Search arXiv⌕ Search

arXiv subjects

Jun Tao

Publications and source records attributed to Jun Tao.

At least 19 recordsLinked to original sources
arXiv2026

A Geometric No-Go Theorem for Regular Black Holes in Scalar-Tensor Theories

Perturbative instabilities generically puzzles singularity-free spacetimes, including non-singular bounce, traversible wormholes and regular black holes (RBHs). Can the scalar-tensor (ST) theories that stabilize the first two also stabilize RBHs? We show that the answer is no for static, spherically symmetric RBHs supported by a radial scalar field within a broad class of ST theories. In any RBHs with two-horizon structure, the ratio of the longitudinal scale to the sphere radius must attain an interior maximum. The proper-time derivative of that ratio is associated to the squared propagation speed of odd-parity tensor perturbations by the background field equations in a broad class of scalar-tensor theories. A Lorentzian odd-tensor cone then requires a local minimum instead of a maximum. This conflicts indicate a geometric no-go theorem, covering a broad class of ST theories including spatially covariant gravity, Horndeski theory, quadratic GLPV, DHOST, and U-DHOST theories, and their admissible disformal extensions. Viable RBHs therefore require relaxing at least one geometric or dynamical assumption.

gr-qc↗
arXiv2026

Revisiting the Lyth bound constraints on inflation from ACT DR6 results

The Lyth bound asserts that the field excursion of inflaton must be sub-Planckian, thereby imposing an upper bound on the amplitude of the tensor power spectrum in inflationary scenario. This bound is conventionally derived assuming a scale-invariant curvature power spectrum, i.e., $n_s = 1$. However, astrophysical observations confirm a red-tilted spectrum with $n_s < 1$. In light of recent results from the Atacama Cosmology Telescope (ACT) DR6, we revisit these constraints using the newly implied scalar spectral index of $n_s \simeq 0.9743$. Incorporating the ACT data yields a different upper bound on the tensor-to-scalar ratio $r$, which can potentially exclude inflationary scenarios previously robust under the original Lyth bound with $n_s = 1$. Our result highlights the urgent need to combine theoretical Lyth bound considerations with the most up-to-date astrophysical data.

astro-ph.CO↗
arXiv2026

Zero-Shot Multi-Class Wafer Map Classification with Rule-Ordered Prompts for Large Multimodal Models

Wafer-map defect classification often requires labeled data and retraining when production conditions change. We present a training-free six-class framework that combines connected-component highlighting with morphology-, location-, and exclusion-aware prompts for a frozen large multimodal model. On Mixed-WM38 (6,866 maps), multi-region prompting achieves 92.9% macro and 93.8% overall accuracy. On WM811K (9,000 maps), it achieves 79.1% and 81.3%, respectively. Eight alternative rule orders achieve 90.1-91.9% overall accuracy, compared with 93.8% for the selected order.

cs.AI↗
arXiv2026

Finite Modular Hamiltonians and Their Singular Limits

Using the open solid torus as a finite parent, we revisit modular flow for spherical regions in conformal field theory. Its nonsingular angular evolution gives finite modular generators in a very simple form, while an exact conformal map to a cavity frame recovers the Rindler, CHM, and stationary-source ball constructions as singular limits. The same finite parent also provides a regular completion of the center obstruction in the complexified ball flow. Charged, Dirac, and inhomogeneous scalar examples are given.

hep-th↗
arXiv2026

Quasinormal-Mode Collider Spectroscopy

Black-hole (BH) quasinormal modes encode the properties of merger remnants and provide powerful tests of strong-field gravity. The BH spectroscopy has predominantly treated them as independent linear oscillations. Nonlinear ringdown can additionally reveal how these modes interact and which intermediate states mediate their interactions. Inspired by cosmological collider physics, we introduce quasinormal-mode collider spectroscopy (QCS), a framework for organizing and predicting nonlinear BH responses. We establish two structural results. First, at an isolated, nondegenerate intermediate pole, a cubic strain residue factorizes into an excitation amplitude and a quadratic conversion coefficient. This factorization imposes rank constraints across output channels and choices of second-order initial data, providing consistency tests of the intermediate-mode description. Second, the intermediate pole residue can be reconstructed from its horizon and infinity radiation. With the first-order field and third-order data held fixed, this reconstruction extends to the full cubic waveform difference generated by changing the second-order initial data. QCS thus connects lower-order radiation to higher-order observables and provides a framework for probing the interaction structure of nonlinear ringdown.

