Searcharxiv⌕ Search

arXiv subjects

Zihao Wang

Publications and source records attributed to Zihao Wang.

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

Endpoint Riesz transforms on normalized graphs

For an infinite connected locally finite undirected graph with positive symmetric edge weights and normalized Laplacian $Δ$, we prove weak type $(1,1)$ estimates for two Riesz transforms. The edge transform has universal constant $2$, with no volume assumption. The vertex carré-du-champ transform has constant $H_1+1$ whenever $H_1=\sup_x m(B(x,1))/m(x)<\infty$. These endpoint constants apply to real-valued inputs and yield strong $\ell^p$ bounds for $1<p\le2$. The proof uses a discrete obstacle decomposition obtained by a contraction mapping, followed by elementary support estimates for the two gradients.

math.CA↗
arXiv2026

IndirectAD: Practical Data Poisoning Attacks against Recommender Systems for Item Promotion

Recommender systems play a central role in digital platforms by providing personalized content. They often use methods such as collaborative filtering and machine learning to accurately predict user preferences. Although these systems offer substantial benefits, they are vulnerable to security and privacy threats, especially data poisoning attacks. By inserting misleading data, attackers can manipulate recommendations for purposes ranging from boosting product visibility to shaping public opinion. Despite these risks, concerns are often downplayed because such attacks typically require controlling at least 1% of the platform's user base, a difficult task on large platforms. We tackle this issue by introducing the IndirectAD attack, inspired by Trojan attacks on machine learning. IndirectAD reduces the need for a high poisoning ratio through a trigger item that is easier to recommend to the target users. Rather than directly promoting a target item that does not match a user's interests, IndirectAD first promotes the trigger item, then transfers that advantage to the target item by creating co-occurrence data between them. This indirect strategy delivers a stronger promotion effect while using fewer controlled user accounts. Our extensive experiments on multiple datasets and recommender systems show that IndirectAD can cause noticeable impact with only 0.05% of the platform's user base. On public benchmark datasets, IndirectAD remains effective under very low poisoning ratios, suggesting that recommender-system poisoning may be more practical than previously assumed.

cs.CR↗
arXiv2026

Monet: Measuring the Ecosystem of Open-Source Text-to-Image Models Tailored for Harmful Services

The open-source text-to-image (T2I) ecosystem enables rapid model development and sharing, but also hosts models intentionally tailored for harmful services, which we call Monets. Prior work has examined specific types of harmful T2I models on individual platforms, but a Monet does not exist in isolation. The broader Monet ecosystem, spanning model characteristics, cross-platform propagation, governance evasion, monetization, and downstream deployment, remains poorly understood. In this study, we present the first systematic, ecosystem-level measurement of Monets. Grounded in the policies of real-world model hubs, we construct a taxonomy of ten harmful service categories and identify 23,947 Monets across eight major T2I model hubs, with the most popular exceeding 19 million downloads. While some developers employ anti-theft mechanisms against unauthorized re-uploading, Monets propagate across platforms at scale, with 40.76% mirrored across hubs. Such propagation further enables governance evasion via cross-platform archiving, keeping 11.99% of Monets accessible after bans on their original platforms, alongside other evasion strategies including keyword obfuscation and model-level safeguard circumvention. Monets also anchor coordinated commercial campaigns---one spanning 668 models with 914 completed commissions and another advertising gray-market account-farming service---and reach users through GitHub projects and inference APIs, raising downstream child safety concerns. These findings expose the limitations of platform-siloed defenses and highlight the need for cross-platform threat intelligence, coordinated governance, and technical safeguards.

cs.CR↗
arXiv2026

Electronic Reconstruction Towards Topological Superconductivity in FeTe

The recent discovery of intrinsic superconductivity in stoichiometric FeTe films has renewed interest in the Te-rich end member of the iron chalcogenides for studies of unconventional and topological superconductivity, yet its intrinsic electronic structure remains unresolved. In this work, we combine molecular beam epitaxy, angle-resolved photoemission spectroscopy (ARPES), electrical transport measurements, density functional theory, and embedded dynamical mean-field theory to track the electronic reconstruction of 20-unit-cell FeTe films as Te annealing progressively removes excess interstitial Fe and drives the system from an antiferromagnetic metal to a superconductor. We find that this evolution is accompanied by recovered quasiparticle coherence, reduced electronic correlations, a Lifshitz transition, and a topological phase transition, yielding dxy-dominated hole and electron pockets that favor inter-pocket scattering. In addition, a shallow dxz/dyz-derived hole band located about 2 meV below the Fermi level may provide an incipient-band pairing channel, while scattering between the two electron pockets at M may offer additional pairing channels. High-resolution polarization-dependent laser ARPES measurements further reveal a topological surface state whose circular dichroism is consistent with the expected orbital-angular-momentum texture of stoichiometric FeTe. These results establish the intrinsic low-energy electronic structure of superconducting FeTe and identify the electronic states most relevant to superconductivity. The coexistence of intrinsic superconductivity and a topological surface state establishes stoichiometric FeTe as a promising platform for exploring topological superconductivity and possible Majorana bound states.

