arXiv subjects

Yu Tian

Publications and source records attributed to Yu Tian.

At least 55 records · Page 3Linked to original sources

arXiv2026

CREST: Curvature-Regulated Event-Centric Sampling for Efficient Long-Video Understanding

Selecting informative frames from long videos is a combinatorial problem that existing methods address either through efficient heuristics without explicit modeling of query-conditioned temporal structure, or through multi stage retrieval pipelines with substantial preprocessing cost. We propose \textbf{CREST}, a training-free frame selection method grounded in the temporal geometry of query--frame relevance. CREST is based on the observation that relevance over time exhibits structured local variation: sharp curvature around salient events and flatter regions in redundant segments. By using local curvature to guide selection, CREST allocates a fixed frame budget more effectively across brief decisive events and slowly evolving evidence. Under a fixed backbone and frame budget, CREST achieves higher accuracy than AKS, a lightweight relevance--coverage baseline, on LongVideoBench and VideoMME, while retaining 93--95\% of the accuracy of MIRA, a stronger multi-stage retrieval pipeline, at only 3--4\% of its preprocessing cost.\footnote{Code and implementation details are included in the supplementary material and will be released publicly upon acceptance.} On TempRel, our diagnostic benchmark for temporal frame selection, CREST achieves a 6.88\% relative improvement over AKS. Pairwise LLM-as-a-judge evaluation further shows that CREST-selected frames yield more coherent frame-conditioned descriptions, with win rates of 60.58\% and 54.50\% on the two benchmarks. These results show that local temporal geometry provides a simple and efficient basis for long-video frame selection.

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

Holographic Turbulence and Numerical Estimate of the Fractal Dimension of the Turbulent Horizon

We numerically study two-dimensional turbulence driven by a scalar operator within the framework of the AdS/CFT correspondence, where the external driving source is used to sustain a quasi-steady turbulent state. We propose a simple and efficient evolution scheme within the Bondi-Sachs formalism. Applying this scheme to numerically solve the full nonlinear equations of motion, we obtain a turbulent black hole in asymptotically $\mathrm{AdS}_4$ spacetime. The inverse energy cascade and the corresponding energy spectrum of both decaying and driven dual turbulence are analyzed. The scalar driving leads to a compressible-energy-dominated flow, and the corresponding power law scaling, $E(k)\propto k^{-1.79}$, agrees well with previous simulations of two-dimensional turbulence in weakly coupled compressible fluids in fluid dynamics. This differs from the Kolmogorov $-5/3$ scaling law. Furthermore, we perform a direct numerical estimate of the fractal structure of the turbulent black hole, obtaining a fractal dimension $D\approx 2.65$, which suggests an interesting universality in the fractal dimension.

hep-th↗

arXiv2026

MultiFair: Multimodal Balanced Fairness-Aware Medical Classification with Dual-Level Gradient Modulation

Medical decision systems increasingly rely on data from multiple sources to ensure reliable and unbiased diagnosis. However, existing multimodal learning models fail to achieve this goal because they often overlook two critical challenges. First, various data modalities may learn unevenly, thereby converging to a model biased towards certain modalities. Second, the model may emphasize learning on certain demographic groups causing unfair performances. The two aspects can influence each other, as different data modalities may favor respective groups during optimization, leading to both imbalanced and unfair multimodal learning. This paper proposes a novel approach called MultiFair for multimodal medical classification, which addresses these challenges with a dual-level gradient modulation process. MultiFair dynamically modulates training gradients regarding the optimization direction and magnitude at both data modality and group levels. We evaluate MultiFair on three real-world medical classification datasets with diverse demographic attributes,including multiclass classification and missing-modality settings. Experimental results demonstrate its effectiveness.

cs.LG↗

arXiv2026

Rethinking Robust Adversarial Concept Erasure in Diffusion Models

Concept erasure methods aim to remove specific unsafe target concepts in diffusion models while preserving image generation utility. To address the vulnerability that erased concepts can be easily recovered under adversarial attacks, adversarial concept erasure methods integrate adversarial optimization into the concept erasure process. However, existing adversarial concept erasure methods face a trade-off between robustness and computational cost. We attribute this to adversarial optimization techniques that use random samples to approximate the adversarial objective function. Adversarial optimization that uses a small number of samples fails to produce adversarial embeddings that accurately capture the target concept space. To mitigate this limitation, we propose Semantic-Guided Adversarial Optimization, which uses a single sample to produce adversarial embeddings that better capture the target concept space. We also propose Semantic-Guided Concept Erasure, which automatically maps the target concept to a semantically similar surrogate. Extensive experiments on not-safe-for-work content, artistic styles, and object-related concepts demonstrate that our method, S-GRACE (Semantic-Guided Robust Adversarial Concept Erasure) achieves state-of-the-art erasure robustness and superior image generation utility, with significantly lower computational cost than existing methods. Our code is available at https://github.com/Qhong-522/S-GRACE.