gr-qc↗
arXiv2026

A Multitask Large Reasoning Model for Molecular Science

Artificial intelligence in molecular science must move beyond pattern recognition toward chemically valid and interpretable reasoning. We present a task-adaptive large reasoning model that integrates chemical knowledge through a synergistic multispecialist architecture, chain-of-thought supervision, and molecule-informed reinforcement learning. Task-conditioned routing coordinates prediction and inference specialists across 10 molecular tasks spanning molecular description and generation, nomenclature translation, property prediction, and reaction prediction. The model outperforms more than 20 general-purpose and molecular large language models, improves aggregate performance over the base model by 50.3%, and surpasses the leading molecular multitask baseline on most tasks. Analyses of specialist representations and reasoning pathways reveal task-specific adaptation while retaining interpretable chemical inference. A case study further demonstrates an integrated workflow for central nervous system candidate generation, property screening, molecular interpretation, and retrosynthetic planning. These results demonstrate a versatile multi-task framework for knowledge-guided molecular reasoning and design, with the potential to serve as a core task engine for future molecular science agents.

cs.LG↗
arXiv2026

Periodic orbits around a magnetically charged black hole in $f(R,T)$ gravity coupled with Euler-Heisenberg electrodynamics

We systematically investigate the zoom-whirl periodic orbits of a small compact object orbiting a magnetically charged black hole in $f(R, T)$ gravity coupled with Euler-Heisenberg nonlinear electrodynamics. We evaluate the impact of the magnetic charge $Q_m$ and the Euler-Heisenberg parameter $a$ on the characteristics of the innermost stable circular orbit (ISCO) and the marginally bound orbit (MBO). Particularly, we focus on the precession parameter q and systematically examine how the magnetic charge and other parameters affect the trajectories of periodic orbits. Using the numerical kludge method, we generate gravitational waveforms for these periodic orbits. The results presented in our work demonstrate that the magnetic charge can significantly modify not only the precession parameter but also the orbital trajectories of the periodic orbits. Moreover, changing the magnetic charge $Q_m$ could cause a significant phase shift in the gravitational waveforms, while other parameters exert a relatively weaker influence on the gravitational waves.

gr-qc↗
arXiv2026

Pruned Traffic Trees: Native Semantic Compression with a Protocol-Structured Model Family for Encrypted Traffic Classification

Deep learning has achieved strong performance in encrypted traffic classification (ETC), yet its computational cost limits deployment on resource-constrained network devices such as routers and middleboxes. Existing compression methods mainly operate on weights, channels, hidden representations, or predictions, but do not explicitly determine which protocol fields and structural contexts should remain. We propose Pruned Traffic Trees (PTT), a three-level protocol-structured model family that treats native protocol structures as compression units. PTT-Full learns protocol-structured representations and field salience from complete Protocol Tree Graphs (PTGs), with flow-level self-supervised learning and protocol-presence-aware sparse execution. The learned salience and TopK+$k$ closure construct Distilled PTGs (PTG-Ds) for PTT-Distilled, while PTT-Lite inherits this topology and reduces width through structure-aligned transfer and flow-level logits distillation. Under flow-disjoint and Strong Information Information (SII)-masked settings, PTT-Full achieves Macro-F1 scores of 0.9519 and 0.9416 on CSTNET-TLS1.3 and CipherSpectrum, while PTT-Lite retains 0.9325 and 0.9136 with 80.3\% and 61.3\% fewer parameters, 98.85\% and 98.78\% lower effective GFLOPs, and 8.75$\times$ and 8.46$\times$ CPU inference speedups. These results demonstrate that treating protocol structure itself as the compression object enables effective performance-efficiency trade-offs for lightweight ETC.

cs.NI↗
arXiv2026

Mitigating Proxy-Induced Traffic Drift in Website Fingerprinting via Model-Agnostic Traffic Tailoring

Website fingerprinting (WF) based on deep learning can effectively identify websites from encrypted traffic. However, users often rely on proxy protocols to bypass censorship, and the diversity of these protocols poses a major challenge, as WF models trained on traffic from one set of protocols perform poorly when evaluated on that from unseen protocols. We attribute this issue to proxy-induced feature drift, where traffic patterns of the same website vary with the proxy protocol, leading to discrepancies that WF models fail to capture and severe performance degradation. To tackle this issue, we propose PA3, a model-agnostic preprocessing framework to analyze and mitigate the proxy-induced drift. PA3 first fingerprints the protocol-specific drift. These fingerprints are then used to tailor the proxied traffic for feature alignment, which mitigates the drift and considerably improves the generalization of WF models on traffic from unseen protocols. Extensive evaluations demonstrate that PA3 substantially enhances generalization on unseen protocols with an average improvement of 0.12 in F1-score (roughly 27% relative), achieving up to a 0.41 absolute gain across models, which narrows the performance gap introduced by the drift. In the best case, PA3 enables WF models to obtain F1-scores above 0.96 on traffic from unseen protocols.

