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Zheng Gong

Publications and source records attributed to Zheng Gong.

At least 19 recordsLinked to original sources
arXiv2026

Axion Emission Controlled by Ion Motion in Relativistic Beam-Plasma Filamentation

The interaction between relativistic electron beams and plasma can generate magnetic fields with strengths of $\sim 10^4$ T far exceeding conventional magnets, which offers a promising environment for producing weakly coupled particles. Using particle-in-cell simulations incorporating an axion-emission model, we show that a 10-GeV electron beam traversing dense plasma emits a forward-collimated axion beam during the ion-mediated nonlinear stage of current filamentation instability. Although the electron-driven linear instability seeds the filaments, ion motion governs their subsequent expansion and coalescence. This evolution reinjects cold-core electrons into large-amplitude orbits in phase space, where they repeatedly cross the strong-field flanks of neighboring magnetic vortices, thereby enhancing axion emission. At a background-plasma density of $\sim 10^{21}\mathrm{cm}^{-3}$, suppressing ion motion reduces the axion yield by a factor of 14.5. Our results demonstrate that ion dynamics play a crucial role in characterizing the emission of weakly coupled particles induced by relativistic filamentation instabilities, suggesting a new beam-driven method for probing the axion-electron interaction at accelerators.

hep-ph↗
arXiv2026

State-Dependent Objective Coordination for End-to-End Balance and Navigation

Traditional robotic navigation systems separate mapping, planning, and low-level control through cascaded modules, introducing computational overhead and accumulated latency. End-to-end reinforcement learning offers a compact unified policy that maps onboard observations directly to control actions. However, on inherently unstable platforms such as two-wheeled inverted-pendulum robots, navigation progress and obstacle avoidance can conflict with attitude stabilization in a strongly state-dependent manner. We propose a lightweight state-dependent objective coordination method for integrated navigation and control. A pitch-conditioned scalarization coefficient $β(s)\in[0,1]$ adjusts the relative contributions of balance stabilization and navigation objectives during training, without extra control hierarchies or auxiliary attitude-stabilization controllers, such as proportional-integral-derivative (PID) or linear-quadratic regulator (LQR) controllers. To support zero-shot deployment, simulation training models execution delay, action hold, asynchronous and noisy observations, and dynamics uncertainty. Gradient analysis reveals persistent directional interference and state-dependent variation in the objectives' relative scales. Comparisons with alternative reward-weighting, constraint, training, and control-structure strategies show that PPO+$β(s)$, where PPO denotes proximal policy optimization, achieves 98% and 95% success rates with small attitude excursions on the fixed obstacle map and the randomly generated high-obstacle-density map, respectively. Zero-shot hardware experiments further demonstrate bounded attitude behavior and consistent obstacle avoidance using only onboard observations. Training and evaluation code will be made publicly available upon acceptance.

cs.RO↗
arXiv2026

Beam filamentation instability drives deuterium-tritium fusion with polarized neutron emission

Filamentation instabilities are generally regarded as detrimental to fast-electron transport in fusion plasmas because they increase beam divergence and redistribute deposited energy. Here, we use two-dimensional particle-in-cell simulations coupled to a spin-dependent deuterium-tritium fusion module to investigate whether filamentation can instead transfer energy from counterstreaming electrons to prepolarized fusion ions. The simulations show that magnetic filaments, together with longitudinal inductive and transverse charge-separation electric fields, accelerate initially stationary deuterons and tritons to energies at which fusion reactions occur. Over the parameter range examined, the calculated neutron yield increases with the saturated magnetic-field energy. The model further predicts anisotropic, spin-resolved neutron emission. The direction of maximum neutron polarization is approximately perpendicular to the dominant deuterium-tritium collision direction and evolves with the angular distribution of the reacting ions. These results suggest that beam filamentation can couple relativistic-electron energy to fusion ions and that polarized neutron emission may provide a reaction-weighted signature of the underlying plasma dynamics.

physics.plasm-ph↗
arXiv2026

Radiation-induced electron spin polarization in ultrarelativistic kinetic turbulence

Electron spin polarization in radiative plasmas with ultrarelativistic kinetic turbulence under highly magnetized conditions is investigated using particle-in-cell simulations. We observe that a significant spin polarization can be sustained when the leptons undergo energetic photon emission accompanied by spin flips during the nonequilibrium turbulent evolution. By analyzing the time evolution of spatially dependent spin polarization, we identify an electromagnetic (EM) regime of kinetic turbulence, distinct from the well-known density-dominated regime characterized by vortex currents and magnetic islands. While in the latter regime the spin polarization exists only transiently, in the EM regime significant anisotropic net polarization emerges and persists in non-dissipative scenarios. The correlation between spin signals and turbulence features is leveraged to introduce the characteristic parameter delimiting the EM regime via the ratio of electric and magnetic energy densities and to gain insight into complex plasma turbulence. This study demonstrates the versatility of a spin-resolved study of the plasma turbulence in extreme environments, such as black holes and magnetar magnetospheres.

