arXiv subjects

Jun Jiang

Publications and source records attributed to Jun Jiang.

At least 19 recordsLinked to original sources

arXiv2026

Exclusive production of doubly charmed baryon pair at B Factories

The exclusive production of doubly charmed baryon pairs $Ξ_{cc}+\barΞ_{cc}$ via $e^+e^-$ annihilation at B factories are calculated in the framework of non-relativistic QCD (NRQCD). The total cross sections, theoretical uncertainties, differential distributions and event yields for the BaBar and Belle II experiments are systematically discussed. According to our study, the detection prospects for both experiments are unfavorable.

hep-ph↗

arXiv2026

DynaSlots: Slot-Centric 3D Dynamics Pretraining for Robot Manipulation

Robot manipulation involves localized changes: picking up a cup moves the cup while leaving the surrounding scene largely unchanged. Yet existing 3D visual pretraining methods, including recent dynamics-aware variants, model state transitions at the scene level, compressing the motion of everything in a scene into a single global latent action. This conflates semantically distinct transitions in scenes with multiple moving parts, obscures dependencies between changing regions, and leaves the learned representation vulnerable to task-irrelevant scene content. We introduce DynaSlots, an action-free, label-free pretraining framework that decomposes each point cloud frame into a small set of temporally consistent slots and models dynamics at this per-slot level rather than the scene level. A temporal slot propagation module promotes consistent slot assignments across frames; an inverse dynamics model then extracts one latent action per slot from feature differences, and a diffusion-based forward dynamics model, paired with a self-supervised interaction head, predicts each slot's future state while learning pairwise proximity-and-motion relations. Experiments on Meta-World, Adroit, and a RealMan RM65-B robot demonstrate that DynaSlots outperforms prior scene-level baselines. It achieves superior success rates in simulation and exhibits strong robustness to scene clutter and unseen objects in real-world manipulation. Code is available at \href{https://zhucezhanghao12345-source.github.io/DynaSlots-Anonymous/}{https://zhucezhanghao12345-source.github.io/DynaSlots-Anonymous/}.

cs.RO↗

arXiv2026

Where to find $X(17)$?

The Atomki group puts forward the hypothesis of an $X(17)$ particle to explain its anomaly observation in ${^{8}\text{Be}^{*}} \to {^{8}\text{Be}} e^+e^-$ transition. Utilizing experimental results from the Atomki experiments, measurements of the electron's anomalous magnetic moment (AMM), the parity-violating Møller scattering experiment, the NA64 experiment, the KLOE-2 experiment, and the PADME experiment, we derive constraints on the $Xee$ coupling of the hypothetical $X(17)$ boson to electrons. A joint $χ^2$ analysis of the electron's AMM (Cs and Rb measurements) and the parity-violating Møller scattering yields two mutually exclusive solutions. The Cs and Rb AMM constraints restrict the combination $(\varepsilon_e^v)^2-5(\varepsilon_e^a)^2$ with opposite signs. Cs+Møller combination favours a dominant axial-vector coupling $|\varepsilon_e^a|\approx5.3\times10^{-4}\gg|\varepsilon_e^v|$, whereas Rb+Møller favours a dominant vector coupling $|\varepsilon_e^v|\approx6.9\times10^{-4}\gg|\varepsilon_e^a|$. The Møller bound suppresses the subdominant coupling for both solutions. Incorporating NA64 and KLOE-2 limits on $(\varepsilon_e^v)^2+(\varepsilon_e^a)^2$ introduces mild tension for the Cs+Møller solution, while the Rb+Møller solution remains nearly unchanged.

hep-ph↗

arXiv2026

Towards channel foundation models (CFMs): Motivations, methodologies and opportunities

Artificial intelligence (AI) models for wireless systems are commonly optimized for fixed tasks, channel distributions, and system configurations, which limits their reuse when downstream objectives or operating conditions change. Channel foundation models (CFMs) address this limitation by pretraining reusable models on channel-informative observations and adapting their parameters or representations across channel-related tasks and conditions. However, existing studies differ substantially in their input modalities, pretraining targets, retained components, and transfer settings, making it difficult to compare how the methods are constructed, reused, and evaluated. This paper surveys CFM research through the central question of how channel-informative pretraining is converted into reusable downstream capabilities. We adopt a channel-centered scope covering explicit channel state information, received-signal observations, and auxiliary environmental or geometric context. Existing methods are organized according to the source of their learning targets into observation-space modeling, relational representation learning, latent-space prediction, task-target learning, system-utility optimization, and hybrid combinations. For representative methods, we compare how reusable parameters are learned and adapted, their downstream applications, and the settings used to evaluate transfer. The resulting framework connects CFM methodologies to physical-layer processing, radio access network and medium access control tasks, integrated sensing and communication, and radio-environment modeling. A matched case study further compares transfer for channel extrapolation, beam prediction, and positioning under common backbone and source-data settings. Finally, we discuss open problems in data construction, transfer evaluation, efficient adaptation, deployment, reliability, privacy, and security.

