Searcharxiv⌕ Search

arXiv subjects

Yi Yang

Publications and source records attributed to Yi Yang.

At least 37 records · Page 2Linked to original sources
arXiv2026

RAVEL: Asynchronous Rolling Inference for Flow-Based Vision-Language-Action Models

Flow-based vision-language-action (VLA) models are highly effective for generalist robot manipulation, yet their reliance on computationally expensive VLM encoding and multi-step iterative action generation imposes a significant latency bottleneck. The resulting inference latency makes it difficult for robots to respond quickly, especially in dynamic environments. We address this limitation with RAVEL (Rolling Asynchronous VLA Enabling Low-Latency Control), an asynchronous inference framework that addresses the computational bottlenecks of both the VLM backbone and the action expert. To reduce the delay from multi-step action denoising, RAVEL allows near-term actions to be executed after a single denoising step by carrying partially denoised future actions forward in a rolling buffer. To avoid blocking on slow VLM encoding, RAVEL decouples VLM encoding from rolling action generation, allowing the action expert to operate continuously using the latest available VLM context, while a lightweight Fast Observation Pathway (FOP) directly conditions the action expert on current observations. Across simulated and real-world manipulation tasks, RAVEL consistently achieves substantially lower response latency while maintaining the task capability of the underlying VLA, enabling high-frequency and responsive closed-loop control.

cs.RO↗
arXiv2026

PDE-JEPA: Predictive Representation Learning of Latent Dynamics Modeling for Parametric PDEs

Physical trajectories contain more than snapshots of a system: they also reveal how its states evolve under governing conditions. However, representation learning for parametric partial differential equations (PDEs) has largely relied on reconstruction-based objectives that emphasize recovering observed physical fields. In this paper, we investigate predictive representation pretraining as an alternative to reconstruction-based learning. We find that predictive representations preserve rich physical information, yet this advantage alone does not ensure accurate field evolution. Based on these observations, we introduce PDE-JEPA for parametric PDE dynamics. Specifically, we first train an encoder using a masked-latent prediction to capture the underlying regularities of PDE dynamics. To explicitly adapt the pretrained representation toward a more dynamics-aligned state space, we then introduce a geometry projector that aligns latent trajectory geometry with the evolution geometry of physical fields. Finally, building on this geometry-aligned latent space, we further develop a physics-structured latent predictor that decomposes the dynamics into parameter-independent evolution and parameter-dependent response components. Extensive experiments on nine widely used PDE benchmarks demonstrate that our framework outperforms existing state-of-the-art methods by an average of 33.4\% in-distribution, while achieving an average improvement of 51.4\% when extrapolating to unseen governing parameters. The project page is available \href{https://tanpig-x.github.io/PDE-JEPA/}{here}.

cs.AI↗
arXiv2026

Synthetic SU(3) non-Abelian gauge fields in photonic synthetic frequency dimensions

Synthetic gauge fields provide a powerful route to engineer photonic wave dynamics, especially in synthetic frequency dimensions formed by modulated resonator modes. While most previous realizations have focused on Abelian or lower-rank non-Abelian structures, a photonic implementation of SU(3) gauge fields in the synthetic frequency dimension has remained lacking. Here we propose a route to realize SU(3) gauge structure using three coupled ring resonators with modulation- and interference-engineered mode conversion. We formulate a tight-binding Hamiltonian with matrix-valued hopping generated by the Gell-Mann matrices and show how its evolution can be decomposed into experimentally implementable optical elements. The resulting platform supports SU(3)-structured band dispersions with color-resolved texture and wave-packet dynamics shaped by SU(3) gauge fields. Full-wave-inspired numerical modeling further confirms the expected spectral response and Zitterbewegung-like motion in SU(3) subspaces. Our results introduce SU(3) gauge fields into synthetic frequency photonics and establish a route toward higher-rank non-Abelian control of light in modulated resonator systems.

physics.optics↗
arXiv2026

TTRSD: Test-Time Reinforcement Learning with Self-Distillation for Vision-Language Models

