arXiv subjects

Yu Tian

Publications and source records attributed to Yu Tian.

At least 19 recordsLinked to original sources

arXiv2026

Splitting Dynamics of Multiply Quantized Vortices in Holographic Superfluid of Finite Temperature

We study the splitting dynamics of multiply quantized vortices with winding numbers $n=5,6,7$ and $8$ in a two-dimensional holographic superfluid at finite temperature, by combining linear perturbation analysis of quasinormal modes with fully nonlinear real-time numerical simulations. Three new physical phenomena are revealed. First, the number of unstable modes no longer strictly follows the $2n-3$ formula as $n$ increases. For the vortex with $n=8$, the unstable mode with $p=2(n-1)$ is absent throughout the entire temperature range, so that only $2n-4$ unstable modes exist. Second, the transition of the dominant unstable mode with increasing temperature exhibits new characteristics. For vortices with $n\le 6$, the dominant mode changes sequentially as $p=2,3,\dots,n$, whereas for $n\ge 7$ jump-like transitions occur-for instance, for $n=7$ the dominant mode jumps from $p=2$ to $p=4$ at $T=0.325T_c$ and then directly to $p=7$ at $T=0.359T_c$, and for $n=8$ it jumps directly from $p=2$ to $p=8$ at $T=0.302T_c$. Third, a single splitting pattern of high-winding-number vortices can contain multiple sub-splitting patterns with distinct topological structures, as exemplified by the $l=4$ pattern of the $n=8$ vortex, which exhibits three sub-patterns at low, intermediate and high temperatures. The nonlinear simulations confirm the predictions of the linear stability analysis, and the implications of our results for cold-atom experiments are discussed.

hep-th↗

arXiv2026

PlantC2USeg: Cross-Scale Consistent Pre-Training for Few-Shot Unified Plant Point Cloud Segmentation

Modern crop breeding demands precise organ-level analysis for trait quantification, making plant point cloud segmentation (PPCS) increasingly important. However, conventional deep learning approaches rely heavily on densely annotated datasets that are labor-intensive to acquire. Unified PPCS adaptation from distribution-shifted examples with minimal additional training remains challenging. To address this, we propose PlantC2USeg, a deep transfer learning framework featuring cross-scale consistency learning to explicitly align features across spatial scales and an information-restricted decoding strategy that prevents reconstruction shortcuts and promotes robust adaptation. The resulting pre-training enables stable few-shot generalization across species and sensing conditions, while unified fine-tuning with inherited thresholds further reduces adaptation overhead. Under full supervision on Soybean3D, PlantC2USeg achieves the highest semantic IoU and instance mWCov among compared methods, at 91.91% and 94.62%. With 20 labeled samples, it leads both metrics at 89.78% and 90.27%; with only 10 samples, it retains the highest mWCov of 83.23% while achieving 83.19% IoU. Across HR3D, 10-shot transfer to tobacco, tomato, and sorghum averages 78.41% IoU and 79.42% mWCov, while 22-shot transfer to SYAU-Maize achieves the highest IoU and mRec at 92.75% and 93.51%. Furthermore, a leading category-averaged mIoU of 85.0% on ShapeNet Part demonstrates the framework's capability to handle diverse shape variations beyond agricultural domains. These results demonstrate that PlantC2USeg reduces overall adaptation effort under distribution shifts, enabling scalable plant phenotyping and transferable 3D representation learning beyond agriculture.

cs.CV↗

arXiv2026

Extremal non-rotating black holes have no fermionic Love

The static tidal Love numbers (TLNs) of $4$-dimensional black holes vanish for bosonic perturbations but are generically nonzero for fermions, with rare exceptions. In this paper, we show that for static, spherically symmetric black holes, fermionic TLNs vanish if and only if the black hole is extremal, in the sense that its horizon is degenerate. This follows from a closed formula for the static fermionic TLN of any asymptotically flat black hole, obtained by solving the static massless Dirac equation exactly on an arbitrary such spacetime and imposing regularity at the horizon. As applications, we analyze the Culetu--Simpson--Visser regular black hole and the loop-quantum-gravity remnant black holes, whose extremal configurations lead to vanishing fermionic yet nonvanishing bosonic TLNs.

gr-qc↗

arXiv2026

When Entropy Is Not Enough: Reclaiming Lost Semantics in LLM Output Length Prediction