eess.SP↗

arXiv2026

The van Est theorem for Rota-Baxter Lie groups

Rota-Baxter Lie groups are the group-level counterparts of Rota-Baxter Lie algebras of weight $1$. In this paper, we first establish a correspondence between representations of Rota-Baxter Lie groups and representations of Rota-Baxter Lie algebras of weight $1$ via differentiation and integration. We then develop a cohomology theory for Rota-Baxter Lie groups with coefficients in a representation by constructing an explicit cochain complex in arbitrary degrees. We justify this theory by proving a van Est type theorem, which relates the cohomology of a Rota-Baxter Lie group to that of its Rota-Baxter Lie algebra of weight $1$. As an application, we prove that equivalence classes of central extensions of a Rota-Baxter Lie group are classified by the second cohomology group.

math.GR↗

arXiv2026

Equivariant generative diffusion learns and generalizes the structural ensemble of amorphous oxides

Amorphous materials are statistical ensembles rather than definitive structures, and conventional density-functional (DFT) and machine-learned-potential simulations sample only a small part of that ensemble. We present an $SE(3)$-equivariant denoising-diffusion model that learns the configurational distribution of amorphous oxides, so the model itself is the structure database. The learning is data efficient. A model trained on $1{,}781$ DFT configurations suffices to reproduce partial radial distribution functions, coordination statistics and bond-angle distributions, and to generate models of over $3\times10^{5}$ atoms at a cost comparable to that of the cheapest classical pair potentials. The trained model can propose amorphous atomic structures for first-principles relaxation to explore the configuration space. For example, it locates an amorphous Zr-Ta-O structure $36$ meV/atom below the previously known minimum. Generation can also extend beyond trained conditions to non-stoichiometric compositions, other mass densities, interfaces, and doping. First-principles verification confirms that generation can be steered to a requested energy, and shows that the denoising training loss does not rank generative quality, because the two measure different things.

cond-mat.mtrl-sci↗

arXiv2026

AI/ML for Mobile Networks: Release 19 Status and Future Challenges

Artificial intelligence (AI) and machine learning (ML) have become essential drivers of sixth-generation (6G) mobile networks. Unlike earlier surveys that mainly review high-level standardization progress, this paper focuses on the practical deployment barriers that remain after Release 19. We identify three open problems: dataset construction under 3GPP guidelines, cross-scenario and cross-simulator generalization, and baseline selection under joint performance-cost KPIs. These problems are examined through a CSI-feedback case study that uses both DeepMIMO (outdoor/indoor, ray-tracing) and QuaDRiGa (indoor-factory/highway, geometry-based stochastic) channel models. It reveals pronounced generalization challenges in zero-shot transfer, especially when models move across different simulators. A clear mismatch exists between deterministic ray-tracing and stochastic geometry-based modeling paradigms and leads to dramatic performance degradation. In contrast, pre-training on a source simulator followed by targeted fine-tuning on the target domain substantially mitigates these cross-simulator difficulties, and markedly outperforms zero-shot transfer. Attention-based models generalize better across the two physics regimes while keeping computational cost moderate. These results explain why the fine-tuning and LCM procedures already partially standardized in Release 19 are necessary, and they point to concrete next steps for scalable LCM, wider use cases, and multi-task learning.

eess.SP↗

arXiv2026

REARL: A Closed-loop Autonomous Driving Simulation Enhancement Framework with Real Traffic Data and Large Language Models