physics.plasm-ph↗
arXiv2026

Anisotropy and energy distribution of leptons and photons in radiative relativistic Alfvénic turbulence

The anisotropic evolution of relativistic particles in magnetically dominated Alfvénic turbulence is a fundamental process governing nonthermal emissions in high-energy astrophysical environments. In this work, we develop a theoretical framework by deriving scaling equations that characterize the pitch-angle dynamics of leptons and photons across both weak- and strong-radiative cooling regimes. Our model reveals a universal coupling between particle energy and angular distribution, arising from the interplay of field-aligned accelerations, perpendicular drift motions, and radiation reactions. The model can uniquely identify ``turning-point energies" that exhibit a nonlinear dependence on the local magnetic field and the energy-injection scale. The validity of this scaling model is confirmed through radiative particle-in-cell simulations. We show that the electron pitch-angle distribution fundamentally dictates photon emission characteristics, further revealing a pronounced statistical correlation between high-energy photon emission and turbulent vortices in the reconnection regions. By bridging the gap between small-scale kinetic interactions and broad-band spectral characteristics, this work provides a quantitative basis for interpreting the hardening of radiation spectra and complex polarization signatures in sources such as pulsar wind nebulae and gamma-ray bursts.

physics.plasm-ph↗
arXiv2026

Magnetic island structures in relativistic laser-driven plasma channels

We develop a theoretical model for self-generated magnetic islands in relativistic laser-driven channels in near-critical-density plasmas. The islands arise from the nonlinear superposition of the quasi-static magnetic fields generated by the longitudinal channel current $j_x$ and the laser-front driven transverse current $j_y$. By deriving the critical conditions among laser depletion, transversely symmetric channel formation, and magnetic-island formation, we identify the laser-plasma parameter window in which the magnetic island structures can exist. Within this window, the balance between the laser ponderomotive force and the charge-separation force, expressed through an effec tive electron density $n_\mathrm{eff}$, determines the transverse island width $H$, whereas the mismatch between the laser group and phase velocities determines the longitudinal period $L$. Large-scale particle-in-cell simulations over a broad range of laser intensities and plasma densities validate the resulting scaling laws. The model turns the island geometry from a qualitative feature of the channel field into a predictable quantity, providing a basis for tailoring electron transport, particle acceleration, high-energy radiation, and novel fusion ignition schemes in relativistic laser-plasma interactions.

physics.plasm-ph↗
arXiv2026

mRNA Design and Optimization with Deep Knowledge-Infused Approach

The mRNA optimization is essential for mRNA vaccines, therapies, and industrial protein production. Based on current explorations, an ideal optimization approach should simultaneously (i) prevent unintended amino-acid changes, (ii) optimize multiple, biologically relevant objectives, and (iii) retain computational efficiency. However, existing methods are forced to trade off between these perspectives, forming an "impossible triangle." We present RNop, a knowledge-infused Transformer that integrates mechanism-aligned losses to address this problem. By encoding biological prior knowledge in losses, RNop makes knowledge infusion explicit and controllable across optimization focus. Trained on over 6 million sequences, in silico analyses show RNop resolves the "impossible triangle" of mRNA optimization with absolute sequence fidelity, significantly improved biological metrics, and high throughput. In in vitro validation, it can deliver up to 2.28-fold expression gain. Ablation studies reveal how each prior contributes to targeted improvements, yielding mechanism-level interpretability. RNop represents a shift in mRNA optimization methodology: by infusing explicit and interpretable knowledge, the "black-box" mRNA design can be transformed into a predictable, explainable engineering problem. RNop is designed as an extensible platform: additional biological priors can be incorporated as modular, mechanism-aligned loss functions, enabling future development and adaptation to related sequence design problems.

q-bio.QM↗
arXiv2026

Wavefront-Guided Electron Injection for Direct Laser Acceleration in Relativistic Laser-Driven Plasma Channel