Efficient LLM serving is often bottlenecked by the need to pad sequences to a fixed maximum length, and this wastes compute and degrades throughput. Predicting output lengths in advance makes it possible to adopt length-aware scheduling, and this reduces the overhead. This advantage is especially pronounced in long-context reasoning and reinforcement learning applications. Existing approaches, such as entropy-guided token pooling, use token-wise entropy as their primary signal, but they tend to ignore differences in semantic content across tokens. So, important tokens are often underweighted, and tokens carrying little information receive disproportionate emphasis. This hurts the reliability of length prediction. We introduce ESTP (Entropy-and-Semantic Token Pooling), a lightweight framework that addresses this issue by combining entropy with attention-based importance scores. These scores are derived directly from the self-attention weights computed during the LLM prefill phase, and this allows ESTP to capture both uncertainty and semantic importance with minimal additional computation. Since the framework reuses prefill activations, it adds almost no extra memory overhead and introduces only minimal latency. On the ForeLen benchmark, ESTP outperforms baseline methods, achieves better prediction accuracy and lower error rates in most scenarios. When integrated with a length-aware scheduler in end-to-end system tests, it further helps improve overall throughput and reduce the padding ratio. Our results offer a practical and effective building block for length-aware LLM serving systems.

cs.AI↗

arXiv2026

Characteristic evolution of conformal scattering: I. Scalar Waves in Minkowski Spacetime

We study the conformal scattering of massless scalar waves in Minkowski spacetime. The conformal scattering problem is formulated as a Goursat (characteristic initial-value) problem of the physical wave equation in compactified double-null coordinates, including the neighborhood of spatial infinity $i^0$. As null infinities $\mathcal{I}^\pm$ lie on the domain boundary by construction, asymptotic radiation is directly accessible. We consider three physical scenarios: free wave propagation, scattering off a P\"oschl--Teller (PT) potential, and the semi-linear $|\phi|^{n-1}\phi$ wave equation. For multipole numbers $\ell =0,1$, an explicit stencil, averaging along the spatial direction, yields globally second-order convergent results. For $\ell \ge 2$, an implicit stencil averaging along the temporal direction is required for numerical stability. Although the singular $i^0$ reduces the convergence of the radiation data on $\mathcal{I}^+$ to first order, Richardson extrapolation enhances the effective convergence rate to approximately $1.5$. For PT scattering, our method accurately computes scattering quantities, notably the phase shifts induced by the potential. In the semi-linear case, our method captures the physical signatures of a self-defocusing Kerr nonlinearity, including self-phase modulation and spectral broadening. The compactified double-null framework proves to be simple and efficient, suggesting a promising approach to the global evolution of conformal scattering.

gr-qc↗

arXiv2026

Agentic-SQL Revisited: Autonomy-Based Taxonomy and Empirical Benchmark Analysis for LLM Text-to-SQL

LLM-based Text-to-SQL progress is reported across heterogeneous benchmarks, backbones, and inference protocols, making cross-system comparison fragile. We reframe the field as a leaderboard aggregation: we collect the metrics authors themselves report and organize them along an inference-autonomy axis spanning constrained, in-context, iterative, agentic, and reasoning-internalized generation, with traceable provenance for every cell. To anchor the aggregation empirically, we run a focused case study on Spider, comparing 8B open-source backbones with and without chain-of-thought (CoT) supervision against few-shot DeepSeek~V3 and GLM-4 baselines. Four patterns emerge: Spider gains transfer unevenly to BIRD and Spider~2.0; autonomy buys robustness at non-trivial cost; reasoning internalization sits between answer-only decoding and externally orchestrated agents; and CoT gains concentrate on Hard and Extra-Hard queries. We release a Python harness mirroring the autonomy axis so that future methods can be added directly to the leaderboard.

cs.AI↗

arXiv2026

Ollivier's Ricci Curvature on Complex-weighted Graphs

Understanding the geometry of complex networks is critical for effective modeling and analysis across domains. While discrete notions of Ricci curvature have emerged as powerful tools for characterizing both local and global network structure, existing formulations are largely confined to undirected networks with real-valued weights. This limits the use of curvature-based analysis of directional and complex-weighted relations that arise naturally in many applications, from social and biological systems to quantum and signal-processing networks. In this work, we introduce a principled extension of Ollivier's Ricci curvature to complex-weighted graphs, which encompasses directed graphs as a special case. We establish fundamental theoretical properties of this new notion, including relations to the magnetic Laplacian and combinatorial upper and lower bounds that relate curvature to cycle structure in local neighborhoods. We further develop computational methods for curvature estimation and demonstrate their utility in community detection on directed networks.

