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Yi Yang

Publications and source records attributed to Yi Yang.

At least 199 records · Page 11Linked to original sources
arXiv2026

Is This Edit Correct? A Multi-Dimensional Benchmark for Reasoning-Aware Image Editing

Diffusion-based image editing has achieved strong visual fidelity under natural language instructions, yet most existing systems still operate at the level of surface instruction following, without reasoning about the implicit contextual constraints embedded in real user requests. This often leads to visually plausible but logically inconsistent edits. In this work, we introduce RE-Edit, a benchmark for REasoning-aware image Editing that evaluates image editing systems across five complementary reasoning dimensions: physical, environmental, cultural, causal, and referential. RE-Edit comprises 1,000 carefully curated samples, each designed such that visual plausibility alone is insufficient and correct editing requires satisfying implicit logical constraints. To support fine-grained analysis, we establish dimension-aligned evaluation criteria and conduct a comprehensive study of ten open-source and two commercial image editing models. Our results show that even advanced systems frequently struggle with implicit multi-dimensional reasoning despite producing high-quality visuals. We further present a lightweight reasoning-guided post-edit baseline as an initial exploration, illustrating how inserting explicit reasoning can help mitigate such failures in a model-agnostic manner.

cs.HC↗
arXiv2026

Black holes surrounded by dark matter spike: Spacetime metrics and gravitational wave ringdown waveforms

The supermassive black holes at the centers of galaxies may be surrounded by dark matter spike, which could leave detectable imprints on the gravitational wave signals they emit. In this work, combining the mass model of M87, we present black hole analytical solutions in dark matter spike from the Tolman-Oppenheimer-Volkof equations. Meanwhile, the gravitational waves emitted at the ringdown phase of the black holes under the axial gravitational perturbation is investigated, and compare them with the Schwarzschild black hole. The main research methods are time domain integration method and continued fraction method. Besides, the impact of the different dark matter parameters on the quasinormal frequencies is investigated and its detectability on these impacts based on the space-based detectors. Our results indicate that the impacts of dark matter spike on the quasinormal frequencies of black holes can reach up to the order of $10^{-4}$. These results may provide some help in further exploring the impact of dark matter spike on the black holes and their related gravitational wave phenomena.

gr-qc↗
arXiv2026

Physically Plausible Human-Object Rendering from Sparse Views via 3D Gaussian Splatting

Rendering realistic human-object interactions (HOIs) from sparse-view inputs is a challenging yet crucial task for various real-world applications. Existing methods often struggle to simultaneously achieve high rendering quality, physical plausibility, and computational efficiency. To address these limitations, we propose HOGS (Human-Object Rendering via 3D Gaussian Splatting), a novel framework for efficient HOI rendering with physically plausible geometric constraints from sparse views. HOGS represents both humans and objects as dynamic 3D Gaussians. Central to HOGS is a novel optimization process that operates directly on these Gaussians to enforce geometric consistency (i.e., preventing inter-penetration or floating contacts) to achieve physical plausibility. To support this core optimization under sparse-view ambiguity, our framework incorporates two pre-trained modules: an optimization-guided Human Pose Refiner for robust estimation under sparse-view occlusions, and a Human-Object Contact Predictor that efficiently identifies interaction regions to guide our novel contact and separation losses. Extensive experiments on both human-object and hand-object interaction datasets demonstrate that HOGS achieves state-of-the-art rendering quality and maintains high computational efficiency.

cs.GR↗
arXiv2026

TASU2: Controllable CTC Simulation for Alignment and Low-Resource Adaptation of Speech LLMs

Speech LLM post-training increasingly relies on efficient cross-modal alignment and robust low-resource adaptation, yet collecting large-scale audio-text pairs remains costly. Text-only alignment methods such as TASU reduce this burden by simulating CTC posteriors from transcripts, but they provide limited control over uncertainty and error rate, making curriculum design largely heuristic. We propose \textbf{TASU2}, a controllable CTC simulation framework that simulates CTC posterior distributions under a specified WER range, producing text-derived supervision that better matches the acoustic decoding interface. This enables principled post-training curricula that smoothly vary supervision difficulty without TTS. Across multiple source-to-target adaptation settings, TASU2 improves in-domain and out-of-domain recognition over TASU, and consistently outperforms strong baselines including text-only fine-tuning and TTS-based augmentation, while mitigating source-domain performance degradation.