cond-mat.supr-con↗
arXiv2026

Minimality and Nonlinear Spectral Rigidity of Hypersurfaces in Real Space Forms

We study nonlinear mean-curvature spectral equations for hypersurfaces in Euclidean, spherical and hyperbolic space. For an admissible analytic response $F$ and ambient sectional curvature $c$, we prove minimality when the spectral parameter satisfies $σ\ge nc$, and strict mean-curvature bounds at gradient points when $σ 1$, and positive mixtures satisfying an explicit width condition. In particular, the result proves Chen's conjecture in Euclidean codimension one and biharmonic minimality in constant negative curvature, without completeness or holonomicity. A common real-analytic continuation and focal-trace argument treats all three geometries. Explicit umbilical models show that the unconditional spectral minimality range is sharp. Independently, we prove quantitative local rigidity, modulo ambient isometries, of compact nonminimal geodesic spheres in all three geometries and proper equal-radius Clifford hypersurfaces in spheres, for smooth laws with $F,F'>0$ near the model amplitude.

math.DG↗
arXiv2026

Halo assembly bias in the early Universe: a clustering probe of the origin of the Little Red Dots

The clustering of galaxies encodes key information about the structure and assembly history of their host dark matter (DM) haloes, providing a powerful probe of the origin of extreme high-redshift systems. While halo assembly bias has been extensively studied at low redshift, its behavior in the early Universe remains poorly explored. Using the large-volume, high-resolution Shin-Uchuu cosmological $N$-body simulation, we characterize halo assembly bias associated with formation time, concentration, and angular momentum across a wide range of halo masses and redshifts. We find that the sign and amplitude of assembly bias depend on halo mass for both concentration and spin. High-concentration and low-spin haloes are more strongly clustered below characteristic peak heights of $ν\sim 1.5$ and $\sim 0.75$, respectively, while the trends weaken or reverse at higher masses. Halo age bias persists at all redshifts but decreases toward higher masses and earlier cosmic times. We apply these results to assess whether clustering can distinguish competing formation scenarios for the Little Red Dots (LRDs). We find that the direct-collapse-black-hole (DCBH) scenario predicts the strongest large-scale bias and enhanced pair fractions, the self-interacting-dark-matter (SIDM) core-collapse scenario and low-spin compact-galaxy scenarios yield weaker clustering due to lower characteristic halo masses and spin-related secondary bias, and a primordial-black-hole (PBH) scenario predicts unbiased clustering. Our results demonstrate that halo assembly bias and characteristic host masses provide powerful diagnostics for constraining the physical origin of LRDs, offering testable predictions for upcoming clustering measurements with JWST and future deep surveys.

astro-ph.CO↗
arXiv2026

TTE-Flash: Accelerating Reasoning-based Multimodal Representations via Think-Then-Embed Tokens

Recent research has demonstrated that Universal Multimodal Embedding (UME) benefits significantly from Chain-of-Thought (CoT) reasoning. In this paradigm, a generative model produces explicit reasoning traces for a multimodal query, with the final representation extracted from an embedding token attending to both the query and the reasoning. Despite its effectiveness, the computational overhead of generating explicit CoT traces is often prohibitive. In this work, we propose replacing explicit CoT with latent think tokens, which are interpreted as latent variables that can produce explicit CoT traces as observed variables. By optimizing think tokens using CoT generation loss and subsequent embedding tokens using contrastive loss, we produce high-performance, reasoning-aware representations at a constant inference cost. Our study investigates two key architectural designs: 1) how think and embeddings tokens should be extracted from the same LLM backbone. 2) how the tokens should be trained as two dependent tasks. We introduce TTE-Flash-2B, a reasoning-aware multimodal representation model that outperforms its explicit-CoT counterpart on the MMEB-v2 benchmark, while producing latent think tokens that are interpretable both textually and visually. Furthermore, zero-shot evaluation across 15 video datasets reveals scaling behavior as the number of think tokens increases.