cs.SI↗

arXiv2026

UniJEPA: A Unified Joint-Embedding Predictive Architecture for Task-Agnostic Visual World Modeling

Joint-Embedding Predictive Architectures (JEPAs) have emerged as a principled framework for self-supervised learning of world models in compact latent spaces, yet existing methods are fragmented: some predict masked parts of a single image in latent space (I-JEPA), others learn to predict global photometric transformations (Image World Models), while video-scale JEPAs predict future temporal states and are post-trained for action-conditioned planning (V-JEPA~2, DINO-World, DINO-WM). These objectives are treated as distinct recipes with separate encoders, predictors, and anti-collapse regularizers, hindering a single model from unifying image-level and video-level world modeling. We present UniJEPA, a unified JEPA that jointly learns photometric prediction (image-level transformations) and temporal prediction (video-level next-state dynamics) in one shared latent space. A single end-to-end objective, composed of a next-embedding prediction loss and a Gaussian regularizer, yields a provably anti-collapse encoder-predictor pair trainable from raw pixels without EMA, stop-gradient, or pre-trained encoders. We show that the same latent space supports controllable abstraction: photometric prediction learns invariant structure while temporal prediction learns equivariant dynamics. After action-conditioned post-training on offline trajectories, UniJEPA enables zero-shot planning by treating goal features as prediction targets. On image, video, and control benchmarks, UniJEPA matches or surpasses task-specific JEPAs while requiring a single loss hyperparameter, and plans up to tens of times faster than generative world models at comparable accuracy.

cs.CV↗

arXiv2026

Longitudinal 3D Foundation Modeling for Neoadjuvant Breast Cancer Response Prediction from Serial DCE-MRI

Pathologic complete response (pCR) is an important endpoint in neoadjuvant chemotherapy (NAC) for breast cancer, and predicting pCR from imaging during treatment could support treatment response assessment. Many existing imaging-based approaches rely on a single static timepoint, which fails to capture changes that occur during treatment. In this work, we present a longitudinal framework that combines a frozen 3D foundation encoder (Pillar-0) with our Temporal Dynamics Network (TDN) to predict treatment response from serial Dynamic Contrast-Enhanced (DCE) MRI acquired across four clinical timepoints from pre-treatment to pre-surgery. The TDN combines time-aware volumetric embeddings with clinical and treatment data to predict pCR. Evaluated on 982 patients from the combined I-SPY2 and ACRIN-6698 cohort, the proposed model achieves strong performance across all reported metrics when longitudinal 3D imaging is fused with clinical data (test AUROC: 73.6%, balanced accuracy: 69.1%). While clinical variables provide the strongest individual predictive signal, longitudinal 3D imaging contributes complementary information when fused with clinical data, improving pCR prediction. Our source code is available at: https://github.com/omarftt/longitudinal_temporal_pillar.

eess.IV↗

arXiv2026

DiffuseAgent-MI: Distributionally-Grounded,Tool-Integrated Self-Evolving Agents for Faithful Visual Reasoning

Tool-integrated vision-language agents have made remarkable progress on compositional and multi-step visual reasoning. Yet their outputs frequently exhibit unfaithfulness: the stated reasoning path diverges from the computation that actually produced the answer, undermining reliability in safety-critical applications. We present DiffuseAgent-MI, a self-evolving agent whose perceptual grounding is governed by a KL-minimal energy model over feature units, providing a distributional view of visual mechanistic interpretability. The agent learns an energy landscape that softly constrains generated samples to lie near the native prior conditioned on the chosen interpretable unit, closing the gap between the explanation and the internal representation. A verifier then supplies trajectory-level faithfulness rewards, and a repair branch re-conditions the energy when the verifier flags an unfaithful step. On GeoQA, SciVis, VQA-v2 and an in-house multimodal reasoning set, DiffuseAgent-MI improves accuracy by up to 5.1 points over prior self-evolving agents while more than doubling mutual-information faithfulness and human-interpretability agreement. Our analysis shows the energy term and the verifier are complementary: the former guarantees distributional faithfulness, the latter trajectory-level faithfulness, and only their combination closes both gaps.