eess.AS↗
arXiv2026

Rendering Multi-Human and Multi-Object with 3D Gaussian Splatting

Reconstructing dynamic scenes with multiple interacting humans and objects from sparse-view inputs is a critical yet challenging task, essential for creating high-fidelity digital twins for robotics and VR/AR. This problem, which we term Multi-Human Multi-Object (MHMO) rendering, presents two significant obstacles: achieving view-consistent representations for individual instances under severe mutual occlusion, and explicitly modeling the complex and combinatorial dependencies that arise from their interactions. To overcome these challenges, we propose MM-GS, a novel hierarchical framework built upon 3D Gaussian Splatting. Our method first employs a Per-Instance Multi-View Fusion module to establish a robust and consistent representation for each instance by aggregating visual information across all available views. Subsequently, a Scene-Level Instance Interaction module operates on a global scene graph to reason about relationships between all participants, refining their attributes to capture subtle interaction effects. Extensive experiments on challenging datasets demonstrate that our method significantly outperforms strong baselines, producing state-of-the-art results with high-fidelity details and plausible inter-instance contacts.

cs.CV↗
arXiv2026

RefineAnything: Multimodal Region-Specific Refinement for Perfect Local Details

We introduce region-specific image refinement as a dedicated problem setting: given an input image and a user-specified region (e.g., a scribble mask or a bounding box), the goal is to restore fine-grained details while keeping all non-edited pixels strictly unchanged. Despite rapid progress in image generation, modern models still frequently suffer from local detail collapse (e.g., distorted text, logos, and thin structures). Existing instruction-driven editing models emphasize coarse-grained semantic edits and often either overlook subtle local defects or inadvertently change the background, especially when the region of interest occupies only a small portion of a fixed-resolution input. We present RefineAnything, a multimodal diffusion-based refinement model that supports both reference-based and reference-free refinement. Building on a counter-intuitive observation that crop-and-resize can substantially improve local reconstruction under a fixed VAE input resolution, we propose Focus-and-Refine, a region-focused refinement-and-paste-back strategy that improves refinement effectiveness and efficiency by reallocating the resolution budget to the target region, while a blended-mask paste-back guarantees strict background preservation. We further introduce a boundary-aware Boundary Consistency Loss to reduce seam artifacts and improve paste-back naturalness. To support this new setting, we construct Refine-30K (20K reference-based and 10K reference-free samples) and introduce RefineEval, a benchmark that evaluates both edited-region fidelity and background consistency. On RefineEval, RefineAnything achieves strong improvements over competitive baselines and near-perfect background preservation, establishing a practical solution for high-precision local refinement. Project Page: https://limuloo.github.io/RefineAnything/.

cs.CV↗
arXiv2026

The Double-Peaked Calcium-Strong SN 2025coe: Progenitor Constraints from Early Interaction and Ejecta Asymmetries

Supernova (SN) 2025coe at a distance of $\sim$25 Mpc is the second-closest calcium-strong (CaST) transient. It was discovered at a large projected offset of $\sim$34 kpc from its potential host galaxy NGC 3277. Multiband photometry of SN 2025coe indicates the presence of two peaks at day $\sim$2 and day $\sim$11 after explosion. Modeling the bolometric light curve, we find that the first peak can be reproduced either by shock cooling of a compact envelope ($R_\mathrm{env}$ $\approx $6-40 $R_{\odot}$; $M_\mathrm{env}$ $\approx $0.1-0.2 $M_{\odot}$) or by interaction with close-in circumstellar material (CSM; $R_{\mathrm{CSM}} \lesssim 6 \times10^{14}$ cm), or a combination of both. The second peak is dominated by radioactive decay of $^{56}$Ni ($M_{\mathrm{ej}} \approx $0.4-0.5 $M_{\odot}$; $M_{^{56}\mathrm{Ni}} \approx 1.4 \times 10^{-2}$ $M_{\odot}$). SN 2025coe rapidly evolves from the photospheric phase dominated by He I P-Cygni profiles to nebular phase spectra dominated by strong [Ca II] $λλ$7291, 7323 and weak [O I] $λλ$6300, 6364 emission lines. Simultaneous line profile modeling of [Ca II] and [O I] at nebular phases shows that an asymmetric core-collapse explosion of a low-mass ($\lesssim$3.3 $M_{\odot}$) He-core progenitor can explain the observed line profiles. Alternatively, lack of local star formation at the site of the SN explosion combined with a low ejecta mass is also consistent with a thermonuclear explosion due to a low-mass hybrid He-C/O white dwarf + C/O white dwarf merger.

