arXiv subjects

Rui Peng

Publications and source records attributed to Rui Peng.

At least 19 recordsLinked to original sources

arXiv2026

World-in-Context: Interactive Video World Modeling with In-Context Conditioning

Interactive video world models require coordinated control over viewing perspectives, character actions, and camera motion. Existing methods typically leave view modes implicit in text captions, conflate action with camera motion, and inject controls through fragmented architectural pathways that capture interdependencies only indirectly. In this work, we present World-in-Context, an interactive world modeling framework powered by In-Context Conditioning (ICC). Rather than relying on specialized conditioning pathways, World-in-Context unifies view, action, and camera controls into explicit tokens contextualized directly within a shared self-attention space. Our formulation reveals that (i) explicit view tokens disentangle conflicting dynamics and unlock emergent view switching from single-view data, while (ii) action and camera inputs play complementary roles in governing local responsiveness and global stability. To preserve temporal continuity across execution intervals, we further introduce Interaction-Transport RoPE (IT-RoPE), parameterizing temporal attention with accumulated control transitions. On WBench, World-in-Context-5B achieves navigation performance on par with 22B EchoWM ($81.5$ vs. $81.6$) and attains a $93.8\%$ perspective-switching success rate, substantially outperforming existing 5B baselines ($18.8\%$). Controlled analyses further demonstrate compounding advantages of World-in-Context under richer conditioning mixtures, favorable data efficiency, and natural applicability to causal rollouts.

cs.CV↗

arXiv2026

The Past Frames the Future: Memory for Autoregressive Video Generation

Advances in generative models have improved video fidelity, enabling long-horizon generation, interactive world modeling, and evolving visual environments. Autoregressive (AR) video generation extends visual sequences through causal rollouts. However, a fundamental bottleneck emerges: as the generated sequence expands, practical models must operate under strictly bounded context windows, storage, and computational limits. Consequently, critical historical information, e.g., entity identities, dynamic states, and intervention-induced causal changes, often leaves the active context long before its relevance diminishes. Overcoming this limitation and maintaining temporal persistence constitutes a fundamental memory problem. We present a systematic and comprehensive review of memory mechanisms in AR video generation. We formulate memory operationally as persistent historical information maintained across outer AR steps, capable of influencing future generation even after the originating evidence is no longer locally accessible. Building upon this unified framework, we organize the literature through five complementary perspectives: (I) Forms, the representational carriers of history; (II) Functions, the specific semantic and physical information requiring preservation; (III) Operations, the lifecycle of writing, reading, updating, managing, and integrating memory; (IV) Learning, the optimization of memory behaviors under closed-loop rollouts; and (V) Evaluation, the paradigms for diagnosing genuine memory capabilities. We conclude by synthesizing open challenges, including composable and resource-aware memory architectures, trustworthy state updating, self-rollout learning, and standardized evaluation. By bridging representations, mechanisms, and learning paradigms, this paper establishes a structured foundation for developing reliable, memory-conditioned video generation systems.

cs.CV↗

arXiv2026

SkelWAM: A Skeleton-Guided World-Action Model for Zero-Shot Cross-Embodiment Manipulation

Reusing manipulation experience across robot embodiments is important for scaling robot learning and reducing repeated task-specific data collection. However, changes in embodiment alter visual appearance, action dimensionality and semantics, and the whole-body configurations that can realize the same tool pose. We present SkelWAM, a skeleton-guided world-action model that couples perception and control through one explicit geometric representation for single-source cross-embodiment manipulation. Arm centerline geometry, tool-center-point (TCP) pose, and parallel-jaw commands form a shared 25-D state. The same definition underlies canonical third-person and wrist observations and future whole-body action targets. Trained with predictive visual supervision, a video-action mixture of transformers predicts canonical skeleton action chunks, which embodiment-specific constrained decoders convert into joint or continuum-robot controls. This formulation requires no one-to-one joint correspondence and uses no target-task demonstrations or target policy updates. We introduce LIBERO-Cross10, a source-only cross-embodiment transfer benchmark covering ten tasks and ten target embodiments across four morphological groups. On this benchmark, Franka-trained SkelWAM achieves 43.3% success over 1,000 episodes, exceeding the best-performing evaluated baseline by 36.2 percentage points. We further deploy a JAKA mini2-trained policy on the Feagine A03 continuum robot for three tabletop manipulation tasks, illustrating the approach's potential for real-world cross-embodiment manipulation. Project page: http://www.liukepku.com/skelwam/index.html

cs.RO↗

arXiv2026

A Disconnected Superconducting Regime at the Parent Limit of Infinite-Layer Nickelates