cs.CV↗

arXiv2026

Physics-Informed Neural Networks for 2D Plane Wave Scattering in Arbitrary Dielectric Structures

In this paper, we introduce a meshless physics-informed neural network based computational framework for solving two-dimensional electromagnetic wave scattering in inhomogeneous media. The framework embeds frequency-domain Maxwell equations and radiation boundary conditions directly into the neural network loss function, enabling accurate prediction of scattered fields for both transverse magnetic (TM) and transverse electric (TE) polarizations across various dielectric configurations. Application of the method to single-cylinder, concentric multilayer cylindrical shells, three arbitrarily arranged cylinders, and composite irregular structures demonstrates that for the TM polarization, all relative $L^{2}$ errors mostly remain at particularly low levels of $\le0.1$. For the TE polarization, sharp variations of the dielectric properties of scatterers lead to singularities in the governing equations, which result in decreased accuracy of the method. This challenge is overcome by introducing at dielectric boundaries a hyperbolic-tangent smoothing function. This procedure significantly improves the accuracy of the method, with the corresponding results closely matching the predictions of the finite-difference time-domain method. This framework exhibits stable convergence behavior across all of the investigated configurations, thus confirming its robustness and scalability to complex electromagnetic scattering problems.

physics.optics↗

arXiv2026

Media Meets Communication in 6G: Fundamentals, Key Technologies, and Applications

The rapid advancement of sixth-generation (6G) networks is accelerating the convergence of media intelligence and communication intelligence, driving media communication beyond conventional bit-level delivery toward intelligent, semantic-aware, and generative paradigms. Emerging media services require not only high data rates and low latency, but also semantic awareness, perceptual quality assurance, adaptive resource orchestration, trustworthy content processing, and personalized media generation. Meanwhile, media technologies are evolving from handcrafted signal processing and conventional coding toward artificial intelligence (AI)-driven representation learning, content understanding, and generative reconstruction. Motivated by these trends, this paper presents a systematic survey of media communication technologies for 6G vision communication by revisiting the evolution of communication and media technologies and clarifying the intrinsic relationship between media content processing and wireless transmission. We introduce a unified framework consisting of four key dimensions: AI-driven media technologies, media-aware wireless transmission, large model-enabled media communication, and intelligent network infrastructures. Specifically, AI-driven media technologies encompass media coding, content understanding, quality assessment, security and compliance detection, and AIGC-enabled media generation, while media-aware wireless transmission is examined from three complementary perspectives: semantic joint source-channel optimization, which jointly encodes task-relevant semantic information; source-aware transmission optimization, which leverages media characteristics for channel adaptation, prediction, and compensation; and channel-aware source optimization, which adapts media coding and reconstruction based on real-time channel conditions.

cs.IT↗

arXiv2026

Quantum Mpemba effect in holography

We investigate the quantum Mpemba effect in a holographic superfluid, in which states with stronger initial symmetry breaking relax faster toward the symmetry-restored equilibrium. We demonstrate its emergence by identifying the shifted free energy computed from the energy flux into the black hole horizon as monotonic distance measure. By decomposing the nonlinear bulk dynamics based on quasinormal modes, we reveal that the anomalous relaxation is governed by a dynamical competition in which the slowest-decaying mode is suppressed while the second mode is amplified. These findings provide a holographic perspective on the quantum Mpemba effect in nonequilibrium relaxation involving strongly coupled degrees of freedom.

hep-th↗

arXiv2026

Formation of holographic vortex in a rotating shell-shaped superfluid

We investigate the holographic superfluid dynamics subjected to external rotation on a spherical geometry. Through a linear perturbation analysis, we identify several dynamically unstable phases in the phase diagram, each characterized by distinct unstable modes. Employing fully nonlinear numerical simulations, we further demonstrate that these unstable modes generically drive the system into vortex-antivortex configurations with definite winding numbers, determined by the symmetry of the corresponding unstable modes.