Test-time reinforcement learning enables vision-language models (VLMs) to adapt using unlabeled inputs. However, repeated sampling under fixed visual conditions can reinforce shared perceptual errors, while sequence-level rewards fail to isolate visual perception the foundational bottleneck that anchors multimodal reasoning risking the degradation of pre-trained reasoning capabilities. We propose TTRSD, a test-time reinforcement learning framework combining multi-view answer-level self-distillation with visual contrastive token selection. A shared policy aggregates teacher predictions across original, cropped, and downsampled views into an answer distribution. Student trajectories generated from the original image receive rewards based on the support for their final answers in this distribution. To allocate this feedback precisely toward perceptual bottlenecks, we compare the log-probabilities of the same sampled tokens under original and visually ablated inputs while holding their textual prefixes fixed, selecting visually sensitive positions for policy-gradient updates. TTRSD separates update direction, determined by group-relative advantages, from update position, determined by visual sensitivity, without requiring ground-truth labels, external verifiers, or a separate teacher. With only 20 unlabeled adaptation samples, TTRSD improves performance across seven benchmarks and three VLMs, raising InternVL3-2B's MMMU accuracy from 35.79% to 49.32%(+13.53%), demonstrating cross-dataset generalization while preserving inherent reasoning integrity.

cs.CV↗
arXiv2026

The 91T/99aa-like Type Ia Supernova 2019vrq, Part I: Photometry, Spectroscopy, and Spectropolarimetry of a Nearly Standard Candle

Among the various major subtypes of Type Ia supernovae (SNe), the luminous 1991T-like (Filippenko et al. 1992; Phillips et al. 1992) and 1999aa-like (Krisciunas et al. 2000; Garavini et al. 2004) events stand out as particularly important for constraining the explosion mechanism. Their white dwarf (WD) progenitors are thought to have masses close to or exceeding the Chandrasekhar limit. We present spectrophotometric and polarimetric time-series observations of the overluminous Type Ia SN2019vrq. Between -9 and +12 days relative to the light-curve peak, its continuum polarization is consistent with zero, as seen in most normal-bright SNeIa and despite a possible rise up to 0.2% toward longer wavelengths as seen in subluminous SNeIa. Polarization across major spectral features is marginal ($\lesssim$0.3%) with the exception of the high-velocity CaII near-infrared triplet, which reaches $\sim$0.5%. This low polarization indicates a substantially spherically symmetric electron-scattering photosphere and minimal large-scale ejecta asymmetry. Early-time spectra reveal hot, doubly ionized ejecta dominated by SiIII and blended iron-group features. Intermediate-mass-element lines, such as CaII and SiII, are more clearly seen than in 91T-like events, though these lines remain weaker than in normal SNeIa. The intrinsically higher peak luminosity of overluminous 91T/99aa-like SNeIa (Yang et al. 2022; Phillips et al. 2024), their comparatively short formation timescale for a massive WD progenitor - a channel that becomes more common toward high redshift, and the moderate viewing-angle dependence of their spectrophotometric properties as inferred from their persistently low polarization, together suggest that this subclass may serve as a promising standard candle at high redshift.

astro-ph.SR↗
arXiv2026

The 91T/99aa-like Type Ia Supernova 2019vrq, Part II: 3D, Non-LTE, Low-Amplitude, Pulsating Delayed-Detonation Models of a Promising Standard Candle

We analyze the overluminous, 91T/99aa-like Type~Ia SN 2019vrq, based on light curves(LCs) and spectropolarimetric time-series. We employ 3D-radiation-hydrodynamical, full non-LTE simulations of low-amplitude, radially-pulsating-off-center-delayed-detonations(PDD) of a possibly rotating near-M(Ch) mass white dwarf (WD) to reproduce the LCs and spectra. The progenitor originates from a 7 Mo main-sequence star of solar metallicity. The explosion yields 0.86Mo of 56Ni and 0.023Mo of 58Ni. The latter falls a factor of 10 below that of `classical' delayed-detonations for 91T/99aa-like SNe, a diagnostic that is directly testable with JWST. The slow deflagration leaves a bound, pulsating WD. The detonation is triggered at 0.8 Mo. LCs and spectra require an outer 0.11 Mo of unburned material with twice-solar Fe, plausibly the ashes of an earlier, unsuccessful explosion, and low-level mixing of nuclear-statistical-equilibrium(NSE) elements. The spectra reflect early high ionization followed by recombination, with the photosphere shifting from intermediate-mass-element- to NSE-dominated layers about a week before maximum. The early high-velocity (HV) CaII IR3 line (likely produced by an aspherical density shell at 24,000km/s of 0.01 Mo) arises from an ionization sandwich rather than a double structure in abundances. After Ca recombines, the CaII IR3 wing reaches 33,000km/s well beyond the HV component. The low polarization is due to low scattering in an iron-group-dominated photosphere, consistent with asphericities <20% and resulting in a directional luminosity dependence 10-15% from the outer layers, and a dispersion of 35% in total. The LC-shape provides a further probe of asphericity, consistent with the locally tested polarimetry limits and relevant for high-z cosmology.