Accurate simulation is crucial for autonomous driving development, yet capturing real-world traffic complexity remains challenging. Existing simulators that rely on predefined rules or static data playback struggle with dynamic traffic. CRITICAL uses real traffic data and a large language model (LLM) to adjust the initial simulation configuration, but the simulated distribution still diverges from real traffic as the rollout evolves. We propose REARL, a closed-loop simulation enhancement framework that integrates real traffic data with LLMs. Real traffic data are clustered, and each cluster center is used as a representative scenario that provides typical real-world traffic patterns for the LLM. A timed sliding-window detector then monitors discrepancies in vehicle speed distribution and mean spacing between pairs of vehicles. If a metric exceeds a threshold, the LLM adjusts vehicle decision-making; otherwise the existing controller is kept. The LLM also selects a matching real vehicle from a traffic snapshot and modulates the simulated vehicle with reference to that real action. In a controlled HighD highway setting, compared with the CRITICAL baseline and a PPO-based learning baseline, REARL reduces the Hellinger distance for speed distributions to 0.3067 and the MAPE for mean spacing to 0.8371, while achieving a time headway (THW) of 22.8575 and a lane change rate of 0.0708.

cs.LG↗

arXiv2026

Polarization and correlation effects in $γγ\to W^-W^+$ at Next-to-Leading-Order Electroweak Accuracy

We study the next-to-leading order (NLO) electroweak (EW) corrections to the polarization and correlation of $W$ boson pairs produced through photon-photon fusion in proton-proton ultra-peripheral collisions at the LHC, with both $W$ bosons decaying leptonically. The azimuthal correlation distributions of the charged leptons, which receive no contribution from anomalous quartic gauge couplings (AQGCs) at linear order, are perturbatively stable at the 0.40\% level, whereas individual correlation coefficient $C_{2,2}$ receives relative corrections of up to $-33.84\%$. Some of correlation coefficients that vanish at leading order (LO) in the Standard Model but are sensitive to AQGCs acquire non-zero one-loop values of order $10^{-4}-10^{-3}$. We quantify the region of the ($a_0/Λ^2,a_c/Λ^2$) plane in which these loop-induced baselines compete with the AQGC contributions and find that, while irrelevant at the current experimental sensitivity, they must be included once the correlation coefficients are measured at the per-mille level.

hep-ph↗

arXiv2026

A computable representation of the physical laboratory enables verifiable workflows

Making science computable requires representations of both scientific knowledge and the physical world in which scientific claims are tested. A computable representation of the physical laboratory is established through typed research objects, capability-bound operations and a compositional workflow algebra. It provides the physical-world counterpart to machine-readable knowledge, expressing workflows as programs over evolving laboratory states with explicit dependencies, decisions, iteration and concurrency. The representation was implemented in a modular agentic robotic laboratory by binding formal operations to executable Function Skills. For diverse scientific intents, capability-relative workflows were generated, while stateful simulation propagated object transformations and verified operation preconditions and laboratory constraints before dispatch. The proposed representation and its engineering framework jointly establish a general computational interface between agent reasoning and capability-bound physical transformations, providing a foundation for end-to-end autonomous scientific discovery.

cs.AI↗

arXiv2026

CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models

Channel foundation models (CFMs) are commonly evaluated in model-specific pipelines that differ in data, radio configurations, partitions, adaptation procedures, task definitions, and metrics, preventing reproducible comparison across CFMs and against task-specific networks. We release CFM-Bench, a unified multi-domain, multi-task benchmark comprising 157,900 official single-frame examples from six domains spanning 3GPP statistical simulation, two ray-tracing pipelines, terrestrial and aerial measurements, and synchronized vehicular multimodal simulation. Source-specific interfaces preserve complex channel state information (CSI) and the physical metadata available in each domain while allowing documented model-specific preprocessing. To reduce spatio-temporal leakage, official partitions isolate complete trajectories, measurement sessions, flights, vehicle links, simulation realizations, or buffered spatial regions. CFM-Bench excludes all benchmark splits from foundation-model pretraining, reserves the official training split for downstream fine-tuning, and reports the data used during model development. Six task groups across PHY, RAN, and ISAC applications cover CSI feedback, frequency and temporal channel extrapolation, propagation-state classification, current- and future-beam prediction, and single-frame and temporal localization. Representative experiments on CSI feedback, channel extrapolation, current-beam prediction, and wireless positioning provide reproducible reference results for pretrained channel-prediction models and task-specific neural networks. These results show that relative model performance can vary across data domains, highlighting the importance of using common data partitions, task definitions, and evaluation metrics. CFM-Bench provides a common substrate for evaluating the transferability of channel representations across models, domains, and tasks.

cs.AI↗

arXiv2026

Parallelized computation of quasi-periodic solutions for finite element problems: A Fourier series expansion-based shooting method

