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Yong Zhang

Publications and source records attributed to Yong Zhang.

At least 19 recordsLinked to original sources
arXiv2026

From Intent to Execution Grant: An Execution-Boundary Conformance Profile for High-Risk AI Actions

AI agents increasingly propose actions with external consequences, including financial transfers, infrastructure changes, software deployments, disclosures, and physical actuation. Authorization engines, policy languages, runtime monitors, provenance mechanisms, and agent guardrails provide important foundations, but do not necessarily define a common semantic contract for the final transition from a particular candidate action to execution authority. We specify EBL-Core, an execution-boundary conformance profile for deciding whether one canonical, fully materialized AI-generated candidate may receive action-scoped execution authority under explicit conditions. It binds a structured intent object, Root and Operational Policies, evidence obligations, typed evidence, context, time, and a verifiable Decision Derivation through an Execution Release Contract (ERC). An ERC is not an authority-bearing token; a verified ALLOW ERC may support a separate Execution Grant governed by Redemption-time validation. EBL-Core specifies action binding, policy non-weakening, evidence handling, deterministic adjudication, derivation verification, and grant lifecycle behavior. An accompanying reference artifact provides schemas, adjudication, separate verification and Semantic Replay, and a linearizable in-memory grant store. In the retained run, 34 static vectors and 15 lifecycle checks matched expected outcomes. Across 100 trials, 32 concurrent Redemption attempts yielded exactly one successful Redemption and protected test effect per trial; 100 Revoke-Redeem races ended in valid terminal outcomes. These bounded results demonstrate executability of the specified subset, not human-intent correctness, evidence truth, complete mediation, production readiness, mechanized correctness, or deployment-level security.

cs.CR↗
arXiv2026

Fermionic hidden zeros

We uncover and prove a general class of hidden zeros in tree-level amplitudes with massless fermions. For arbitrary even numbers and arbitrary arrangements of massless real adjoint fermions and gluons, color-ordered gauge-theory amplitudes in $D=4,6,10$ vanish on general rectangular kinematic loci supplemented by species-dependent bridge conditions. The fermion--fermion bridge is governed simply by the vector current $\chi_i\gamma^\mu\chi_j$, while mixed and gluonic bridges are fixed by the corresponding gauge-covariant contractions. A worldsheet analysis proves the result at arbitrary multiplicity and traces its universality to the local fusion channels $ff\to g$, $fg/gf\to f$, and $gg\to g$, with no new primitive bridge structures appearing at higher fermion multiplicity. The same mechanism extends to ten-dimensional amplitudes with gravitons and arbitrary even numbers of gravitinos in factorized gamma-traceless polarizations, and to single-trace Einstein-Yang-Mills amplitudes with adjoint gluinos and gravitons. These results expose a finite local structure underlying fermionic hidden zeros across gauge and gravitational amplitudes.

hep-th↗
arXiv2026

Physics-Informed Error Field Learning: A Post-Training Optimization Framework for Physics-Informed Neural Networks

Physics-Informed Neural Networks (PINNs) have emerged as an important class of numerical methods for solving partial differential equations (PDEs). However, during the late-stage optimization process, further parameter updates often yield diminishing accuracy improvements while increasing computational costs. To address this issue, this paper proposes a Physics-Informed Error Field Learning (PIEFL) framework for PINNs. Unlike conventional approaches that continuously approximate the solution field using a single network, PIEFL introduces an auxiliary error network after the primary network achieves satisfactory accuracy and shifts the learning objective from the solution field to the error field. By deriving error control equations under physical constraints, the error network learns the discrepancy between the current approximation and the exact solution, and the learned error correction is combined with the primary prediction to improve solution accuracy. The proposed framework avoids continuous optimization of the entire solution space and focuses computational resources on correcting existing prediction errors. Moreover, PIEFL requires no modification to the primary network architecture, making it compatible with existing PINN models and applicable as a general post-training optimization strategy. Numerical experiments on representative PDEs demonstrate that PIEFL achieves higher solution accuracy under the same computational budget, validating its effectiveness in improving the performance of PINNs.