astro-ph.SR↗
arXiv2026

Tomography analysis of the intermediate-luminosity Type Iax SN 2024pxl

We present an abundance tomography analysis of SN 2024pxl, an intermediate luminosity Type Iax supernova ($M_{r}=-16.82 \pm 0.19$ mag), with the most-detailed follow-up in the Type Iax subclass to-date. As one of the few intermediate luminosity Type Iax objects, SN 2024pxl may link the two extremes of the peculiar thermonuclear supernova subclass. To test this hypothesis, we analyze its spectral evolution through the first 100 days after the explosion and aim to probe the structure of its ejecta. We conduct an abundance tomography analysis using synthetic spectra produced with the one dimensional radiative transfer code TARDIS. The fit of the spectral time series provides a radial scan of physical properties and probes the stratification of chemical elements throughout most of the SN ejecta. The observed spectral evolution is well fit with the final model, similar to the general predictions of the pure deflagration scenarios, but significant modifications are required in the density function of the inner ejecta and in the chemical profiles of the outermost regions. The constrained physical characteristics, such as the photospheric velocities and the time of maximum light, are also consistent with other SNe~Iax. Despite their small number, intermediate luminosity Type Iax SNe are not outliers in the subclass but demonstrate the continuous nature of SNe Iax through their luminosity range. Following this observation, we argue that all SNe~Iax share the same progenitor and explosion origin.

astro-ph.HE↗
arXiv2026

A Finite-History Interpretation of the AMS-02 Positron Spectrum

I examine whether the separation between the characteristic energy scales of cosmic-ray electrons and positrons can be understood as a finite-history effect, without introducing a separate dominant source specifically to generate the high-energy positron feature. In this description the positron retains the opposite Dirac phase orientation relative to ordinary matter clocks, while local interactions and positive physical energies remain unchanged. Reduced accumulated overlap with the matter-defined Galactic environment is represented by a single, approximately shape-preserving energy rescaling. An illustrative overlap benchmark moves the broad structure of an empirical electron reference near 10 GeV into the few-hundred-GeV region. A number-conserving spatial-dilution example provides an order-of-magnitude interpretation of the relative amplitude. The comparison uses the published AMS-02 electron spectrum directly, with fixed, rounded horizontal and vertical scales rather than optimized spectral parameters. Known populations, including pulsars, may contribute subleading components in this interpretation. The resulting characteristic-scale displacement and geometrical amplitude interpretation provide a possible physical account of the positron spectral hierarchy.

hep-ph↗
arXiv2026

ZYT-World: A Real-Time Controllable World Model for Closed-Loop Autonomous-Driving Simulation

Generative world models offer controllable and repeatable closed-loop simulation for end-to-end and vision-language-action driving policies, but production deployment exposes three unresolved requirements: faithfully reproducing a mixed fisheye-pinhole rig at native resolutions; reconciling causal, per-timestep interaction with long-horizon stability and low latency; and preserving scene identity when a location is revisited. We present ZYT-World, a single architecture that natively generates four fisheye views with field of view > 180° and three pinhole views. Projection-specific Plucker adapters encode camera geometry, ego-motion adaptive layer normalization provides global motion control, and a lightweight pixel-aligned layout conditions traffic participants and signals through instance-level boxes, headings and colors. Heterogeneous training combines full-rig geometric coverage with high-resolution detail. Teacher forcing, causal consistency distillation, self-rollout distribution matching distillation, and RigCritic transform a 40-step bidirectional teacher into a one-step, per-latent streaming generator, with RigCritic evaluating the seven-view rig jointly. A 19M-parameter variational autoencoder decoder (TinyVAE), W8A8 quantization, and our inference engine reduce decoding, backbone, and incremental-execution costs, respectively. Finally, cross-trajectory pairs derived from real captures train a plug-in implicit-memory module that preserves place-specific evidence. On the internal multi-view test set, the one-step model retains more than 90% of the teacher's PSNR and SSIM, while FID, FVD, and LPIPS stay within 11% of the teacher. Under the generator-only timing in Figure 2, it is 107.7 times faster than the 40-step bidirectional teacher. TinyVAE decodes 59.8 times faster than Wan. 30s rollouts and cross-trajectory revisits show the intended long-horizon and memory behavior.