hep-th↗

arXiv2026

Fermionic Love number of higher-dimensional Reissner-Nordstr\"om black holes

In this paper, we generalize our previous work on the fermionic tidal Love numbers (TLNs) to higher-dimensional Reissner-Nordstr\"om black holes. The massless Dirac equation is solved in $D$-dimensional spacetime using ingoing Eddington coordinates and regular tetrads. After identifying the regular solution branch, we extract the fermionic TLNs from its asymptotic behavior at infinity. The resulting TLNs exhibit a rich dimension-dependent structure that generalizes the four-dimensional case. Unlike bosonic TLNs, which vanish for certain values of the total angular momentum $l$ in dimensions $D>4$, fermionic TLNs remain non-zero for all $l$ and $D \geq 4$, except for extremal black holes. Moreover, the $l$-dependence weakens as $D$ increases, disappearing entirely in the infinite-dimensional limit. These results provide new insights into black hole responses to fermionic perturbations in higher-dimensional spacetimes.

gr-qc↗

arXiv2026

TelecomGPT-R1: A Unified Open-Source Reasoner for the Telecom Stack

Telecommunications is a high-leverage domain for large language model (LLM)-based reasoning because routine engineering workflows require joint grounding in normative specifications, operational telemetry, vendor-specific fault evidence, and exact RF/network calculations. However, current LLM integration in telecom remains bottlenecked by a two-sided capability gap: generic reasoners often lack telecom-specific grounding, while domain-specific telecom LLMs remain limited in structured, multi-step reasoning. To bridge this gap, we release TelecomGPT-R1-9B, a unified open-source telecom reasoner that ranks top-performing on the GSMA open telco leaderboard. Specifically, we curate a 67,427-example supervised fine-tuning (SFT) corpus organized around four complementary reasoning axes: protocol, knowledge, modeling, and fault. The corpus is built from axis-matched public web sources and enhanced through axis-specific chain-of-thought (CoT) generation and prefix-continuation self-validation. Starting from Qwen3.5-9B, we further develop a two-stage post-training recipe. First, multi-teacher low-rank adaptation (LoRA)-based SFT injects telecom knowledge and induces axis-specific reasoning formats. Second, group relative policy optimization (GRPO), stabilized by decoupled clip and dynamic sampling policy optimization (DAPO), optimizes the policy using four axis-aligned binary verifier rewards. Across seven public telecom benchmarks, TelecomGPT-R1-9B ranks first among open-source telecom LLMs and achieves a seven-axis mean comparable to state-of-the-art closed-source frontier reasoners.

cs.CL↗

arXiv2026

Tidal Love numbers and the dynamical instability of AdS bubbles

In this work, we study non-radial perturbations of AdS bubbles and their tidal Love numbers (TLNs). The odd- and even-parity TLNs are computed up to $l=6$ in the limit $k \to \infty$. The odd-parity TLNs are found to be negative, while the even-parity TLNs are positive for $\upsilon^2_s=-1$. As $l$ increases, the tidal Love numbers approach zero. The TLNs of the even-parity sector up to order $l=41$ are also calculated over the entire parameter space of $k$, from $0$ to $\infty$. We find that in the region where $p/\sigma>0$, an increasing number of TLNs become negative as $l$ increases. For $l = 41$, the highest order we have examined, the TLNs are negative everywhere except in a narrow region very close to the zero of $p/\sigma$, which agrees well with the instability criterion in the eikonal limit for self-gravitating membranes proposed by Yang {\it et al.}\ [P. R. L. {\bf 130}, 011402 (2023)].

gr-qc↗

arXiv2026

Generalizing Perron--Frobenius theory and eigenvector-based centralities to networks with complex edge weights

A fundamental concept in linear algebra and its applications to network analysis is the Perron--Frobenius (PF) theorem, which underpins eigenvector-based centrality measures such as eigenvector centrality, PageRank, and hubs and authorities. By invoking the PF theorem, we know for strongly connected networks with positive edge weights that the eigenvector corresponding to the largest eigenvalue of the weight matrix yields a well-defined centrality measure (namely, eigenvector centrality). Traditional formulations of the PF theorem and associated centrality measures assume that networks have real-valued weights. However, many networks in areas such as quantum information, quantum chemistry, electrodynamics, and machine learning have complex-valued edge weights. In this paper, we study generalizations of the PF theorem to complex-valued matrices, establish connections between these generalizations, and propose generalized eigenvector-based centrality measures to analyzing node importances in networks with complex edge weights. We also prove results about the existence of complex-weighted networks that satisfy generalized PF properties and calculate associated centrality measures for several examples, which we draw from application areas such as electron transport, circuit analysis, mathematical chemistry, and communication networks.

math.SP↗