astro-ph.HE↗
arXiv2026

Neutral-Fermion constructions of factorial $gp$-and $gq$-Functions

We develop neutral-fermionic constructions for the factorial $gp$-and $gq$-functions introduced by Nakagawa and Naruse, which are respectively dual to the factorial $GQ$- and $GP$-functions of Ikeda and Naruse. In particular, we realize the factorial $GP$-, $GQ$- and $gq$-functions as vacuum expectation values. As applications, we obtain, Jacobi--Trudi type determinantal formulas for the transition coefficients between functions with different equivariant parameters for $gq$ and its dual $GP$, as well as a Pfaffian formula for the factorial $gq$-functions. We further prove a remarkable coincidence among the transition coefficients for parameter changes for $gp$, $gq$, $GQ$, and $GP$. These coefficients admit a description in terms of factorial Grothendieck polynomials of type A.

math.CO↗
arXiv2026

Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Action

Foundation models have demonstrated impressive performance in enhancing healthcare efficiency across a wide range of medical applications. Nevertheless, their limited ability to perceive, understand, and interact with the physical world significantly constrains their effectiveness in real-world clinical workflows, where safety-critical decision-making and physical execution are tightly coupled. Recently, embodied artificial intelligence (AI) has emerged as a promising physical-interactive paradigm for intelligent healthcare, enabling agents to operate in complex medical environments. As research in this area rapidly expands, understanding how intelligent agents function as integrated, end-to-end systems in clinical environments becomes increasingly critical. However, existing surveys on medical embodied AI largely emphasize individual aspects or functional components, lacking a unified system-level organization of the field. To support and consolidate recent advances, we systematically survey the core components of medical embodied AI, with a particular emphasis on the coordinated integration of perception, decision-making, and action. We further review representative medical applications and relevant datasets, and we analyze the major challenges encountered in real-world clinical practice. Finally, we discuss key directions for future research in this rapidly evolving field. The associated project can be found at https://github.com/VMVLab/Medical_Embodied_AI_Paper_List.

cs.AI↗
arXiv2026

Enhancement of the Rate of Tidal Disruption Events in Active Galactic Nuclei due to the Sweeping Secular Resonance Mechanism

Tidal disruption event (TDE) rates in active galactic nuclei (AGN) consistently exceed predictions from two-body relaxation, particularly in post-starburst and green valley galaxies. We explain this excess with a new mechanism: a sweeping secular resonance (SSR) driven by an intermediate-mass companion (IMC) and a depleting gaseous disk. As the disk mass declines, a resonance between stellar and IMC orbital precession sweeps through the nuclear cluster, exciting stellar eccentricities to near unity on orbital timescales far faster than gravitational relaxation. Our analytical framework, validated by N-body simulations (REBOUND), shows this mechanism requires IMC-to-SMBH mass ratios of $q \geq 10^{-3}$, disk mass ratio $p \geq 10^{-3}$, and few Myr-scale disk depletion. It is highly effective for co-orbiting IMCs but negligible for counter-orbiting ones. The TDE rate peaks at $10^{-3}-10^{-2}$ per galaxy per year for a depletion timescale $τ_{\rm dep} \sim 10$ Myr. Even lower-mass IMCs can produce significant enhancements with compact, long-lived disks. Our model naturally explains elevated AGN TDE rates and implies that a high TDE incidence is a potential tracer of hidden parsec-scale IMCs, offering testable predictions for future AGN monitoring.