We investigate electron injection into direct laser acceleration (DLA) in relativistic laser-driven plasma channels using particle-in-cell simulations. We identify and characterize a wavefront-guided injection mechanism, in which electrons are continuously fed into the plasma channel through the density pile-up layer at the laser-pulse front. Phase-space analysis reveals a localized injectable region within the pile-up layer, indicating that only a selected subset of electrons satisfies the conditions required for the subsequent direct laser acceleration. This mechanism provides a physical interpretation for the high-charge capability of DLA by explaining how electrons are continuously supplied to the accelerating channel. Beyond this continuous supply process, the injection dynamics are further modulated by the periodic variation of the carrier phase at the laser-pulse front. The spatial locations of injected electrons are found to be closely associated with magnetic-island structures formed under laser-phase modulation, suggesting that the laser wavefront not only supplies electrons but also organizes their entry into the accelerating channel. These findings advance the physical understanding of energetic-electron generation in relativistic laser-driven subcritical-density plasma channels and are relevant to the development of compact DLA-based particle and radiation sources.

physics.plasm-ph↗
arXiv2026

Brewster-anomaly delocalization for free-electron radiation

Localization effects are central to disordered electronics and photonics. In electronics, Anderson localization governs electron confinement in randomly perturbed lattices. Similarly, its photonic counterpart inhibits light transport via disorder -- but with a unique exception: Brewster-anomaly delocalization, where the Brewster effect prevents multiple-scattering interference and counteracts the localization. Despite extensive research in electronics and photonics separately, the intricate role of localization effects in free-electron--light interactions -- vital for lasers, accelerators, microscopy and spectroscopy, and quantum information -- remains largely unexplored. At the same time, localization effects are widely regarded as a key factor limiting the efficient coupling between free electrons and light in random media. Here we overcome this key limitation via the unconventional interplay between Brewster-anomaly delocalization and free-electron radiation. In this way, free-electron radiation can be localization-free, intense and directional even in strongly disordered, unengineered multilayers. Essentially, this delocalization-mediated free-electron radiation is remarkably invariant not only to the random-medium configuration, but also to the light frequency and the electron velocity. Our findings unlock new opportunities for particle detectors and achromatic light sources operating in easy-to-fabricate complex media at previously inaccessible frequencies.

physics.optics↗
arXiv2026

Distance to Class Prototypes: Active Learning for Object Detection

Deploying a deep object detector in a new setting is limited less by architecture than by the cost of annotating data from that setting. Active learning lowers the cost by choosing which images to label, and the choice is only as good as the signal used to score an unlabeled image. That signal is usually the class posterior, which is cheap but poorly calibrated, or the disagreement across several models or several stochastic passes, which is better but multiplies inference over a pool far larger than the labeled set. We propose a signal richer than the posterior yet still read from one forward pass of one network. A supervised contrastive term added to the training objective shapes a per-object embedding space in which distance encodes class membership, and an unlabeled detection is scored by how far it lies from the region occupied by its predicted category, weighted by its confidence. The criterion needs no ensemble, no auxiliary predictor and no repeated inference, and its entire cost is 2.89M parameters, an increase of 8.3% over a bare detector. On PASCAL VOC and MS-COCO it beats the posterior of the same detector in every round in which a selection is made, by up to 1.08% mAP50 against run to run deviations of 0.02% to 0.18%, and it stays competitive with ensemble and Monte Carlo dropout criteria costing three to fifty forward passes per unlabeled image. Experiments use the single-stage detector under which the compared criteria report their results, so that the selection decision is isolated from the strength of the detector.

cs.CV↗
arXiv2026

Generative Verification: Rethinking the Uncertainty Signal for Active Learning of Object Detection

Nearly every acquisition function for active object detection shares one arrangement, in that the model being improved is also the model being interrogated. We depart from it. In generative verification an independent generative model re-derives the label of a detection from the pixels inside its predicted box, and the disagreement between the two becomes the acquisition signal. Two properties follow from the arrangement itself rather than from any tuning. A displaced box, a box on background and a correct box carrying the wrong label all yield a crop that fails verification, so the failure modes arrive already combined in one scalar and the hand-weighted classification and localization terms of existing criteria are no longer needed. And because the verifier never observes the detector confidence, confidently wrong detections score highest, although a self-derived signal reads them as uninteresting and they are the costliest to leave unlabeled. We build the verifier as a conditional diffusion model whose diffusion target is a label representation rather than an image. Its reverse process is stochastic, so repeated generations return a distribution whose concentration reports how firmly the evidence determines the label, where a classifier returns a single point estimate. On PASCAL VOC and MS-COCO the signal outperforms output-uncertainty, feature-geometry, perturbation and ensemble criteria, gaining about one mAP50 point per round on MS-COCO, with its largest margins in the early rounds where confident detector errors are most common.

cs.CV↗
arXiv2026

EyeMVP: OCT-Informed Fundus Representation Learning via Paired CFP--OCT Pretraining