cs.CV↗
arXiv2026

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World

Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bottleneck in the pursuit of general physical intelligence. Although emerging coding agents can generate code to automate algorithm search, their successes remain largely confined in digital environments. We conjecture that the missing abstraction to automate robotics research is a repeatable feedback loop for real-world policy improvement: reset the scene, execute a policy, verify the outcome, and refine the next iteration. To bridge this gap, we introduce ENPIRE, a harness framework for coding agents that instantiates this physical feedback routine with four core modules: an Environment module (EN) for automatic reset and verification, a Policy Improvement module (PI) that launches policy refinement, a Rollout module (R) to evaluate policies with one or multiple physical robots operating in parallel, and an Evolution module (E) in which coding agents analyze logs, consult literature, improve training infrastructure and algorithm code to address failure modes. This closed-loop system transforms real-world manipulation learning into a controllable optimization procedure, minimizing human effort while allowing fair ablations across training recipe and agent variants. Powered by ENPIRE, frontier coding agents can autonomously train a policy to achieve a 99% success rate on challenging, dexterous manipulation tasks, such as organizing a pin box, fastening a zip tie, and tool use, a process that further accelerates when we dispatch an agent team on a robot fleet. Our results suggest a practical and scalable path toward deploying coding agents to autonomously advancing robotics in the physical world.

cs.AI↗
arXiv2026

VDGS: Visibility-Driven Large-Scale 3D Gaussian Splatting for Aerial Scene Reconstruction

Large-scale scene reconstruction is a critical foundational technology in robotic autonomous systems such as 3D mapping and autonomous driving. In recent years, 3D Gaussian Splatting (3DGS) has demonstrated remarkable advantages in both visual quality and computational efficiency, making it a promising representation for large-scale scene reconstruction. However, it still faces challenges in large-scale scenes, including excessive memory consumption and uneven viewpoint coverage caused by UAV acquisition, limiting its real-world applications. To address this, we propose VDGS, a novel 3DGS framework that incorporates camera distribution into scene modeling. VDGS introduces visibility-driven statistics for scene anchors to quantify supervision strength. These statistics are further leveraged for scene partitioning and for gradient compensation in under-optimized regions, thereby promoting balanced optimization across different regions. Extensive experiments on multiple large-scale aerial scene datasets demonstrate that, under imbalanced viewpoint distributions, VDGS consistently outperforms existing methods, while maintaining competitive performance in scenarios with more uniform view distributions.

cs.CV↗
arXiv2026

Bi-FlowGS: Bridging Generative View Completion and Gaussian Geometry through Bidirectional Flow Co-Refinement

Sparse-view 3D scene reconstruction with 3D Gaussian Splatting (3DGS) is inherently underconstrained. Plausible renderings can also coexist with erroneous Gaussian geometry, as errors in positions or depths may be concealed by opacity, scale, and appearance; we term this failure mode Geometry Cheating. Existing regularization methods constrain geometry but remain limited to observed views, while video-diffusion-based methods complete unseen views yet mainly use them as RGB pseudo-supervision, underusing motion and temporal priors and lacking explicit geometry supervision. We present Bi-FlowGS, which uses optical flow to bridge generative view completion and Gaussian geometry regularization. Our plug-and-play Video-to-Geometry Flow Distillation (V2G) distills temporal correspondence priors from restored videos into Gaussian geometry to alleviate Geometry Cheating. Conversely, Geometry-to-Video Flow-Guided Restoration (G2V) uses the current 3DGS geometry to guide temporally consistent video restoration, providing more reliable generative supervision. Together, V2G and G2V form an implicit bidirectional co-refinement process, enabling restored videos and the optimized 3DGS scene to iteratively improve each other. Experiments demonstrate improved rendering quality and geometric consistency across wide-baseline and unbounded 360° benchmarks.