astro-ph.GA↗
arXiv2026

Structural Diversity Drives Disruptive Scientific Innovation

Scientific innovation increasingly depends on collaboration, yet the organizational structure that fosters breakthrough ideas remains poorly understood. Existing metrics - such as team size or compositional diversity - capture readily observable characteristics but not the deeper architecture of collaboration. We introduce Structural Diversity (SD): the extent to which a team bridges multiple distinct knowledge communities within its prior collaboration network. Using a century-scale dataset of 260 million scientific publications (1900-2025) and combining causal inference with a quasi-natural experiment based on a U.S. National Science Foundation policy change in 2012, we show that SD is a powerful and robust predictor of disruptive innovation, outperforming traditional team novelty indicators such as team freshness and edge density. Moreover, SD positively interacts with team size and is able to mitigate the well-known "curse of scale" by transforming scale from a liability into a resource for creative synthesis. We find that one mechanism underlying this effect is Disciplinary Integration (DI): teams with higher SD can more effectively combine heterogeneous knowledge into novel configurations. Our findings position SD as both a new theoretical construct and an actionable design principle for organizing scientific collaboration. By linking the architecture of team assembly to the dynamics of creative discovery, our work offers a structural explanation for how collective intelligence can be systematically engineered to foster disruptive innovation.

cs.SI↗
arXiv2026

A Yang-Mills Type Gauge Theory of Gravity and the Dark Matter Problem

A Yang-Mills type gauge theory of gravity is shown to have a structure richer than that of Einstein's General Theory of Relativity. By elevating the full connections to independent dynamical gauge fields, the theory admits non-trivial vacuum configurations driven by gauge field self-interactions. We demonstrate that this non-linear geometric structure naturally yields an effective spacetime that explains the form of the galactic rotation curves and the anomalous amount of intergalactic gravitational lensing, completely circumventing the need for exotic dark matter particles.

physics.gen-ph↗
arXiv2026

Cosmological Evolution in the $GL(4,\mathbb{R})$ Yang-Mills Theory of Gravity: Resolving the JWST Early Galaxy Crisis and Late-Time Acceleration

We investigate the cosmological implications of the $GL(4,\mathbb{R})$ Yang-Mills gauge theory of gravity. A long-standing theoretical challenge in standard cosmology is the reliance on ad hoc rolling scalar fields (e.g., the inflaton or quintessence) to drive early-time inflation and late-time acceleration, despite their unknown particle physics origins. In this work, by treating the affine connection as a gauge field and the world metric as a non-dynamical background, we derive an exact expansion history that strictly eliminates the need for any rolling scalar fields or a cosmological constant. The dynamics are governed solely by the Yang-Mills field strength squared. We demonstrate that the early Universe exhibits a singularity-free coasting expansion $a(t) \propto t$, resolving the JWST high-redshift galaxy crisis by granting significantly more time for early structure formation. As radiation dilutes, the Universe naturally transitions into a Weitzenböck vacuum state, where a residual, topologically non-trivial constant torsion locks the spacetime kinematics to drive a late-time exponential acceleration $a(t) \propto \exp(ξt)$. This framework establishes a purely geometric and gauge-theoretic origin for the cosmological evolution.

physics.gen-ph↗
arXiv2026

Observation of Floquet erratic non-Hermitian skin effect in photonic mesh lattice

In ordered, translationally invariant non-Hermitian systems, the skin effect is understood as a boundary phenomenon: nonreciprocal hopping drives an extensive accumulation of eigenstates towards the edges, whereas the periodic-boundary spectrum remains Bloch extended. Here we experimentally reveal the opposite limit -- a disorder-enabled, boundary-independent, and intrinsically bulk form of skin localization -- the recently predicted erratic non-Hermitian skin effect (ENHSE), realized in a driven photonic platform. Using a time-multiplexed photonic mesh lattice with programmable gain, loss, and phase modulation, we engineer spatially fluctuating imaginary gauge fields and realize a Floquet non-Hermitian lattice whose global reciprocity can be tuned independently of strong local nonreciprocity. We observe a disorder-driven non-Hermitian topological transition between two oppositely directed disordered skin phases through a critical point of global reciprocity. At this transition, boundary skin accumulation disappears, yet the wave dynamics self-organizes into bulk-localized patterns without any interface, providing direct evidence of ENHSE. The measured localization profiles agree with simulations and exhibit the defining feature that distinct eigenstates share a common bulk-localized envelope determined by the disordered imaginary gauge fields. By further introducing controllable on-site disorder, we reveal the competition between ENHSE and Anderson localization, and show how increasing scattering progressively suppresses erratic skin dynamics. Our results help establish ENHSE as a unique disorder-induced non-Hermitian phenomenon and open a route to engineering localization, transport, and topology beyond conventional Bloch and boundary-based paradigms.