High-dimensional nonlinear mechanical systems admit quasi-periodic solutions that are essential for the understanding of the dynamical systems. These quasi-periodic solutions stay on some invariant tori governed by complex PDEs in hyper-time. Here, we propose a Fourier series expansion-based shooting method (FSE-Shooting) for the parallelized computation of quasi-periodic solution with $d$ base frequencies ($d \ge 2$). We represent the associated $d$-torus as a collection of trajectories initialized at a ($d-1$)-torus. We drive a set of ODEs that hold for any of these trajectories. We also derive a set of boundary conditions that couple the initial and terminal states of these trajectories and then formulate a set of nonlinear algebraic equations via the coupling conditions. We use Fourier series expansion to parameterize the ($d-1$)-torus and shooting method to iterate the Fourier coefficients associated with initial torus such that the coupling conditions are satisfied. In particular, the terminal points of these trajectories are parallelized computed via Newmark integration, where the time points and Fourier coefficients are transformed to each other by alternating Frequency-Time method. A straightforward phase condition is devised to track the quasi-periodic solutions with priori unknown base frequencies. Additionally, the by-product of the FSE-Shooting can be also directly used to compute the Lyapunov exponents to assess the stabilities of quasi-periodic solutions. The results of three finite element systems show the efficiency and versatility of FSE-Shooting in high-dimensional nonlinear dynamical systems, including a three-dimensional shell structure with $1872$ DOFs.

math.NA↗

arXiv2026

General multi-steps variable-coefficient formulation for computing quasi-periodic solutions with multiple base frequencies

Quasi-periodic solutions with multiple base frequencies exhibit the feature of $2π$-periodicity with respect to each of the hyper-time variables. However, it remains a challenge work, due to the lack of effective solution methods, to solve and track the quasi-periodic solutions with multiple base frequencies until now. In this work, a multi-steps variable-coefficient formulation (m-VCF) is proposed, which provides a unified framework to enable either harmonic balance method (HB) or collocation method (CO) or finite difference method (FD) to solve quasi-periodic solutions with multiple base frequencies. For this purpose, a method of alternating U and S domain (AUS) is also developed to efficiently evaluate the nonlinear force terms. Furthermore, a new robust phase condition is presented for all of the three methods to make them track the quasi-periodic solutions with prior unknown multiple base frequencies, while the stability of the quasi-periodic solutions is assessed by mean of Lyapunov exponents. The feasibility of the constructed methods under the above framework is verified by application to three nonlinear systems.

math.DS↗

arXiv2026

Stress-testing large language model agents in a robotic chemistry laboratory

AI is evaluated through knowledge, reasoning and plan generation, yet scientific agency requires reliable physical action and adaptation to evidence. Here, we use a robotic chemistry laboratory as a physical-world testbed to make scientific agency measurable. Its 45 modular workstations exposed as machine-readable skills enabled 4,608 trials. Only 3.3% of trials produced expert-assessed executable workflows under laboratory constraints; even the best system achieved 28.1%. Long-horizon planning remained a challenge: only three executable workflows exceeded 30 operations, although the longest contained 44. Across five rounds, experimental feedback prompted local adjustments but no workflow-level replanning or analytical-method redesign. By making physical executability and evidence-driven replanning measurable, our study provides an evidence-based assessment of deployment readiness and a diagnostic framework to guide closed-loop improvements towards physically grounded autonomous research.

cs.AI↗

arXiv2026

Non-abelian Extensions of Lie algebras with derivations

In this paper, we investigate non-abelian extensions of Lie algebras with derivations from several different perspectives. We show that the theory of non-abelian extensions of a Lie algebra with a derivation can be characterized by means of the second non-abelian cohomology, the Deligne groupoid, the homotopy category of strict Lie $2$-algebras with strict derivations, and the notion of a $(\mathfrak{g}, D)$-kernel, respectively. Moreover, within this unified framework, we address the following existence problem: given a non-abelian extension of Lie algebras $$ 0\longrightarrow\mathfrak{h}\overset{i}\longrightarrow\hat{\mathfrak{g}}\overset{p}\longrightarrow\mathfrak{g}\longrightarrow 0, $$ let $(K,D)\in\mathrm{Der}(\mathfrak{h})\times\mathrm{Der}(\mathfrak{g})$ be a pair of derivations of $\mathfrak{h}$ and $\mathfrak{g}$ respectively. When does there exist a derivation $\hat{D}$ of $\hat{\mathfrak{g}}$ such that $\hat{D}|_{\mathfrak{h}}=K$ and $D\circ p=p\circ\hat{D}.$ We provide an obstruction class for the existence of such a lift.