cs.CV↗
arXiv2026

Scattering Equations as the lowest order K-identities in the calculation of Stringy Scaling of Hard String Scattering Amplitudes

We prove explicitly the n-point K-identities we proposed previously in the calculation of one tensor hard string scattering amplitudes (HSSA). These K-identities were the key to obtain the stringy scaling behavior in the saddle point calculation of the n-point HSSA and, on the other hand, to consistently match with the calculation of decoupling of zero norm states. Moreover, we introduce a G function to generate an infinite set of generalized K-identities (GKI). The lowest order set of these GKI is the scattering equations (SE) used in the calculation of field theory amplitudes in the CHY formalism. The next to leading order set of these GKI is the K-identities used previously in the calculation of one tensor HSSA. We expect that the higher order sets of these GKI can find applications in calculating other HSSA.

hep-th↗
arXiv2026

Causal-Horizon Effects on Quarkonium Fragmentation in Jets

We reassess a horizon-inspired description of quarkonium formation in jets, distinguishing momentum redistribution from loss of spin alignment. A normalized energy-sharing prescription, with benchmark inputs for the QCD string tension and quarkonium radii, softens specified $J/ψ$ and $Υ(1S)$ fragmentation baselines without fitting their measured distributions. For the LO implementation displayed by LHCb, the mean momentum fraction changes from 0.629 to 0.476, compared with 0.452 in the prompt data. Applied to CMS-displayed PYTHIA bottomonium spectra, the same prescription produces shifts of the observed order, but neither comparison establishes agreement with the complete shape. An independent NLO bottomonium baseline already overestimates the low-fraction region, and an approximate acceptance-ordered application increases that excess. These results constrain the interpretation of an additional universal softening contribution. Momentum redistribution that preserves each event's spin matrix cannot change the fully integrated polarization. A separate $J/ψ$ formation-reweighting benchmark changes the polar anisotropy from 0.420 to 0.389; a larger reduction requires an additional spin-relaxation assumption. The earlier claim that the measured bottomonium fragmentation distribution establishes a resolution of the inclusive polarization puzzle is superseded. The remaining hypothesis is tested primarily through fragmentation in separately specified experimental phase spaces, with polarization as a conditional consequence.

hep-ph↗
arXiv2026

Bayesian Phase Stabilization at the Shot-Noise Limit for Scalable Quantum Networks

High-precision optical phase stabilization in quantum networks is fundamentally constrained by the strict photon-flux and duty-cycle limits required to avoid disturbing fragile quantum states. This challenge becomes especially critical when coordinating multiple independent light sources for multi-step quantum protocols. Here, we develop an integrated phase-stabilization framework that incorporates a Bayesian phase estimator to optimally extract information from sparse single-photon detection events. This approach outperforms conventional maximum-likelihood estimation and achieves the shot-noise limit under minimal photon flux. The framework enables real-time correction of combined phase noise from both nodal lasers and transmission fibers, facilitating a two-step excitation protocol for heralded entanglement generation between separate trapped-ion nodes via single-photon interference. Operating with a detected photon rate of approximately 1 MHz and a duty cycle less than 6.5%, the system maintains interferometric visibility greater than 97% over fiber links of 10 km and 100 km. This phase control yields long-lived ion-ion entanglement with parity contrast exceeding 85%, enabling device-independent quantum key distribution with finite-size security analysis over 10 km and a positive asymptotic key-rate over 100 km. Moreover, the resulting memory-memory entanglement at 10 km survives beyond the average time required to establish it, which is a fundamental requirement for quantum repeaters. This work establishes a robust and scalable foundation for practical long-distance quantum networks.