cs.AI↗
arXiv2026

Collaborative Memory for Multi-Agent VLM Systems

Vision-language model (VLM) agents combine specialized perception, tools, and reasoning to address complex visual tasks. In multi-agent settings, different agents inspect different image regions, video frames, or visual representations, so collaboration extends beyond distributed reasoning to distributed perception. This makes shared visual context a central problem in VLM agent collaboration. In this paper, we frame memory hierarchy, cross-agent sharing, and consistency mechanisms around the need to reconcile interpretations and update dependent reasoning. Effective collaboration requires agents to build on contributions from other agents, recover missing visual context, and reconcile differing interpretations as new evidence emerges. Shared visual memory preserves not only images or textual summaries but also the dependencies among observations, agent interpretations, and subsequent reasoning. Together, these design considerations shape how information flows and evolves across VLM agents. The proposed framework provides a foundation for building reliable and resource-efficient agent teams.

cs.AI↗
arXiv2026

Strongly stable CMC-one hypersurfaces in every hyperbolic space of dimension at least four

For every integer $d\ge4$, we prove strong stability for a subfamily of classical rotational hypersurfaces in $\mathbb H^d$ with normalized mean curvature one. The examples are complete, two-sided, properly embedded, and nowhere umbilic, with topology $\mathbb {R}\times\mathbb{S}^{d-2}$. An explicit positive supersolution yields a quantitative stability inequality for all compactly supported test functions. Consequently, endpoint horospherical rigidity fails in every ambient dimension at least four.

math.DG↗
arXiv2026

Finite index constant mean curvature hypersurfaces in space forms of dimension at most seven

We study complete two-sided constant-mean-curvature hypersurfaces of dimensions $2\le n\le6$ and finite Morse index in simply connected space forms. In the round sphere the immersed domain is compact; in Euclidean space every noncompact example is minimal; in hyperbolic space of curvature $-1$ we obtain compactness under explicit dimension-dependent mean-curvature thresholds, from $H^2>1$ for surfaces to $H^2\ge5/3$ in dimension six. The conclusions also hold for the volume-constrained index, without properness, volume-growth, or curvature-bound assumptions. A common Green-function argument uses explicit rational combinations of eight identities and the stable Bernstein theorem of Hong-Li-Wang. Stable hyperbolic tubes show that no mean-curvature threshold independent of dimension can give compactness in all dimensions.

math.DG↗
arXiv2026

Backward SDEs-based Diffusion for Physics-Constrained Generation

Pretrained score-based diffusion models provide strong unconditional priors, yet enforcing measurement or physics consistency in inverse problems is often handled by heuristic guidance, intermittent projections, or task-specific conditional training, with limited guarantees of feasibility at the end of inference. We propose terminal-conditioned inversion for score-based SDE priors. Given a frozen Score-SDE prior and a task-defined terminal feasibility specification, we construct an associated backward stochastic differential equation whose adapted solution defines a principled inverse map from the terminal requirement to a prior state at a chosen noise level. Under standard regularity conditions, we establish existence and uniqueness of the adapted solution and obtain terminal consistency by construction. We further develop a practical neural BSDE solver that composes arbitrary pretrained diffusion priors with domain constraints without modifying the score-defined coefficients, producing an anchored prior state that enables neighborhood sampling for uncertainty characterization. Experiments on toy datasets validate stable terminal-conditioned inversion and distributionally consistent neighborhood sampling. As a real-world case study, we apply the framework to sparse-view CT reconstruction and achieve improved reconstruction quality over representative training-free baselines while satisfying strict measurement feasibility under the prescribed terminal specification. Project is available in: \href{https://laplacelab.github.io/BSDEDiffusion/}{https://laplace.center/icmlbsdeI/}

cs.LG↗
arXiv2026

Know When to Stop, Where to Restart: Accelerating Multi-Turn Agentic On-Policy Distillation

On-policy distillation (OPD) has become a standard approach for transferring capabilities from large teachers to compact students. Its cost, however, is dominated by autoregressive student rollouts and scales poorly in multi-turn agentic settings. Existing acceleration methods truncate or relocate the supervision signal according to fixed, offline budgets, despite substantial variation in teacher-signal reliability both within and across trajectories. Our empirical analysis on $τ^2$-bench reveals a clear structure in this variation: informative supervision is concentrated in the prefix of each turn, and, most importantly for multi-turn agentic training, the cross-turn loss of teacher endorsement is temporally locked to the student's first erroneous action rather than accumulating gradually over turns. Building on these findings, we propose STRIDE (Stop-and-Restart on-policy Distillation acceleration), which combines two complementary techniques: adaptive early stopping, which terminates a rollout once the cumulative teacher log-probability falls below an out-of-distribution threshold, and a prefix buffer, which caches high-quality prefixes and restarts generation at the weakest correct turn. Together, these mechanisms induce a data-driven curriculum that progressively extends coverage to later turns. On $τ^2$-bench retail, our method matches full-trajectory OPD and exceeds the 30B teacher at a $3.73\times$ speedup, surpasses the baseline itself at $2.34\times$, and retains a $4.51\times$ speedup under cross-domain multi-teacher training. As a supplementary generalization test beyond the agentic setting, STRIDE outperforms full OPD on AIME 2025 at a $5.10\times$ speedup and on AIME 2024 at a $3.08\times$ speedup; averaged across the two evaluations, both fixed-budget truncation baselines remain below full OPD.