Color fundus photography (CFP) is the mainstay of large-scale retinal screening, but its diagnostic capacity is limited by the lack of depth-resolved structure, which optical coherence tomography (OCT) provides yet is less accessible at population scale. We present EyeMVP, a cross-modal retinal foundation model that uses paired CFP--OCT pretraining to learn OCT-informed CFP representations while requiring only CFP at inference. Pretrained on 674,893 same-eye same-day CFP--OCT triples from 112,642 patients across eight hospitals, EyeMVP uses cross-modal masked reconstruction to enrich CFP features with OCT-associated supervision, and combines source-constrained cross-attention with CFP-derived structural masks to accommodate the non-aligned geometry of en-face CFP and cross-sectional OCT. Across 15 dataset-level settings spanning classification and segmentation, under both full-data and few-shot regimes, EyeMVP performs on par with or better than representative retinal foundation models, with consistent gains on macular and optic-nerve tasks; it attains AUROCs of 0.923 for macular edema and 0.867 for myopic macular schisis, two conditions poorly resolved in CFP. In an exploratory reader study, EyeMVP surpasses junior and intermediate ophthalmologists but not seniors on macular edema, while exceeding all groups on myopic macular schisis. These results indicate that cross-modal reconstruction can enrich CFP representations with OCT-associated supervision, offering a practical route to stronger CFP-based screening.

cs.CV↗
arXiv2026

Electron penetration heating in turbulent magnetic loops driven by nonrelativistic laser-plasma interaction

Using particle-in-cell simulations to study nonrelativistic laser pulse propagation in a under-critical plasma, we identify a novel mechanism that occurs during the growth of turbulent magnetic loops: electron penetration heating. The loops have an electromagnetic left-hand chirality distinct from that of well-known quasistatic magnetic islands. The fast electrons penetrate through the loops and thus are accelerated to unexpected relativistic energies due to the symmetry breaking induced by the coupling between the loop field and the non-relativistic electromagnetic wave. The identified features of penetration heating and magnetic loops might provide an alternative perspective for understanding superponderomotive electron heating in under-critical plasmas irradiated by nonrelativistic laser pulses. This is a potential explanation for anomalous hot electron generation in scenarios of laser-driven inertial confinement fusion.

physics.plasm-ph↗
arXiv2026

Directory-Aware Query and Maintenance in Vector Databases

Vector databases typically manage metadata as flat scalar attributes, which limits their ability to express hierarchical directory semantics commonly used to organize code repositories, enterprise documents, and agent memories. As a result, directory-scoped retrieval and structural updates are often implemented as application-layer workarounds, making recursive scope resolution expensive and directory maintenance difficult to keep consistent. This paper studies native directory semantics as a first-class capability for vector databases. We formalize two core operators: Directory-Semantic Query (DSQ) for hierarchically scoped retrieval, and Directory-Semantic Maintenance (DSM) for structural updates. We then evaluate three implementation strategies: query-time path expansion (PE-Online), ingestion-time path expansion (PE-Offline), and a Trie-based Hierarchical Index (TrieHI). Our analysis exposes the fundamental limitations of expansion-based designs: flattening the hierarchy incurs high recursive-query latency in PE-Online and unscalable write amplification during structural changes in both expansion strategies. In contrast, TrieHI keeps the directory topology as a native prefix tree, enabling efficient recursive retrieval through tree traversal and reducing maintenance cost through topological node manipulation. We benchmark these design points within ByteDance's Viking vector search engine and release two large-scale datasets, WIKI-Dir and ARXIV-Dir, to support future research on directory-semantic vector search. Finally, TrieHI has been integrated into OpenViking, an open-source context database for AI agents, where it supports filesystem-style context organization and directory-recursive retrieval.

cs.DB↗
arXiv2026

Arbor: Tree Search as a Cognition Layer for Autonomous Agents

Arbor is a multi-agent framework that introduces structured tree search as a cognition layer for autonomous agents operating in large, stateful action spaces. Prior autonomous optimization systems operate on isolated targets with stateless evaluation. Arbor instead maintains an explicit search tree of scored hypotheses that serves as the shared working memory across agents, evolving with every measurement, treating failures as diagnostic signal that reshapes subsequent exploration, and expanding as prior successes shift the bottleneck distribution. We validate Arbor on full-stack LLM inference optimization, a domain where achieving peak performance has historically required coordinated effort from engineering teams across the application, framework, compiler, kernel, and hardware stack. Arbor pairs an Orchestrator agent, which drives optimization by delegating to Domain Specialists across the inference stack, with a Critic agent that safeguards stability through root-cause analysis, introspection, and measurement validation -- a checks-and-balances architecture where neither agent can unilaterally drive the system. Agent capabilities are decomposed into hard skills (domain expertise) and soft skills (coordination protocols that determine how contributions compose), enabling fully autonomous multi-day campaigns. Arbor achieves up to 193% inference throughput-latency Pareto improvement over vendor-optimized baselines, while a single agent without the harness plateaus at +33% throughput improvement and crashes irrecoverably within hours. Arbor generalizes to multiple generations of hardware platform, and run-to-run variance is within 2 percentage points demonstrating that the method is hardware-agnostic and reproducible.