Infinite-layer nickelates have been widely viewed as cuprate analogs in which superconductivity emerges and forms a superconducting dome centered around 10-20% cation substitution. Here we show that pristine and stoichiometric PrNiO2, without cation substitution, exhibits intrinsic superconductivity characterized by zero resistance and diamagnetism in uncapped films. Through heterostructure engineering, we further exclude an interfacial origin of the superconductivity. Remarkably, zero-resistance superconductivity is consistently observed in trivalent-substituted PrNiO2, whereas it is rapidly suppressed by dilute divalent substitution. Combined with angle-resolved photoemission studies, these results indicate that such a new superconducting regime is confined to within 3% additional hole doping from pristine PrNiO2. Furthermore, this phase is separated from the previously established superconducting dome around ~ 20% divalent doping by a non-superconducting region in the phase diagram, and is further distinguished by a remarkably stronger upper-critical-field anisotropy. These findings establish a unique separated superconducting regime, suggesting that infinite-layer nickelates are not merely cuprate analogs but host distinct superconducting physics.

cond-mat.supr-con↗

arXiv2026

Marginal-Fermi-Liquid-like Behavior without Pseudogap in Infinite-Layer Nickelates

Pseudogap formation, strange-metal behavior and unconventional superconductivity are closely intertwined in hole-doped cuprates, yet their relationship remains unresolved. Infinite-layer nickelates offer a distinct 3d9-derived platform to address this question by combining a cuprate-like Ni dx2-y2 Fermi surface with multiband electronic degrees of freedom. Here we use angle-resolved photoemission spectroscopy to resolve the low-energy spectral function of superconducting La0.8Ca0.2NiO2 and parent LaNiO2 thin films. In La0.8Ca0.2NiO2, the electronic self-energy Im Sigma(omega) is approximately linear in energy and its slope increases from (pi/2, pi/2) to (pi, 0), revealing momentum-dependent marginal-Fermi-liquid-like scattering. Both films show a progressive suppression of low-energy spectral weight from the diagonal direction toward (pi, 0), with stronger suppression in parent LaNiO2. However, finite Fermi-level spectral weight persists around the entire Fermi surface, with no leading-edge shift or back-bending indicative of pseudogap formation in either the electron pocket or the cuprate-like hole band. Our results demonstrate that momentum-selective correlations and marginal-Fermi-liquid-like scattering can occur without a detectable cuprate-like pseudogap, providing a benchmark for identifying the essential normal-state electronic ingredients of high-temperature superconductivity.

cond-mat.supr-con↗

arXiv2026

Towards Consistent Video Geometry Estimation

This work presents ViGeo, a feed-forward foundation model for recovering spatially dense and temporally consistent geometry from video sequences. Built upon a plain transformer architecture without task-specific architectural modifications, ViGeo supports streaming, full-sequence, and long-video inference within a unified model. The key design is dynamic chunking attention, which exposes the model to both bidirectional and causal temporal contexts during training and allows it to adapt its attention pattern at test time without retraining. To improve supervision quality, we further introduce a completion-based data refinement framework. This framework trains a video depth completion teacher that conditions on sparse and noisy annotations and exploits video/multi-view context to produce dense, temporally coherent, and geometrically reliable training targets. Beyond depth and point maps, ViGeo also predicts surface normals within the same framework. Trained solely on public datasets, ViGeo achieves state-of-the-art performance across online, offline, and long-video depth estimation, surface normal estimation, and video point map estimation.