cs.LG↗
arXiv2026

The Complexity of Minimizing Subsidies in Envy-Free House Allocation

The house allocation problem is a classical one-sided matching problem that concerns the assignment of a set of $m$ houses to $n$ agents according to their preferences, where each agent is assigned exactly one house. Among the various objectives studied in this setting, envy-freeness is one of the most widely adopted fairness criteria. As envy-free house allocations do not always exist, we address this challenge by introducing subsidies and aim to compute allocations that achieve envy-freeness with minimum total subsidy. For binary instances, we show that a total subsidy of at most $(n-1)$ suffices to guarantee envy-freeness in house allocation, and this bound is tight. Building on the known NP-hardness for general utilities, we further show that computing an allocation that minimizes the total subsidy is NP-hard, even under binary utilities. However, when there are only a bounded number of types of agents with binary utilities, the problem can be solved in polynomial time. Finally, we present a polynomial time algorithm that computes the minimum subsidy required to achieve envy-freeness for two types of agents with general utilities.

cs.GT↗
arXiv2026

Multi-stage neural operator learning with application for convolutions

Convolution integrals widely exist in applications, and to enable fast and accurate computations, this paper introduces two general multi-stage neural operator learning frameworks. The first, Deep Collocation Neural Operator (DCNO), is a supervised approach that iteratively refines the operator approximation by learning residuals from input-output data pairs. The second, Deep Galerkin Neural Operator (DGNO), is an unsupervised framework applicable when the target operator can be represented by a PDE, leveraging the weak form of the PDE residual for training. Both methods progressively construct basis operators through multiple training stages to enrich the approximation space, leading to significantly improved accuracy over standard one-shot operator learning. We provide theoretical analysis for their approximation capabilities and implement them for learning convolutions. Extensive numerical experiments demonstrate that both DCNO and DGNO achieve high accuracy, approaching machine precision under single float for convolution problems, and offer substantial efficiency gains for numerous queries or parametric variations compared to traditional solvers. We also extend these frameworks to handle multi-input operator learning scenarios involving variations in both the density and kernel of a convolution.

cs.LG↗
arXiv2026

Who Can Make the Action Happen? An Authority-Decomposition Framework for High-Risk Automated Systems

High-risk automated systems distribute control across services, credentials, protected components, and lifecycle mechanisms. Labels such as authorized, approved, privileged, or protected therefore do not answer a basic causal question: which actors can actually make a consequential action occur? This paper provides an action-relative method for deriving which trust-domain coalitions are sufficient to cause protected execution, defined as the occurrence of a designated protected state transition. The framework models components, powers, resources, boundaries, and alternative realization structures; includes update, recovery, override, disablement, and alternative invocation; and separates causal control over execution from control over the authoritative account of an operation. It derives inclusion-minimal sufficient coalitions and tests whether claimed execution boundaries remain independent of designated upstream domains. Cross-domain analytical cases illustrate the method. In a split-control, release-intended, open-state, source-bounded Havenlon protocol model, the ordinary witness requires five trust domains, while certificate replacement yields a three-domain inclusion-minimal known requirement set among source-enumerated protocol witnesses; the Linux domain remains insufficient for the complete transition. Deployed global non-bypassability and boundary-bound veto coverage remain unresolved. The framework is a conceptual and analytical tool. It does not certify implementations, establish deployment security, guarantee complete discovery of hidden powers, or define evidence-verification semantics.

cs.CR↗
arXiv2026

Boosting Knowledge-based Visual Question Answering with Structured Context Reasoning

Knowledge-based Visual Question Answering aims to answer questions about an image by integrating external knowledge with visual and textual information. Recent approaches often rely on in-context learning to prompt Large Language Models (LLMs) with multimodal context in a zero-shot or few-shot manner. However, we observe that directly concatenating heterogeneous visual descriptions and retrieved knowledge into long, unstructured prompts often degrades reasoning performance, due to both excessive irrelevant context and the lack of explicit relational structure. In this paper, we propose an LLM-based Structured Context Reasoning (SCoRe) framework that infers both explicit and implicit relationships for prediction. SCoRe consists of three stages: Context Acquisition, which generates diverse visual notes and retrieves explicit knowledge via an efficient two-stage multimodal retrieval strategy; Context Selection, which filters relevant visual, explicit, and implicit knowledge using LLM-guided selection; and Context Compression, which performs Relational Logic Distillation (RLD) to transform raw text into explicit entity-relation triplets. These relational triplets serve as a concise and structured prompt for final answer prediction. Extensive experiments on the OK-VQA and A-OKVQA benchmarks demonstrate that SCoRe consistently outperforms state-of-the-art methods.