cs.CV↗

arXiv2026

Fermionic Love number of higher-dimensional Reissner-Nordström black holes

In this paper, we generalize our previous work on the fermionic tidal Love numbers (TLNs) to higher-dimensional Reissner-Nordström 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

Internal structure of Hayward black holes

Regular black holes, free of central singularities, provide an ideal laboratory for probing the geometric structure of spacetime. The global structure of some regular black holes, e.g. Hayward black hole, features an event horizon and a Cauchy horizon, raising fundamental questions about the latter's stability. In this work, we investigate collapse of a scalar field in Hayward spacetime. Under weak scalar perturbations, the inner horizon maintains a stable finite radius. In the circumstance of a strong scalar field, the inner horizon shrinks to zero volume, accompanied by the formation of a spacelike singularity. The Hayward geometry is effectively converted into a Schwarzschild-like geometry. Furthermore, the strength of the scalar field governs the contraction dynamics of the inner horizon. As the parameter $p$ of the initial profile for the scalar field approaches the critical threshold ${p_*}$, the radius of the inner horizon ${r_{-}}$ exhibits a universal scaling behavior: ${r_{-}}\propto{|p - {p_*}|^γ}$, with a critical exponent $γ\approx 0.5$.

gr-qc↗

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 $p/σ>0$, 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 $p/σ$, 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↗

arXiv2026

GEMINI: Generalized Ensnarlment Measure from Incomplete-linkage of Network-network Interactions

Spatially embedded networks are central to many physical and biological systems, where geometry and connectivity jointly shape structure and function. Examples abound across the scales of biological organization, from network-like membrane-bound organelles in the cell to mesoscale tissue organization of multiple distinct flow networks in organs and beyond. In each of these cases, the complexity of the architectures has heretofore frustrated our ability to link mechanism or regulation of these structures to reduced modeling or even relevant characterization, putting structure-function relationships largely out of reach. Complex, functional spatial networks can be decomposed into tree-like and cyclic substructures, but we still lack both an understanding of how these elements intertwine to give rise to function, and the tools to holistically quantify both the topological and geometric aspects of these features in their full network context. To close this gap, we here introduce GEMINI, a topology and geometry aware operator that directly characterizes incomplete linking and more general spatial associations between edges in spatially embedded network architectures. GEMINI contains information on edge-edge association through an incomplete version of the Gauss linking integral which simultaneously endows it with topological sensitivity when collections of edges form linked assemblages. Validation on both synthetic lattices and on mouse brain vasculature data demonstrates that GEMINI systematically captures and classifies the complexity of structural organizations. Our results provide a general approach for analyzing spatial networks in realistic data, where topology and geometry together determine function, thus opening the door to a more complete understanding of structure-function relationships across a broad set of biological examples where complex network organization is key.

physics.comp-ph↗

arXiv2026

Safe-FedLLM: Delving into the Safety of Federated Large Language Models

Federated learning (FL) addresses privacy and data-silo issues in the training of large language models (LLMs). Most prior work focuses on improving the efficiency of federated learning for LLMs (FedLLM). However, security in open federated environments, particularly defenses against malicious clients, remains underexplored. To investigate the security of FedLLM, we conduct a preliminary study to analyze potential attack surfaces and defensive characteristics from the perspective of LoRA updates. We find two key properties of FedLLM: 1) LLMs are vulnerable to attacks from malicious clients in FL, and 2) LoRA updates exhibit distinct behavioral patterns that can be effectively distinguished by lightweight classifiers. Based on these properties, we propose Safe-FedLLM, a probe-based defense framework for FedLLM, which constructs defenses across three levels: Step-Level, Client-Level, and Shadow-Level. The core concept of Safe-FedLLM is to perform probe-based discrimination on each client's local LoRA updates, treating them as high-dimensional behavioral features and using a lightweight classifier to determine whether they are malicious. Extensive experiments demonstrate that Safe-FedLLM effectively improves FedLLM's robustness against malicious clients while maintaining competitive performance on benign data. Notably, our method effectively suppresses the impact of malicious data without significantly affecting training speed, and remains effective even under high malicious client ratios.