physics.optics↗
arXiv2026

LogiStory: A Logic-Aware Framework for Multi-Image Story Visualization

Generating coherent and communicative visual sequences, such as image sequences and videos, remains a significant challenge for current multimodal systems. Despite advances in visual quality and the integration of world knowledge, existing models still struggle to maintain logical flow, often resulting in disjointed actions, fragmented narratives, and unclear storylines. We attribute these issues to the lack of attention to visual logic, a critical yet underexplored dimension of visual sequence generation that we define as the perceptual and causal coherence among characters, actions, and scenes over time. To bridge this gap, we propose a logic-aware multi-image story visualization framework, LogiStory. The framework is built around the central innovation of explicitly modeling visual logic in story visualization. To realize this idea, we design a multi-agent system that grounds roles, extracts causal chains, and verifies story-level consistency, transforming narrative coherence from an implicit byproduct of image generation into an explicit modeling objective. This design effectively bridges structured story planning with visual generation, enhancing both narrative clarity and visual quality in story visualization. Furthermore, to evaluate the generation capacity, we construct LogicTale, a benchmark comprising richly annotated stories, emphasizing causal reasoning, and visual logic interpretability. We establish comprehensive automatic and human evaluation protocols designed to measure both visual logic and perceptual quality. Experiments demonstrate that our approach significantly improves the narrative logic of generated visual stories. This work provides a foundational step towards modeling and enforcing visual logic in general image sequence and video generation tasks.

cs.CV↗
arXiv2026

Echoes of ownership: Adversarial-guided dual injection for copyright protection in MLLMs

With the rapid deployment of multimodal large language models (MLLMs), disputes regarding model ownership have become increasingly frequent, raising significant concerns about intellectual property protection. In this paper, we propose a framework for generating copyright triggers for MLLMs, enabling model publishers to embed verifiable ownership information into the model. The goal is to construct trigger images that elicit ownership-related textual responses exclusively in fine-tuned derivatives, while remaining inert in other non-derivative models. Our method constructs a tracking trigger image by treating the image as a learnable tensor, performing adversarial optimization with dual-injection of ownership-relevant semantic information. The first injection is achieved by enforcing textual consistency between the output of an auxiliary MLLM and a predefined ownership-relevant target text; the consistency loss is backpropagated to inject this ownership-related information into the image. The second injection is performed at the semantic-level by minimizing the distance between the CLIP features of the image and those of the target text. Furthermore, we introduce an additional adversarial training stage involving the auxiliary model. It is specifically trained to resist generating ownership-relevant target text, thereby enhancing robustness in heavily fine-tuned derivative models. Extensive experiments demonstrate the effectiveness of our dual-injection approach in tracking model lineage under various fine-tuning and domain-shift scenarios. Code is at https://github.com/kunzhan/AGDI

cs.CV↗
arXiv2026

Robust Predictive Modeling Under Unseen Data Distribution Shifts: A Methodological Commentary

Most research designing novel predictive models, or employing existing ones, assumes that training and testing data are independent and identically distributed. In practice, the data encountered at serving time often deviate from the training distribution, leading to substantial performance degradation and potential design validity and/or biased measurement issues. This challenge is further complicated by the fact that the serving time data are frequently unavailable during model development. This method commentary raises awareness of this overlooked issue through a real-world customer churn example and reviews the growing literature on domain generalization, a subfield of transfer learning that explicitly addresses situations in which the target domain is unseen during training. We further argue for adopting an uncertainty-aware predictive modeling mindset and illustrate how this perspective can be operationalized through the distributionally robust optimization framework. Finally, we offer several practical recommendations to enhance the robustness of predictive modeling under unseen data distribution shifts.

cs.LG↗