cs.CV↗
arXiv2026

Equilibrium Distributions for Strongly Nonlinear Many-Body Systems

Obtaining equilibrium distributions of nonlinear systems is essential for accurately computing macroscopic observables. Conventional theoretical corrections are typically limited to weak nonlinearities, where interaction terms can be treated as effectively uncorrelated perturbations and the random phase approximation applies. In this Letter, we develop a framework to determine equilibrium distributions based on the generalized energy equipartition principle. Our approach recovers existing corrections in the weakly nonlinear regime and, crucially, remains valid for strong nonlinearities, where perturbative contributions become correlated and conventional approaches break down. Numerical simulations of the nonlinear Schr\"odinger equation, the Majda-McLaughlin-Tabak model, and the Fermi-Pasta-Ulam-Tsingou model demonstrate accurate corrections for nonlinearities more than an order of magnitude stronger than those accessible to conventional theories.

cond-mat.stat-mech↗
arXiv2026

HI envelope around the carbon star V420 Vul

We report the detection of an extended 21-cm parsec-scale \ion{H}{i} structure toward the Mira variable V420\,Vul using archival Galactic Arecibo L-band Feed Array survey data. The emission exhibits a spatially coherent but intensity-asymmetric morphology that nevertheless retains a globally symmetric kinematic profile centered near $v_{\mathrm{LSR}} \sim 47.6\,\mathrm{km\,s^{-1}}$. At an adopted distance of $\sim 1.9$\,kpc, the structure extends over $\sim 20$\,pc, implying a dynamical timescale of order $10^6$\,yr. Although the total \ion{H}{i} mass ($\sim 70\,\mathrm{M_\sun}$) indicates that the atomic gas reservoir is heavily dominated by the ambient interstellar medium rather than pristine stellar ejecta, the spatially resolved spectra and position-velocity diagrams reveal an underlying symmetric velocity framework centered on the star. We interpret this as the dynamical imprint of the stellar wind preferentially channeling through a porous ambient cloud, demonstrating that cohesive kinematic records of late-stage stellar mass loss can be preserved over parsec scales and megayear timescales despite dominant interstellar coupling.

astro-ph.SR↗
arXiv2026

PDRs4All XXII. Near-Infrared continuum in the Orion Bar

Conspicuous excess emission is present in the near-infrared (NIR) region in various objects, including reflection nebulae, planetary nebulae, and nearby galaxies. However, the spatial distribution and spectral shape of the excess emission remain poorly understood. We studied the NIR continuum emission spectroscopically and obtained its spatial distribution relative to the aromatic infrared band (AIB) at 3.3um in the Orion Bar prototypical photodissociation region (PDR). We aim to characterize its spectral shape and discuss its origin. We employed 3D spectroscopic data of the Orion Bar taken with the integrated field unit of NIRSpec on JWST from the Early Release Science program "PDRs4All." Contribution from the foreground ionized gas was estimated using the Cloudy code and subtracted. The observed regions were divided into nine physically distinct regions and an average spectrum was derived for each region. The nine regions, including the ionized gas, atomic PDR, and molecular PDR, clearly show remaining continuum in the region 1--4.5um. The continuum at wavelengths longer than 2.7um shows good correlations with the 3.3um AIB, while the correlation of the continuum at 1.2um is not significant. We further find that the NIR continuum in the Orion Bar can be approximated by a summation of two blackbodies. The low-temperature component correlates with the AIB well, while the high-temperature component does not. The average spectra also show absorption features at 3.0 and 4.27um, which are attributed to the presence in the spectra of water ice and CO2 ice. We discuss possible origins of the NIR continuum, among which recurrent fluorescence from carbon clusters better explains the observed low-temperature component. The presence of ice species suggests a contribution from a deeper layer of the PDR along the line of sight producing characteristic ice absorption features.

astro-ph.GA↗
arXiv2026

Exact Resonances Are Not Sufficient for Phonon Energy Diffusion

Multi-phonon resonance conditions underpin kinetic theories of phonon transport and lattice thermalization. We show that exact resonance matching, nonzero interaction coefficients, and network connectivity do not guarantee persistent energy diffusion. Symmetry-enforced balance relations drive exact-resonant collision currents to nonthermal zero-flux states, producing kinetic arrest from individual resonant sets to connected networks. Complete energy spreading is sustained by quasi-resonances. The thermodynamic and weak-nonlinearity limits do not commute: the leading kinetic behavior is recovered in the former, whereas at fixed finite size the thermalization time diverges through higher-order crossovers as the nonlinearity vanishes. Exact-resonance existence and connectivity are therefore kinematic, not sufficient dynamical, criteria for phonon energy diffusion.