cs.NI↗
arXiv2026

The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence

We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The flagship M2 contains 229.9B total parameters with only 9.8B activated per token. Designed end-to-end for agentic deployment, the M2 series rests on three components: (i) agent-driven data pipelines producing large-scale, verifiable trajectories across agentic coding and agentic cowork, each grounded in an executable workspace and an artifact-aligned reward; (ii) Forge, a scalable agent-native RL system that adapts to long-horizon agent trajectories, paired with windowed-FIFO scheduling, prefix-tree merging, inference optimization, and a clean training-inference-agent decoupling that supports both white-box and black-box agents; (iii) the latest M2.7 checkpoint takes an early step toward self-evolution -- autonomously debugging training runs and modifying its own scaffold. Across M2 through M2.7, this combination translates a mini-activation footprint into frontier-tier performance on agentic coding, deep search, office-task, and reasoning benchmarks.

cs.AI↗
arXiv2026

From Scalars to Time Series: Rethinking Implicit Neural Representations for Time-Varying Volumetric Data

Implicit neural representations (INRs) for time-varying volumetric data are typically trained using dense sampling over spatiotemporal coordinates, where each observation corresponds to a single point in space and time. This coordinate-wise formulation requires extensive sampling during optimization, leading to high computational cost and inefficient use of temporal structure. In this work, we revisit this design choice and show that dense spatiotemporal sampling is not necessary for learning time-varying fields. Instead, we represent the data as a collection of spatially indexed time series and train INRs using sequence-level supervision over each spatial location, rather than coordinate-wise scalar samples. This reformulation eliminates the need for dense spatiotemporal sampling and instead learns each spatial location from its full temporal evolution in a structured manner. We demonstrate that this representation is compatible with a range of existing INR architectures and consistently improves reconstruction quality, while significantly reducing training cost. Furthermore, we show that this formulation can be combined with mixture-of-experts architectures, and that our MoE instantiation further improves reconstruction quality compared to both the base reformulation and existing MoE-based INR methods, providing a stronger capacity allocation under heterogeneous temporal dynamics.

cs.AI↗
arXiv2026

Enhancing Vision Foundation Models via Multimodal Continual Pre-Training

Vision Foundation Models (VFMs) provide strong visual representations for a wide range of applications. In this work, we enhance prevailing VFMs through multimodal training, allowing them to effectively process visual inputs at varying resolutions while producing visual representations that are better aligned with language representations, regardless of their original pre-training objectives. To this end, we introduce M-CPT, a Multimodal Continual Pre-Training framework designed to improve the understanding capability of pre-trained VFMs while preserving their strong visual representation quality. M-CPT introduces a Continual Position Embedding (CPE) for handling flexible visual resolutions, along with a feature alignment objective that improves the consistency between visual and textual representations during multimodal training. Extensive experiments on leading VFMs, including DINOv2, SigLIP, and AIMv2, demonstrate that M-CPT consistently improves multimodal understanding performance while preserving strong performance on standard vision benchmarks such as classification and segmentation.

cs.CV↗
arXiv2026

Finite Coherence in Gravitational Waves from Tidally Excited Axion Clouds

Axion clouds around rotating black holes form gravitational atoms whose tidal transitions can radiate gravitational waves in binaries. For strongly coupled Bohr crossings, transition radiation is governed by the outgoing two-level coherence, not by the transition probability alone. This coherence is suppressed both on the adiabatic branch and in the weak passage limit, but survives for intermediate sweep rates, producing a finite transition waveform and a localized orbital response. In more massive systems, fine and hyperfine transitions produce narrowband gravitational radiation and cumulative departures from vacuum binary waveforms. Coherent tidal crossings offer a gravitational-wave probe of axion-cloud dynamics.

gr-qc↗
arXiv2026

Periodic orbits and their gravitational waves in EMRIs: supermassive black hole affected by galactic dark matter halos

Periodic orbits exhibiting zoom-whirl behavior have become attractive topics for studying particle dynamics and gravitational wave emission in extreme-mass-ratio inspirals (EMRIs). This study systematically investigates periodic orbits around black holes and their gravitational wave radiation in three dark matter halo environments: NFW, Beta, and Moore models. The dark matter distribution in these models can be effectively incorporated using two parameters -- the dark matter characteristic mass and halo characteristic radius. Our results reveal that for a larger dark matter mass and a smaller characteristic radius, the shapes of the periodic orbits and the corresponding gravitational waveforms show more significant deviations from the Schwarzschild case. As the halo characteristic radius increases, the orbital shapes and waveform characteristics gradually converge with the Schwarzschild black hole results. Our results also suggest that the NFW and Beta models produce nearly indistinguishable results, while the Moore model shows distinct signatures compared with Beta/NFW models. Furthermore, calculations of characteristic strains in frequency spectra show that gravitational-wave signals associated with these periodic orbits lie above the sensitivity curves of LISA, TianQin, and Taiji, indicating their detectability in future space-based gravitational wave observatories. These findings deepen our understanding of dark matter halo effects on periodic motions and gravitational wave signatures. Keywords: GR black holes; Gravitational waves in GR and beyond: theory; dark matter theory; astrophysical black holes

gr-qc↗
arXiv2026

Wormhole Solutions and Pre-inflationary Epoch in $F(R, T)$ Gravity with Axion Fields