math.RA↗

arXiv2026

JEPA-CFM: A Joint Embedding Predictive Architecture-based Channel Foundation Model for Robust Fluid Antenna Systems

Fluid antenna systems (FAS) have emerged as a promising technology for sixth-generation (6G) wireless networks. By allowing antenna elements to move freely within a compact region, FAS can exploit rich spatial diversity without additional hardware. However, acquiring real-time channel state information (CSI), extrapolating channel values to unmeasured antenna ports, and determining accurate user positions remain major obstacles. These challenges stem mainly from strong spatial correlations within the limited aperture and the scarcity of observable data. To overcome these limitations, this paper introduces joint embedding predictive architecture (JEPA)-based channel foundation model (CFM) specifically designed for FAS. The model adopts JEPA to learn versatile representations by extracting high-level latent embeddings of masked or unobserved channel segments. Unlike conventional approaches that attempt pixel-by-pixel reconstruction of raw CSI coefficients, JEPA-CFM focuses on predicting abstract structures in a compact feature space. The pre-training objective combines three complementary loss terms: the standard masked autoencoder reconstruction loss, the JEPA latent prediction loss, and a sliced isotropic Gaussian regularization (SIGReg) term. Together, these components prevent representation collapse and significantly enhance robustness under severe spatial correlation and highly sparse observations. After pre-training, the encoder is frozen, and lightweight task-specific heads are attached: a decoder for channel extrapolation and a global average pooling layer followed by a multi-layer perceptron regression head for wireless positioning. Extensive simulations in the realistic DeepMIMO urban scenario demonstrate that JEPA-CFM substantially outperforms the conventional masked autoencoder baseline in channel extrapolation and wireless positioning.

eess.SP↗

arXiv2026

Great X: A Unified Multi-Modal Simulator Bridging the Sim2Real Gap for 6G

Large-scale, precisely synchronized multi-modal datasets are critical for data-driven sixth-generation (6G) wireless research, yet real-world collection remains costly and difficult. Existing multi-platform simulators often suffer from timing misalignment, limited modality support, and simplified scene and material modeling, which reduce fidelity and enlarge the simulation-to-reality (Sim2Real) gap. We propose Great-X, a unified single-engine simulator implemented in Unreal Engine. Great-X integrates native ray tracing with co-located visual and electromagnetic material properties on shared 3D meshes, enabling pixel-level consistency between radio and visual outputs. A deterministic fixed-step clock and synchronous buffering provide frame-accurate alignment across CSI, RGB, depth, LiDAR, radar, and event data. Based on this framework, we construct Great-MCD, containing over three million synchronized samples across urban and rural environments, day and night conditions, multiple UAV types, and diverse trajectories. Experiments show that Great-X generates channel impulse responses consistent with real measurements and provides transferable data for wireless positioning and CSI feedback, outperforming existing simulators and measured-data baselines in both zero-shot and fine-tuned settings.

cs.CV↗

arXiv2026

Between Safe Boundaries: Exploiting Temporal Consistency for Jailbreaking Text-To-Video Generation Models

Recently, text-to-video (T2V) models have been widely deployed, sparking growing concerns over their robustness against jailbreak attacks. Existing jailbreak methods, mostly adapted from text-to-image attacks, suffer notable drawbacks when applied to T2V systems. They fail to fully leverage temporal consistency, an inherent characteristic of video generation. Besides, these methods demand heavy video query optimization, which is infeasible in practical black-box scenarios. Their adversarial prompt search is also driven by heuristic local signals, lacking principled structured exploration strategies. To tackle these limitations, we propose BSB, a structured, query-efficient jailbreak framework for T2V models. BSB harnesses temporal consistency by encoding harmful intent as the transition between two individually harmless boundary states. Under this paradigm, the attack targets boundary-state pairs whose interpolation tends to produce unsafe intermediate frames during video generation. Directly evaluating all candidate pairs within the video space incurs prohibitive computation cost. Instead, BSB conducts Monte Carlo Tree Search (MCTS) in a cheaper textual proxy space and regularly calibrates search outcomes with sparse video-level evaluations. We conduct comprehensive experiments on mainstream commercial T2V models including Veo 3.1, Sora 2, Seedance and Kling v1. Results show BSB surpasses all existing jailbreak baselines, delivering an average 18.6% relative gain in attack success rate over the strongest competitor across evaluated models. Our findings identify temporal consistency as an understudied yet vital attack surface for T2V models and verify that structured search facilitates effective vulnerability discovery under constrained query budgets.

cs.CR↗