cs.CV↗

arXiv2026

Glob3R: Global Structure-from-Motion with 3D Foundation Models

Recent 3D geometric foundation models, such as VGGT, provide robust feed-forward 3D reconstruction by directly predicting camera poses and 3D scene points from input images. However, their results remain inaccurate, and scaling them to long sequences or large unordered image sets typically requires chunk-wise processing, which can introduce drift and inconsistency. We present Glob3R, a global SfM-style reconstruction built on 3D foundation models. Our key idea is to explicitly optimize feed-forward geometric predictions. To this end, we augment a frozen Pi3X backbone with a lightweight dense matching head that predicts image warps between selected reference frames and neighboring views. These dense warps are converted into sparse but reliable multi-view feature tracks, which provide correspondence constraints for global optimization. We further introduce a keyframe-based sliding-window association strategy that propagates tracks and relative poses across overlapping windows, enabling scalable reconstruction. Finally, we perform global motion averaging and bundle adjustment to refine camera poses, reduce scale inconsistencies, and recover dense scene geometry. Extensive experiments on indoor, outdoor, large-scale driving, and unordered SfM benchmarks demonstrate that Glob3R achieves robust and accurate reconstruction. It consistently improves over feed-forward foundation-model baselines and recent scalable reconstruction methods, while being more robust than classical SfM pipelines. The refined poses also lead to higher-quality neural rendering, validating the benefit of combining foundation-model priors with global geometric optimization. Project page: https://junyuandeng.github.io/Glob3r

cs.CV↗

arXiv2026

HR-VILAGE-3K3M: A Human Respiratory Viral Immunization Longitudinal Gene Expression Dataset for Systems Immunity

Respiratory viral infections pose a global health burden, yet the cellular immune mechanisms underlying protection and pathology remain unclear. Natural infection cohorts often lack pre-exposure baselines and time-controlled sampling, whereas inoculation and vaccination trials generate well-structured longitudinal transcriptomic data. However, these datasets are scattered across repositories and processed inconsistently, hindering integrative and AI-driven analyses. To address these challenges, we developed the Human Respiratory Viral Immunization LongitudinAl Gene Expression (HR-VILAGE-3K3M) repository: an AI-ready resource integrating bulk and single-cell transcriptomic profiles from 3,178 subjects across 66 studies. The dataset spans vaccination, inoculation, and mixed exposures, with samples from blood and nasal swabs collected from public repositories including GEO, ImmPort, and ArrayExpress. We curated and harmonized subject-level metadata, standardized outcome measures, and applied unified preprocessing with rigorous quality control. We further provide benchmark analyses illustrating its utility. This resource supports discovery of biomarkers, immune mechanisms, and methodological development. As one of the largest longitudinal transcriptomic resources for human respiratory viral immunization, HR-VILAGE-3K3M enables reproducible and scalable analyses to accelerate vaccine and antiviral research.

q-bio.GN↗

arXiv2026

Pure Spin Photocurrent in Altermagnetic Photovoltaic Battery

Altermagnets, featuring momentum-dependent spin splitting without net magnetization, provide a promising platform for spintronic functionalities beyond conventional ferromagnets and antiferromagnets. Here, we propose an altermagnetic spin photovoltaic battery consisting of a nonmagnetic semiconducting layer sandwiched between two altermagnetic electrodes. Using first-principles quantum-transport simulations, we show that a V2Te2O/ZnSe/V2Te2O junction supports a pure spin photocurrent for opposite Néel vectors in the two altermagnetic electrodes, with spin-up and spin-down photocurrents equal in magnitude and opposite in sign. The effect persists under both linearly and circularly polarized light and remains tunable with photon energy and polarization angle. Our results establish a realistic route toward light-driven pure spin-current generation in altermagnetic junctions.

cond-mat.mes-hall↗

arXiv2026

Electronic Origin of Ferromagnetic Excitations in the Candidate Spin-Triplet Superconductor CeSb2

The origin of quasi-one-dimensional (q1D) ferromagnetic (FM) excitations in the candidate spin-triplet superconductor CeSb$_2$ has remained unclear. Here we report an electronic mechanism for emergent q1D magnetism in the quasi-two-dimensional lattice of CeSb$_2$, revealed by angle-resolved photoemission spectroscopy (ARPES). High-resolution ARPES resolves no spin-density-wave gap on the dispersive Fermi pockets, disfavoring a nesting-driven mechanism for the q1D FM excitations. Instead, resonant ARPES reveals a pronounced selective enhancement of Ce 4$f$ spectral weight on the $C_2$-distributed Fermi pockets aligned with the Ce ladder. This observation signifies band-selective Kondo coupling that generates strongly anisotropic magnetic exchange interactions, which can naturally account for both the q1D ferromagnetic excitations and the competing magnetic orders. Our results identify a band-selective Kondo coupling mechanism for emergent low-dimensional magnetism in correlated $f$-electron systems.