cs.AI↗
arXiv2026

Enabling Real-Time Colonoscopic Polyp Segmentation on Commodity CPUs via Ultra-Lightweight Architecture

Real-time polyp segmentation is essential for early colorectal cancer detection, yet clinical deployment remains blocked by GPU dependency. We introduce the UltraSeg family, a set of CPU-native segmentation models operating below 0.3M parameters. UltraSeg-108K (0.108M) establishes the extreme-compression frontier, while UltraSeg-130K (0.130M) integrates cross-layer lightweight fusion for enhanced multi-center generalization. The architecture replaces parameter-heavy components with grouped multi-rate dilated convolutions and attention-gated cross-layer fusion, achieving real-time throughput on a single CPU core (exceeding 50 FPS at 256*256 and 30 FPS at 352*352) without sacrificing clinical-grade accuracy. Evaluated on seven public datasets, UltraSeg-130K attains Dice scores exceeding 0.8 at both resolutions, substantially outperforming all existing sub-0.3M competitors. Notably, it approaches or exceeds UNet-Medium (7.76M parameters) on zero-shot external validations while using only 1.7% of its parameters, establishing the first strong baseline for CPU-native real-time polyp segmentation. When scaled to 4.38M parameters, UltraSeg achieves accuracy competitive with heavyweight state-of-the-art models while maintaining an order-of-magnitude parameter advantage, demonstrating that the proposed design principles yield intrinsic representational gains across the entire efficiency spectrum. By delivering the first clinically deployable, CPU-native real-time solution, this work provides an immediately usable tool for resource-limited settings and a reproducible blueprint for real-time medical AI beyond endoscopy. Source code is publicly available.

cs.CV↗
arXiv2026

Solving Reach- and Stabilize-Avoid Problems Using Discounted Reachability

In this article, we consider the infinite-horizon reach-avoid (RA) and stabilize-avoid (SA) zero-sum game problems for general nonlinear continuous-time systems, where the goal is to find the set of states that can be controlled to reach or stabilize to a target set, without violating constraints even under the worst-case disturbance. Based on the Hamilton-Jacobi reachability method, we address the RA problem by designing a new Lipschitz continuous RA value function, whose zero sublevel set exactly characterizes the RA set. We establish that the associated Bellman backup operator is contractive and that the RA value function is the unique viscosity solution of a Hamilton-Jacobi variational inequality. Finally, we develop a two-step framework for the SA problem by integrating our RA strategies with a recently proposed Robust Control Lyapunov-Value Function, thereby ensuring both target reachability and long-term stability. We numerically verify our RA and SA frameworks on a 3D Dubins car system to demonstrate the efficacy of the proposed approach.

math.OC↗
arXiv2026

Optimization of laser-driven proton acceleration in a near-critical-density plasma

Optimizing laser and plasma parameters is crucial for enhancing accelerated proton energy in laser-driven proton acceleration with finite laser energy for applications such as cancer therapy. Tight focusing plays a significant role in improving laser-driven proton acceleration, which is generally believed as a result of the enhancement of laser intensity. However, we find that even at a fixed laser intensity, reducing the focal spot size still enhances the proton energy. Through particle-in-cell simulations and theoretical modeling, we find that at a small spot size (0.8 μm), the maximum proton energy is enhanced by 56.3% compared to that obtained at a conventional spot size (3 μm). This improvement is attributed to the dominance of ponderomotive-force-driven electrons at reduced spot sizes, which generate stronger charge-separation fields that propagate at higher velocities. Furthermore, to optimize proton acceleration, we analytically derive an ideal plasma density profile that promotes phase-stable proton acceleration, yielding an additional energy increase of 61.3% over the case of a tightly focused laser interacting with a planar target of uniform density. These findings remain robust under parameter variations, indicating that advanced focusing techniques combined with optimized plasma profiles could relax the demand for high laser energies, thereby reducing the reliance on large-scale laser facilities in medical and scientific applications.

physics.plasm-ph↗