cond-mat.stat-mech↗
arXiv2026

Bounds on Odd and Odd-Even Induced Subgraphs

Let $G$ be an $n$-vertex graph and let $\ell:V(G)\to\mathbb{F}_2$ prescribe degree parities. A set $S\subseteq V(G)$ is $\ell$-admissible if every $v\in S$ has degree congruent to $\ell(v)$ modulo $2$ in $G[S]$. Let $h_\ell(G)$ be the maximum order of an $\ell$-admissible set, set $f_{\mathrm{oe}}(G):=\min_\ell h_\ell(G)$, and write $f_o(G):=h_{\mathbf{1}}(G)$, where $\mathbf{1}(v)=1$ for every $v\in V(G).$ We prove three main results for graphs without isolated vertices. First, by extending Zeng's odd-cut method to arbitrary parity prescriptions an introducing a one-sided completion lemma, we show that $h_\ell(G)\ge n/6$ for every $\ell$. Consequently, $f_{\mathrm{oe}}(G)\ge n/6$, improving the previous bound $2n/21$. Second, for bipartite graphs we derive lower bounds on $f_o(G)$ in terms of the $\mathbb{F}_2$-rank of the bipartite adjacency matrix and combine them to obtain \[ f_o(G)\ge \left(\frac14+\frac1{256}\right)n=\frac{65}{256}n. \] Thus, in the bipartite case, the factor $2$ in Scott's bound $f_o(G)\ge n/(2\chi(G))$ can be replaced by $128/65<2$. Finally, writing $\alpha=\alpha(G)$, a fourth-moment argument gives, for $\alpha\ge2$, \[ f_o(G)\ge \frac{\alpha}{2}+\frac{\log_3\alpha}{8} -\frac14\log_3\log_3\sqrt{\alpha}. \] We also construct bipartite graphs satisfying \[ f_o(G)\le \frac{\alpha(G)}2+\log_2\!\bigl(\alpha(G)+1\bigr)+\frac12, \] showing that the logarithmic additive improvement over Scott's bound $f_o(G)\ge\alpha(G)/2$ has the optimal order of magnitude.

math.CO↗
arXiv2026

Anomalous enhancement of thermal radiation transport by quasidisorder

The transition from order to disorder is conventionally regarded as detrimental to solid-state heat transfer in classical wave and quasiparticle systems. In striking contrast, we show that in near-field thermal radiation, breaking long-range order-shifting from periodic to quasiperiodic configurations-induces a counterintuitive enhancement of energy transport. This effect arises from delocalized interactions within quasiperiodic elements, where this quasiperiodicity relays and amplifies thermal electromagnetic energy transfer across large spatial separations, surpassing even corresponding near-field scenarios. The extraordinary transport properties induced by quasidisorder effect of near-field thermal radiation could unlock exciting opportunities for heat flow manipulation, offering transformative implications for thermal science and advancing the fundamental understanding of collective excitations in non-ordered systems.

physics.app-ph↗
arXiv2026

Interferometric molecular line observations toward the 21 {\mu}m protoplanetary nebula IRAS 06530-0213

The identification of the $21\,\mu\mathrm{m}$ feature in some protoplanetary nebulae remains a longstanding puzzle, whose interpretation requires characterization of the molecular gas environments of associated sources. Here, we present high-resolution interferometric observations from the Northern Extended Millimeter Array toward the $21\,\mu\mathrm{m}$ protoplanetary nebula IRAS 06530-0213. Multiple molecular transitions of $\mathrm{HC_3N}$, $\mathrm{C_4H}$, and $\mathrm{SiC_2}$ are detected and spatially resolved. We analyze these spectral lines alongside previously reported CO and $^{13}\mathrm{CO}$ emission toward this source. Differing from the extended barrel-like molecular structure traced by CO and $^{13}\mathrm{CO}$, $\mathrm{HC_3N}$ and $\mathrm{C_4H}$ are predominantly concentrated in the inner low-latitude equatorial zones. Reproducing the observed morphology requires $\mathrm{HC_3N}$ to possess a more inwardly concentrated abundance distribution compared with CO and $^{13}\mathrm{CO}$, implying that the inner equatorial regions provide favorable conditions for carbon-chain chemistry.