cs.LG↗
arXiv2026

Band Renormalization in a Metal-Organic Framework/Au(111) Triple-Lattice Heterostructure

Two-dimensional conjugated metal-organic frameworks (2D c-MOFs) hold great promise for chemiresistive sensing, electrocatalysis, and energy storage. Their interfacial interactions with metal electrodes can significantly influence electronic properties and device functionality. As a representative 2D c-MOF, Ni3(HITP)2 (HITP = 2,3,6,7,10,11-hexaiminotriphenylene) exhibits excellent performance in various electronic devices, yet the microscopic mechanism of interfacial interactions in Ni3(HITP)2/metal heterostructures remains unclear. Here, we combine molecular beam epitaxy synthesis, scanning tunneling microscopic and spectroscopic characterization, and tight-binding analysis to investigate monolayer Ni3(HITP)2 epitaxially grown on Au(111). The resulting heterostructure forms a commensurate kagome-hexagonal-honeycomb triple-lattice architecture. Energy-dependent local density of states mapping resolves a ligand-derived state at 0.4 eV and dispersive bands dominated by Au surface states near the Fermi level, both of which are reproduced by the tight-binding model. These results demonstrate that the Au(111) substrate induces charge transfer, which in turn leads to Fermi-level pinning, while substrate-mediated hopping reconstructs the band structure. This atomic-scale study of Ni3(HITP)2/Au(111) epitaxial heterostructures elucidates interlayer coupling mechanisms and advances the understanding of MOF/metal interfaces that are integral to electronic and energy-storage devices.

cond-mat.mtrl-sci↗
arXiv2026

RCL-Mamba: A Dual-domain State Space Model for Measurement-oriented Image Restoration in Rotational Sparse-View Scanning Computed Laminography

Rotational Scanning Computed Laminography (RCL) is widely utilized for the Non-Destructive Testing (NDT) of large planar components. However, to facilitate rapid inspection, continuous sparse-view scanning is often employed, where the angular integration effect during exposure induces rotational blur in the projection domain. Furthermore, the data incompleteness inherent in sparse sampling manifests as sparse artifacts in the reconstructed image domain. To address these cross-domain degradations, this paper proposes RCL-Mamba, a measurement-oriented dual-domain State Space Model (SSM)-based image restoration network. The framework adopts a cascaded joint processing strategy: it first corrects the rotational blur in the projection domain and subsequently suppresses the sparse artifacts in the image domain. Additionally, we design a Mamba-CNN dual-branch module to adaptively balance large-scale blur correction with local detail recovery. Evaluations on both simulated datasets and real-world Printed Circuit Board (PCB) scans demonstrate that RCL-Mamba outperforms existing baselines in blur removal, artifact suppression, and structural preservation. Line-profile-based structural measurement further verifies that the proposed method better preserves via/pad boundaries and slender trace profiles. Crucially, by reducing the required scanning views from 512 to 64, our method enhances inspection efficiency by approximately 8-fold without compromising reconstruction quality, offering a robust measurement-oriented restoration solution for high-throughput RCL inspection with improved structural measurement fidelity.

cs.CV↗
arXiv2026

Signatures of nodal superconductivity in stoichiometric FeTe

Superconductivity in stoichiometric FeTe opens access to the FeTe endpoint of the Fe(Se,Te) phase diagram, yet the nature of its superconducting pairing state remains unresolved. In this work, we combine scanning superconducting quantum interference device (SQUID) microscopy, electrical transport, scanning tunneling microscopy and spectroscopy (STM/S), and mean-field calculations to investigate the local superfluid response and pairing state of FeTe thin films with tunable stoichiometry. Even in stoichiometric FeTe, we observe micrometer-scale spatial variations in both superfluid stiffness and superconducting transition temperature $T_c$, while the London penetration depth remains non-saturating down to 0.02$T_c$ and follows a power-law temperature dependence with an exponent of approximately 1-1.5. Together with a V-shaped low-energy density of states and two-gap modeling, these results indicate a superconducting state with gap nodes or deep minima, consistent with either a $d$-wave or nodal $s$-wave superconducting state. Our findings establish stoichiometric FeTe as a distinct superconducting regime that departs from the trend toward more isotropic gaps at intermediate Se/Te compositions, providing a new benchmark for modern microscopic theories of iron-chalcogenide superconductivity. Our work also reveals a crossover from weak to rapid suppression of $T_c$ as superfluid stiffness decreases, connecting FeTe to the broader phenomenology observed in unconventional superconductors.

