtain $\sum m_ν=0.207^{+0.047}_{-0.052}~{\rm eV}$ for SdSDE+$\sum m_ν$, reduced to $\sum m_ν=0.162^{+0.055}_{-0.056}~{\rm eV}$ when $N_{\rm eff}$ is also varied. This arises from the positive correlation between $N_{\rm eff}$ and $\sum m_ν$, together with the systematic preference of SdSDE for values of $N_{\rm eff}$ below the standard value. Furthermore, the best-fit $χ^2$ comparison shows that $Λ$CDM with extended neutrino parameters is strongly preferred over the corresponding SdSDE extension. Overall, the positive neutrino mass preference induced by SdSDE may reflect parameter compensation rather than an improved global fit, a possibility that should be further tested with future high-precision observational data.
G-computation is a powerful method for estimating unconditional treatment effects with covariate adjustment in randomized clinical trials. It typically relies on fitting canonical generalized linear models. However, this could be problematic when the sample size or event number is small relative to the number of covariates. Common issues include the underestimation of the variance and the potential nonexistence of maximum likelihood estimators. Bias reduction methods are commonly employed to address these issues, including Firth correction, which guarantees the existence of corresponding estimates. Yet, their application within g-computation remains underexplored. In this article, we analyze the asymptotic bias of g-computation estimators and propose a novel bias-reduction method that improves both estimation and inference. Our approach performs bias correction via generalized Oaxaca-Blinder estimators, and thus the resulting estimators are guaranteed to be bounded. The proposed debiased estimators use slightly modified versions of maximum likelihood or Firth correction estimators for nuisance parameters. We also introduce a simple small-sample bias adjustment for variance estimation to improve finite-sample inference validity. Through extensive simulations, we demonstrate that our proposed method offers superior finite-sample performance, effectively addressing the bias-efficiency tradeoff. Finally, we illustrate its practical utility by reanalyzing a completed randomized clinical trial. In this example, our method improves precision in a small subgroup analysis for which the standard method fails to fit the regression model.
Multimodal large language models (MLLMs) have demonstrated powerful capabilities in general spatial understanding and reasoning. However, their fine-grained spatial understanding and reasoning capabilities in complex urban scenarios have not received significant attention in the fields of both research and industry. To fill this gap, we focus primarily on road markings as a typical example of fine-grained spatial elements under urban scenarios, given the essential role of the integrated road traffic network they form within cities. Around road markings and urban traffic systems, we propose \textbf{RoadBench}, a systematic benchmark that comprehensively evaluates MLLMs' fine-grained spatial understanding and reasoning capabilities using Bird's-Eye View (BEV) and First-Person View (FPV) image inputs. This benchmark comprises eight tasks consisting of 3,040 strictly manually verified test cases, constructed from 2,137 unique BEV images and 721 unique FPV images collected from five Chinese cities with relatively consistent traffic conventions. These tasks form a systematic evaluation framework that bridges understanding at local spatial scopes to global reasoning. They not only test MLLMs' capabilities in recognition, joint understanding, and reasoning but also assess their ability to integrate image information with domain knowledge. After evaluating 20 mainstream MLLMs, we confirm that RoadBench is a challenging benchmark for MLLMs while revealing significant shortcomings in existing MLLMs' fine-grained spatial understanding and reasoning capabilities within urban scenarios. In certain tasks, their performance even falls short of simple rule-based or random selection baselines. These findings, along with RoadBench itself, will contribute to the comprehensive advancement of spatial understanding capabilities for MLLMs.