quant-ph↗
arXiv2026

Causal-Horizon Scaling of Quarkonium Suppression in Strong QCD Fields

The simultaneous observation of strong sequential bottomonium suppression and small azimuthal anisotropy constrains the time scale and geometry of quarkonium dissociation in relativistic heavy-ion collisions. We investigate an early-time contribution in which strong pre-equilibrium color fields generate an effective proper-acceleration scale and an associated causal length. The maximal inverse causal-length scale is fixed through the phenomenological anchoring condition $T_{U,\mathrm{eff}}^{\max}\simeq T_c$, while the local kinematic acceleration notation $T_{U,\mathrm{loc}}$ is kept distinct from an equilibrium temperature. The survival probability is described by an event-averaged one-scale exponential ansatz. With no state-by-state adjustment, the resulting horizon component captures the main LHC state ordering and centrality trend. For the directly measured $Υ(2S)$ to-$Υ(1S)$ double ratio, the anchored horizon coefficient and one effective late-stage coefficient give a quantitative conditional description. Because both contributions are assigned the same centrality function, the data constrain their combined exponent rather than a unique partition between early- and late-stage suppression. At RHIC, the absolute $R_{AA}$ central values lie below the isolated horizon component, while the relative $2S$-to-$1S$ suppression remains compatible with the predicted state-size hierarchy within present uncertainties. Because the proposed early factor is local and scalar, its contribution is identically $v_2^H=0$ within the idealized factorized construction, consistent with current CMS measurements.

hep-ph↗
arXiv2026

Mapping the Emerging Social Science of Large Language Models

Large language models (LLMs) increasingly shape communication, learning, work, creativity, and decision-making, yet social-science research on these developments remains fragmented. We map this emerging field using a curated corpus of 198 papers reviewed in full and a field-scale corpus of 47,719 published papers from five bibliographic databases. Combining sentence embeddings, K-means clustering, within-cluster Latent Dirichlet Allocation (LDA), author and LLM classifications, and structural topic modeling, we identify three domains: LLM as Social Minds, examining socially interpretable model behavior; LLM Societies, examining collective dynamics among interacting model-based agents; and LLM-Human Interactions, examining how people perceive, use, and are affected by LLMs. These domains contain 13 subcategories spanning reasoning, personality and bias, behavioral games, collective intelligence, simulation, trust, work, creativity, and education. In the curated corpus, the three-domain solution is highly stable under resampling (adjusted Rand index = 0.952), and K-means assignments agree with author full-text classifications for 77.78% of papers. At field scale, 13 of 15 topics map onto the taxonomy, while K-means and structural-topic-model domains agree for 73.83% of overlapping papers. LLM-Human Interactions accounts for 78.02% of domain-mapped topic mass, but venue analysis reveals a contrasting pattern: Social Minds and LLM Societies together account for 66.37% of highly cited papers in leading conference venues, whereas LLM-Human Interactions accounts for 76.81% in the corresponding journal subset. The resulting taxonomy provides a reproducible framework for understanding how model behavior, agent interaction, and institutional context jointly shape the social consequences of LLMs.

cs.CY↗
arXiv2026

TransNormal-2: Geometry-Grounded Rectified Flow with Edge-Aware Decoding for Precise Normal Estimation

Diffusion-based models enable monocular geometry estimation, yet their pixel-space precision is limited by a shared, under-studied error source: VAE reconstruction degradation. The 8x spatial compression in the VAE encoder-decoder degrades surface normals at object boundaries; even encoding and decoding ground-truth normals introduces 1.3--8.5° of mean angular error (MAE), with edge MAE reaching 2.8x the global MAE. We present TransNormal-2, a FLUX.2-based rectified-flow framework with single-step deterministic inference that addresses this degradation on both sides of the VAE decoder: in how latent predictions are supervised during training, and in how decoded normals are corrected at inference. First, geometry-aware pixel-space losses, including inverse rendering self-consistency, von~Mises-Fisher angular loss, and wavelet edge-aware regularization, complement latent MSE by enforcing spherical normal geometry and diffuse image-formation cues after VAE decoding. Second, a lightweight Geometric Refinement Module (GRM) applies an RGB-guided residual correction to reduce boundary-localized decoding errors without freely rewriting the coarse prediction. On general-scene benchmarks, TransNormal-2 matches or exceeds MoGe-2 on all eight reported metrics while using only 1.4% as many task-specific normal annotations. The gains are clearest for transparent objects, reducing MAE by 4.2° on ClearGrasp and 3.1° on ClearPose over the strongest prior baselines. Code will be released at https://longxiang-ai.github.io/TransNormal-2.

cs.CV↗