astro-ph.GA↗
arXiv2026

Non-volatile integrated photonics on lithium tantalate-on-insulator

Scalable reconfigurable photonic integrated circuits require low-loss, high-speed optical control without continuous holding power. Yet widely used thermo-optic tuning and continuously biased electro-optic tuning consume static power and introduce thermal crosstalk or bias drift. Here we demonstrate a monolithic non-volatile photonics platform on lithium tantalate-on-insulator (LTOI). In congruent x-cut lithium tantalate, the switched ferroelectric-domain configuration is retained after the write field is removed. On the same LTOI platform, we demonstrate a waveguide propagation loss of approximately 0.05-0.06 dB/cm and multilevel non-volatile phase tuning in separate devices. The programmed states remain distinguishable through $10^{6}$ write cycles. Weighted segmented electrodes resolve 137 phase positions across a $\pi$ range, corresponding to an analogue phase-setting resolution of approximately $0.007\pi$. We further combine non-volatile phase control with high-speed electro-optic modulation to achieve zero-static-power bias control of a >110 GHz modulator and a 59.3 dB extinction ratio after non-volatile trimming. At the system level, an image-edge-detection chip achieves a measured energy efficiency of 3.48 TOPS/W. These results establish LTOI as an integrated photonics platform that combines persistent optical reconfigurability with low-loss routing and high-speed electro-optic modulation.

physics.optics↗
arXiv2026

Leveraging Raman response in X-cut thin-film lithium tantalate for ultrabroadband combs and polychromatic visible light

X-cut thin-film lithium tantalate (TFLT) offers a unique combination of third nonlinearity, electro-optic effects, and a high optical damage threshold. However, its strong Raman response has historically hindered broadband Kerr comb generation. Here, we leverage this inherent Raman response by engineering coupling-defined dissipation. This allows us to reconfigure the relative thresholds of Raman and Kerr processes without modifying the intrinsic microresonator dispersion. Through this coupling-engineered threshold control, we can deliberately access distinct comb states, ranging from pure Kerr combs to Raman-Kerr synergistic broadband combs. We demonstrate a Kerr comb spanning 450 nm and a Raman-Kerr comb spanning 650 nm, representing the broadest combs reported to date on X-cut TFLT platforms. Moreover, in strongly coupled devices, we show that a single near-infrared pump can generate visible emission across multiple bands (from violet to red) via cascaded second sum-frequency processes. Our work demonstrates that a strong Raman response can be transformed from a parasitic competitor into an enabling mechanism for achieving broader comb spectra and generating polychromatic visible light. This work establishes X-cut TFLT as a powerful monolithic platform for nonlinear light sources, electro-optic functions, and complex photonic systems.

physics.optics↗
arXiv2026

DiffLens: A Visualization System to Explore Local Differences in Graph Sampling

Graph sampling techniques have been widely used to simplify network computation and visualization, which also results in inevitable differences between the sampled networks and the original networks in terms of nodes, edges and structures. Investigating such differences can inform graph sampling technique users of the pros and cons of different techniques and select the appropriate one, and can also help graph sampling developers evaluate their own technique. However, there are still no systematic ways to achieve such a goal. This paper fills this research gap by first proposing systematic and generic quantitative measures to quantify three categories of graph differences (i.e., neighbor-based, path-based, and structure-based). Built upon this, we further propose DiffLens, a novel visualization system to help graph sampling developers and users intuitively explore local differences at different regions of their interest within a sampled graph, where three new lens-based visual designs are presented to display the neighbor-based, path-based, and structure-based differences respectively. We conducted two case studies and a user study using real-world network datasets to evaluate DiffLens. The results confirmed its effectiveness and usability in helping users explore local differences and compare different graph sampling strategies.

cs.HC↗
arXiv2026

OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators

We propose OPSD-V, an on-policy self-distillation paradigm for post-training few-step autoregressive (AR) video diffusion models. Existing few-step AR video generators can produce long videos with low latency, but still suffer from error accumulation and weakened motion dynamics during long autoregressive rollout. OPSD-V reduces long-horizon degradation while preserving the original few-step inference path. The key idea is to introduce real long-video data as temporal context during training and use it to provide dense trajectory-level supervision. Specifically, the student follows the exact inference-time rollout, generating each chunk conditioned on its own previously generated KV cache. In parallel, the teacher is evaluated at the same student-visited denoising states, but uses a cleaner AR-consistent temporal cache in which older history can be replaced by real-video context. This provides dense denoising-level corrective targets under on-policy AR cache dynamics, without changing the sampler, number of denoising steps, or inference-time cache mechanism. We apply OPSD-V to representative few-step AR video models, including Self-Forcing and LongLive. Experiments show consistent improvements in visual quality, motion dynamics, and VBenchLong scores. A user study with 10 participants comparing 20 video pairs shows that OPSD-V is preferred over the base models in 66.0% of overall-preference judgments (82.5% excluding ties).

cs.CV↗