cond-mat.supr-con↗
arXiv2026

Deeply Interleaved Text-Image Contexts for Multimodal LLMs Assessment

Current evaluations and training of multimodal models predominantly focus on multi-image tasks, largely overlooking interleaved text-image scenarios. In such multi-image tasks, text typically serves merely as task instructions, lacking deep semantic interaction with the visual content. In contrast, realworld applications like text-image co-creation, character tracking, and spatial reconstruction require constant interaction between text and images. Consequently, models must possess a deep understanding of these interleaved contexts. To bridge this gap, we introduce a novel benchmark, TIC-Bench (deeply interleaved Text-Image Contexts), designed to evaluate the capability of models to integrate text-image clues and recover the ground truth facts within deeply interleaved contexts. This benchmark encompasses three core domains: Logical, Temporal, and Spatial Association, which are further categorized into eight specific types, comprising a total of 2,280 questions. We evaluated 10 state-of-the-art MLLMs and observed a substantial performance gap compared to human experts, together with persistent difficulties in integrating evidence distributed across interleaved visual and textual inputs. Ultimately, this benchmark provides a valuable analytical tool for assessing and advancing the ability of multimodal models to effectively integrate text and image information in deeply interleaved contexts. TIC-Bench is publicly available at https://huggingface.co/datasets/pino10010/TIC-Bench

cs.CV↗
arXiv2026

CT-$Δ$Bench: A Benchmark for Longitudinal 3D Medical Imaging Difference Reporting with Vision-Language Models

In medical imaging, the clinical value of Computed Tomography (CT) lies not only in depicting current disease status, but crucially in enabling longitudinal comparison of serial scans to determine disease evolution, a process that underpins response assessment, recurrence detection, and ongoing patient management. Yet, despite this central role of temporal comparison in clinical decision-making, existing medical foundation models remain largely confined to single-study understanding, leaving temporally grounded cross-examination insufficiently addressed. To address this gap, we study longitudinal imaging difference reporting, a task in which a model takes two temporally separated scans from the same patient and generates a clinically meaningful report describing interval changes between them. We introduce CT-$Δ$Bench, a dedicated benchmark for this task with patient-level splitting to prevent information leakage. To better evaluate this task beyond surface-level text similarity, we further develop change-aware metrics specifically designed to capture clinically meaningful longitudinal changes, and conduct an independent physician validation to assess the reliability of the synthesized references and event extraction pipeline. We also compare direct paired-CT reasoning with an indirect two-stage pipeline that first generates single-timepoint reports and then performs textual differencing. Finally, we propose DeltaMed, a baseline model for direct paired-CT difference reporting, and train it on the benchmark training set. Together, these contributions lay the groundwork for temporally aware medical foundation models that better reflect real-world longitudinal clinical reasoning.

cs.CL↗
arXiv2026

Vitrification-Devitrification Enables Tunable Photonic and Gas Sorption Properties of Zeolitic Imidazolate Frameworks

Zeolitic imidazolate framework (ZIF) glasses represent an emerging family of melt-quenched glasses, which exhibit immense potential for applications in gas separation, energy storage, and optics. However, their intrinsic porosity remains elusive due to the inherent challenges in resolving their disordered atomistic structures. Here, we systematically investigate the porosity of ZIF-4 and ZIF-62 crystals and their corresponding glasses. CO2 sorption at 195 K enables quantitative assessment of microporosity in both crystalline and glassy states, allowing the accessible micropore volume of the ZIF glasses to be determined. Moreover, establishing a direct relationship between photonic properties and structural porosity in Zn-based ZIF glasses remains challenging. Here we demonstrate striking broadband blue-light emission from ZIF-4 glass annealed under optimized conditions. A pronounced red shift is observed when increasing the annealing temperature above the glass transition temperature. By correlating the evolution of photoluminescence with structural porosity, we reveal the interplay between the photonic and gas sorption properties of ZIF glasses. These findings provide new insights into the structure-property relationships of ZIF glasses and offer a pathway toward the rational design of multifunctional MOF glasses.

cond-mat.mtrl-sci↗