cond-mat.str-el↗

arXiv2026

Role of interstitial $s$ orbital in a model of infinite-layer nickelates

Motivated by recent angle-resolved photoemission spectroscopy (ARPES) experiments on infinite-layer (IL) nickelates, we employ determinant quantum Monte Carlo (DQMC) to study the three-orbital Emery model ($d$-$p$ model) coupled to an additional interstitial $s$ orbital retaining the three-dimensional dispersion. Our large-scale simulations reveal that: (1) the interstitial $s$-orbital-derived electron pocket is significantly reduced by the strong interaction but persists upon 20\% hole doping, reaching a size comparable to experimental observations; (2) the $d_{x^2-y^2}$-orbital dispersion is strongly renormalized by interactions, leading to a weak $k_z$ dependence consistent with ARPES measurements. Furthermore, compared with the conventional three-orbital $d$-$p$ model, the $d$-$p$-$s$ model exhibits enhanced short-range antiferromagnetic correlations. These results highlight the crucial role of strong correlations and multi-orbital effects in shaping the low-energy electronic structure and many-body correlations in IL nickelates, and demonstrate the necessity of treating interaction-driven many-body physics within a realistic multi-orbital framework.

cond-mat.str-el↗

arXiv2026

LHM++: An Efficient Large Human Reconstruction Model for Pose-free Images to 3D

Reconstructing animatable 3D humans from casually captured images of articulated subjects without camera or pose information is highly practical but remains challenging due to view misalignment, occlusions, and the absence of structural priors. In this work, we present LHM++, an efficient large-scale human reconstruction model that generates high-quality, animatable 3D avatars within seconds from one or multiple pose-free images. At its core is an Encoder-Decoder Point-Image Transformer architecture that progressively encodes and decodes 3D geometric point features to improve efficiency, while fusing hierarchical 3D point features with image features through multimodal attention. The fused features are decoded into 3D Gaussian splats to recover detailed geometry and appearance. To further enhance visual fidelity, we introduce a lightweight 3D-aware neural animation renderer that refines the rendering quality of reconstructed avatars in real time. Extensive experiments show that our method produces high-fidelity, animatable 3D humans without requiring camera or pose annotations. Our code and project page are available at https://lingtengqiu.github.io/LHM++/

cs.CV↗

arXiv2026

Sliding Ferroelectricity Driven Spin-Layertronics in Altermagnetic Multilayers

The synergy of ferroicity with altermagnetism offers a novel platform for designing multifunctional altermagnetic-spintronic device technology. In this work, we propose a mechanism to achieve nonvolatile electrical manipulation of spin and layer degrees of freedom in an altermagnetic bilayer via sliding ferroelectricity. Using first-principles calculations, we show that an interlayer translation can induce a switchable out-of-plane ferroelectric polarization in bilayer CuF2, which directly couples to and reverses the d-wave altermagnetic spin splitting. Notably, the altermangetic spin splitting is layer-locked, the sliding ferroelectricity-driven switching thus embodying a nonvolatile spin-layertronics functionality that couples spin-polarized transport and layer degree of freedom in a single platform. We show that in quadrilayer CuF2, four polarization states are identified which may offer multi-state logic device applications. These findings establish sliding ferroelectricity as a versatile tool for designing voltage-controlled, high-speed and energy-efficient spin-layertronic devices based on altermagnets.

cond-mat.mtrl-sci↗

arXiv2026

Intrinsic Geometry-Appearance Consistency Optimization for Sparse-View Gaussian Splatting

3D human reconstruction from a single image is a challenging problem and has been exclusively studied in the literature. Recently, some methods have resorted to diffusion models for guidance, optimizing a 3D representation via Score Distillation Sampling(SDS) or generating a back-view image for facilitating reconstruction. However, these methods tend to produce unsatisfactory artifacts (\textit{e.g.} flattened human structure or over-smoothing results caused by inconsistent priors from multiple views) and struggle with real-world generalization in the wild. In this work, we present \emph{MVD-HuGaS}, enabling free-view 3D human rendering from a single image via a multi-view human diffusion model. We first generate multi-view images from the single reference image with an enhanced multi-view diffusion model, which is well fine-tuned on high-quality 3D human datasets to incorporate 3D geometry priors and human structure priors. To infer accurate camera poses from the sparse generated multi-view images for reconstruction, an alignment module is introduced to facilitate joint optimization of 3D Gaussians and camera poses. Furthermore, we propose a depth-based Facial Distortion Mitigation module to refine the generated facial regions, thereby improving the overall fidelity of the reconstruction. Finally, leveraging the refined multi-view images, along with their accurate camera poses, MVD-HuGaS optimizes the 3D Gaussians of the target human for high-fidelity free-view renderings. Extensive experiments on Thuman2.0 and 2K2K datasets show that the proposed MVD-HuGaS achieves state-of-the-art performance on single-view 3D human rendering.