cs.CR↗

arXiv2026

OncoTriad-QA: A Patient-Level Radiology-Pathology-Genomics Benchmark for Pan-Cancer Reasoning

Cancer diagnosis and characterization require integrating complementary evidence from radiology, pathology, genomics, and clinical metadata. However, most medical large language model (LLM) and vision-language model (VLM) benchmarks focus on isolated modalities or narrow image-text tasks, leaving patient-level oncology assessment across multiple evidence streams largely untested. We introduce OncoTriad-QA, a patient-level radiology-pathology-genomics benchmark for pan-cancer question answering. OncoTriad-QA contains 86.1k semantic questions across 9,281 TCGA patient cases from 32 cancer cohorts, aligning CT/MRI radiology, whole-slide histopathology, somatic mutations, copy-number alterations, DNA methylation, bulk RNA-seq, and clinical metadata. Case-specific annotations are constructed through a source-grounded LLM-assisted pipeline using curated labels, diagnostic reports, molecular profiles, and modality-derived evidence as primary sources of truth, with automated consistency checks and clinician review. We also introduce OncoVLM, a reference multimodal model that maps modality-native radiology, pathology, DNA methylation, and RNA-seq evidence into an LLM interface through learned projectors. Experiments show that existing general-purpose and medical LLMs remain limited on comprehensive pan-cancer QA, especially when questions require integrating imaging findings, tumor morphology, and molecular evidence. After fine-tuning on OncoTriad-QA, OncoVLM exceeds MedGemma-4B by an average of 10.7 points when using MCQ accuracy and BERTScore-F1, with consistent gains across multiple-choice and open-ended questions under radiology-only, pathology-only, and all-available settings. These results demonstrate the benchmark's value for training and evaluating models for integrated cancer question answering.

cs.CL↗

arXiv2026

Can Oscillatory and Persistent Nonlinearities Be Bridged in Black Hole Ringdown?

Quadratic quasinormal modes (QQNMs) and Christodoulou memory effect are key nonlinear phenomena in gravitational wave physics. QQNMs characterize the near zone nonlinear response of a perturbed black hole, whereas the memory effect is a nonlinear remnant imprinted at null infinity by outgoing radiation. This naturally raises the question of whether and in what sense the two can be bridged. We show that they are related through bridge coefficients which depend primarily on remnant black hole parameters during ringdown. Future space-based gravitational wave detectors can probe this relation. These results provide a new avenue for testing gravity and a fresh perspective on the nonlinear regime of general relativity.

gr-qc↗

arXiv2026

Transition of vortex dipole dynamics in holographic superfluids

Using holographic duality, we reveal a transition in vortex dipole dynamics below a critical dipole size in strongly interacting superfluids, characterized by a significant suppression of mutual friction. In the bulk, this transition is triggered by a topological reconnection of vortex tubes, which disconnects the boundary vortices from the black hole horizon and forms a \textit{U-pipe}. Consequently, the post-transition evolution is governed by the contraction of the bulk \textit{U-pipe} rather than the mutual friction associated with the horizon, revealing a scale-dependent dissipation mechanism. We further show that this reconnection persists over a broad temperature range, even when the transition becomes unobservable at high temperatures. Our results provide a dissipation-based interpretation for the anomalous critical dipole scale observed in strongly interacting cold-atom experiments, and suggest the existence of distinct dissipative regimes in strongly interacting superfluids.

hep-th↗

arXiv2026

Explicit and covariant formula for thermodynamic volume in extended black hole thermodynamics

In extended black hole thermodynamics, the cosmological constant and other couplings are treated as thermodynamic variables, yielding the first law $\tildeδM = T\tildeδS+Ω\tildeδJ +\mathcal{V} \tildeδP+\cdots$, where $P\equiv -\fracΛ{8π}$. A long-standing conceptual gap in this framework is that, unlike $M$, $T$, $S$, $Ω$, and $J$, the thermodynamic volume $\mathcal{V} $ lacks a first-principles definition and can only be deduced from other thermodynamic quantities. This deficiency indicates that the underlying origin of $\mathcal{V} $ remains poorly understood. In this paper, we resolve this issue and provide an explicit, covariant formula for $\mathcal{V} $. We demonstrate that $\mathcal{V} $ (and the conjugate quantities of other couplings) universally decomposes into two contributions: one arising from the explicit coupling dependence of the Lagrangian, and the other from the response of the fundamental dynamical fields. This clarifies the physical meaning of the thermodynamic volume and places it on the same footing as other intrinsic thermodynamic quantities.

gr-qc↗