This study investigates axion-dilaton wormhole solutions within the framework of $F(R,T)$ gravity to resolve the issue of insufficient inflationary e-folds in the no-boundary proposal. By examining both Giddings-Strominger and expanding wormhole solutions in asymptotically flat Euclidean spacetime, we demonstrate that the matter-geometry coupling induces complex dynamical oscillations in the scale factor and the dilaton field. These complex oscillatory modes significantly reduce the Euclidean action compared to standard general relativity, consequently enhancing the nucleation probability. Furthermore, we extend this theoretical setup to a ``wineglass'' half-wormhole model in Euclidean Anti-de Sitter (EAdS) spacetime. Our analysis yields a specific constraint on the coupling parameter. This constraint introduces an unstable maximum in the potential and simultaneously decreases the action. The presence of this unstable maximum fundamentally alters the probability distribution of initial states, rendering the evolution of universes from high-potential regions far more probable. Consequently, this approach significantly increases the likelihood of long-lasting inflation, providing a theoretical pathway to reconcile the no-boundary proposal with astronomical observations requiring sustained cosmic expansion.

gr-qc↗
arXiv2026

Treat Traffic Like Trees: A Semantic-Preserving Hierarchical Graph-Based Expert Framework for Encrypted Traffic Analysis

Graph-based deep learning methods have been widely employed in encrypted traffic analysis to exploit latent correlations across different granularities. However, while complex preprocessing pipelines and sophisticated model structures often achieve strong performance, they may obscure inherent protocol semantics during representation learning. Moreover, the hierarchical structure of protocol layers and their corresponding fields, defined by protocol specifications and routinely utilized in manual traffic analysis, remains underexplored in existing learning frameworks. In this paper, we propose Protocol Tree Graph Attention with Mixture of Experts (PTGAMoE), a semantic-preserving hierarchical graph-based expert framework for encrypted traffic analysis. The field-based graph construction and expert committee design enable PTGAMoE to quantify the model's preferences for specific fields and protocols. Extensive experimental results on representative benchmark datasets under strict no-data-leakage settings demonstrate that PTGAMoE significantly outperforms state-of-the-art (SOTA) models. Furthermore, the semantic-preserving design provides interpretable insights into protocol-level feature importance and expert-level contributions, reflecting the model's decision-making logic in encrypted traffic classification tasks.

cs.NI↗
arXiv2026

Exceptional Points in Quasinormal Spectra of Hairy Black Holes

Exceptional points (EPs) in quasinormal mode (QNM) spectra are non-Hermitian degeneracies at which both the eigenvalues and eigenfunctions coalesce. In this paper, we identify an EP in the scalar QNM spectrum of hairy black holes in the Einstein-Maxwell-scalar theory by scanning the parameter space. We then investigate its implications for ringdown signals by extracting QNMs from time-domain waveforms. Our results show that an EP ansatz, which includes a resonant contribution containing a term linear in time, provides a more robust description of ringdown at the EP than the standard ansatz based on a superposition of independent damped modes. In particular, it captures the resonant contribution associated with spectral coalescence more naturally, and even when the waveform is fitted with the standard ansatz, the resulting fit may still exhibit characteristic features of the EP ansatz.

gr-qc↗
arXiv2026

STEP: Scientific Time-Series Encoder Pretraining via Cross-Domain Distillation

Scientific time series are central to scientific AI but are typically sparse, highly heterogeneous, and limited in scale, making unified representation learning particularly challenging. Meanwhile, foundation models pretrained on relevant time series domains such as audio, general time series, and brain signals contain rich knowledge, but their applicability to scientific signals remains underexplored. In this paper, we investigate the transferability and complementarity of foundation models from relevant time series domains, and study how to effectively leverage them to build a unified encoder for scientific time series. We first systematically evaluate relevant foundation models, showing the effectiveness of knowledge transfer to scientific tasks and their complementary strengths. Based on this observation, we propose STEP, a Scientific Time Series Encoder Pretraining framework via cross domain distillation. STEP introduces adaptive patching to handle extreme-length sequences and a statistics compensation scheme to accommodate diverse numerical scales. It further leverages cross-domain distillation to integrate knowledge from multiple foundation models into a unified encoder. By combining complementary representations across different domains, STEP learns general-purpose and transferable features tailored for scientific signals. Experiments on seven scientific time series tasks demonstrate that STEP provides both an effective structure and an effective pretraining paradigm, taking a STEP toward scientific time series representation learning.

cs.LG↗