cs.CV↗

arXiv2026

KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment

Maintaining comprehensive and up-to-date knowledge graphs (KGs) is critical for modern AI systems, but manual curation struggles to scale with the rapid growth of scientific literature. This paper presents KARMA, a novel framework employing multi-agent large language models (LLMs) to automate KG enrichment through structured analysis of unstructured text. Our approach employs nine collaborative agents, spanning entity discovery, relation extraction, schema alignment, and conflict resolution that iteratively parse documents, verify extracted knowledge, and integrate it into existing graph structures while adhering to domain-specific schema. Experiments on 1,200 PubMed articles from three different domains demonstrate the effectiveness of KARMA in knowledge graph enrichment, with the identification of up to 38,230 new entities while achieving 83.1\% LLM-verified correctness and reducing conflict edges by 18.6\% through multi-layer assessments.

cs.CL↗

arXiv2025

Perturbative renormalization of chiral nuclear forces at subleading order in 3S1-3D1 channel

We investigate renormalization of chiral nuclear forces in the coupled channel of 3S1-3D1 of nucleon-nucleon scattering. The one-pion exchange potential is treated nonperturbatively at leading order while subleading potentials are perturbations. Very much like the uncoupled channel of 3P0 , the singular attraction of one-pion exchange gives rise to the so-called genuine exceptional cutoffs, where artificial correlations between subleading contact operators emerge and they result in ill-defined values of the low-energy constants. To address this issue we follow the solution proposed for 3P0 in Ref. [1] and apply it to 3S1-3D1 . The truncation uncertainty of an effective field theory allows certain degrees of freedom in choosing renormalization conditions, or fitting schemes of the low-energy constants. By exploiting this freedom near the exceptional cutoffs, we are able to remove the said correlations. A much mitigated cutoff variation of the phase shifts, which is acceptable to the power counting, is thus obtained.

nucl-th↗

arXiv2025

The principal eigenvalue of an age-structured operator with diffusion and advection: qualitative analysis and an application

In this paper, we investigate an eigenvalue problem associated with an age-structured operator incorporating random diffusion and advection. Our primary focus is on examining the asymptotic behaviors of the principal eigenvalue with respect to large advection and small or large diffusion rates. We subsequently apply these results to a nonlinear age-structured model, providing a better understanding of how diffusion and advection influence the spatial distribution of species. Among other ingredients, our approach involves constructing various types of super- and sub-solutions to tackle the novel challenges posed by the nonlocal terms in the problems under consideration.

math.AP↗

arXiv2025

KINDLE: Knowledge-Guided Distillation for Prior-Free Gene Regulatory Network Inference

Gene regulatory network (GRN) inference serves as a cornerstone for deciphering cellular decision-making processes. Early approaches rely exclusively on gene expression data, thus their predictive power remain fundamentally constrained by the vast combinatorial space of potential gene-gene interactions. Subsequent methods integrate prior knowledge to mitigate this challenge by restricting the solution space to biologically plausible interactions. However, we argue that the effectiveness of these approaches is contingent upon the precision of prior information and the reduction in the search space will circumscribe the models' potential for novel biological discoveries. To address these limitations, we introduce KINDLE, a three-stage framework that decouples GRN inference from prior knowledge dependencies. KINDLE trains a teacher model that integrates prior knowledge with temporal gene expression dynamics and subsequently distills this encoded knowledge to a student model, enabling accurate GRN inference solely from expression data without access to any prior. KINDLE achieves state-of-the-art performance across four benchmark datasets. Notably, it successfully identifies key transcription factors governing mouse embryonic development and precisely characterizes their functional roles. In mouse hematopoietic stem cell data, KINDLE accurately predicts fate transition outcomes following knockout of two critical regulators (Gata1 and Spi1). These biological validations demonstrate our framework's dual capability in maintaining topological inference precision while preserving discovery potential for novel biological mechanisms.

q-bio.MN↗