arXiv subjects

Chang Liu

Publications and source records attributed to Chang Liu.

At least 163 records · Page 9Linked to original sources

arXiv2026

Distributed Online Bandit Submodular Maximization with Bounded Sampling Violations

We study distributed online submodular maximization under partition matroid constraints, in which multiple agents select a limited number of actions from their own subsets sequentially to maximize the cumulative value of a sequence of objective functions. We develop a unified algorithmic framework that accommodates full-information and bandit feedback models. For both feedback models, we prove that the proposed algorithms achieve sublinear $(1-1/e)$-regret guarantees, which are comparable to those achieved by existing centralized counterparts. Furthermore, to tackle the sampling violation issue caused by continuous relaxation and rounding, we develop a bounded stochastic pipage rounding scheme and show that the probability of sampling violation vanishes asymptotically. As a result, the cumulative sampling violation remains sublinear in $T$, which is further shown to be not improvable under certain conditions. Numerical results validate the theoretical findings in this paper.

cs.LG↗

arXiv2026

GaussianFusion: Unified 3D Gaussian Representation for Multi-Modal Fusion Perception

The bird's-eye view (BEV) representation enables multi-sensor features to be fused within a unified space, serving as the primary approach for achieving comprehensive 3D perception. However, the discrete grid representation of BEV leads to significant detail loss and limits feature alignment and cross-modal information interaction in multimodal fusion perception. In this work, we break from the conventional BEV paradigm and propose a new universal framework for multi-modal fusion based on 3D Gaussian representation. This approach naturally unifies multi-modal features within a shared and continuous 3D Gaussian space, effectively preserving edge and fine texture details. To achieve this, we design a novel forward-projection-based multi-modal Gaussian initialization module and a shared cross-modal Gaussian encoder that iteratively updates Gaussian properties based on an attention mechanism. GaussianFusion is inherently a task-agnostic model, with its unified Gaussian representation naturally supporting various 3D perception tasks. Extensive experiments demonstrate the generality and robustness of GaussianFusion. On the nuScenes dataset, it outperforms the 3D object detection baseline BEVFusion by 2.6 NDS. Its variant surpasses GaussFormer on 3D semantic occupancy with 1.55 mIoU improvement while using only 30% of the Gaussians and achieving a 450% speedup.

cs.CV↗

arXiv2026

Decoding the Early-Time Light Curves of Type Ia Supernovae. I. A Hierarchical Bayesian Framework for Demographic Inference

Light curves of Type Ia Supernovae (SNe Ia) in the days following explosion encode the diversity of progenitor systems and explosion physics. We present a hierarchical Bayesian framework to robustly constrain the population-level light-curve morphology of SNe Ia by fitting a large light-curve dataset simultaneously to power-law rises. Using a multivariate Gaussian population prior, this framework automatically down-weights sparsely sampled SNe and noisy measurements in the inference, obviating the need for restrictive quality cuts that introduce selection biases. Validation on simulated power-law light curves demonstrates that the population prior effectively suppresses the volume-projection bias from the asymmetric likelihood: compared to the classic two-step approach of fitting individual SNe and then aggregating the results, the hierarchical approach dramatically reduces the bias on the population-level parameters (mean, scatter, and correlation). When fitting the power-law model to light curves with more realistic morphologies, while the rise time can be mildly underestimated due to model misspecification, the recovered population scatter remains reliable. Furthermore, SNe with early flux excesses can emerge as outliers in the inferred parameter space, offering a potential diagnostic for identifying such events. Finally, we show that the inferred population distribution can also improve individual-event inference. Restricting the population prior to nuisance amplitudes, while preserving the complete correlation structure, regularizes fits to individual SNe without shrinking the physically meaningful rise time and rise index toward their population means.

astro-ph.HE↗

arXiv2026

Decoding the Early-Time Light Curves of Type Ia Supernovae. II. Population Parameters of One Thousand ZTF Supernovae

Early-time light curves of Type Ia Supernovae (SNe Ia) encode critical information about their progenitor systems. We characterize the rise of normal SNe Ia using a volume-complete sample of 972 events from the Zwicky Transient Facility Data Release 2, an order of magnitude larger than any previous dataset for similar analyses. Fitting light curves up to $30\%$ of peak flux with a power-law model under a hierarchical Bayesian framework, we provide robust population-level constraints on the rise time ($t_\mathrm{rise}$; $μ=18.55\pm0.08$ days, $σ=1.42\pm0.07$ days), rise index ($α$; $μ=2.10\pm0.04$, $σ=0.48\pm0.03$ in ZTF $r$), and $g-r$ color evolution ($α_g - α_r$; $μ=0.20\pm0.02$, $σ=0.17\pm0.02$). These power-law fits are sensitive to the chosen truncation epoch if data beyond $\sim$$40\%$ of peak flux are included, but generally converge when restricted to earlier epochs. The relation between rise morphology and light-curve width ($\texttt{SALT2}$ $x_1$ stretch) bifurcates into two distinct regimes: high-stretch SNe Ia show clear trends where a higher $x_1$ correlates with shallower rises and more persistent blue colors, whereas low-stretch SNe Ia lack such trends. While rise times correlate positively with $x_1$ overall, this relation flattens significantly within the high-stretch population. Searching for anomalies, we identify several normal SNe Ia with unusually long rise times, which potentially exhibit short-duration ($\lesssim$2 days) flux excesses over a smooth rise. Long-duration ($\sim$5 days) flux excesses appear common within the high-stretch population and are tied to the shallow rises and early blue colors, pointing to widespread outward $^{56}$Ni mixing. Multi-dimensional explosion models with more realistic progenitor setups are needed to fully reproduce the observed dichotomy in rise morphology and stretch.

astro-ph.HE↗

arXiv2026

JWST Observations of Calcium-Strong Transients: I. Complex Nebular He Emission in SN 2024uj

We present the first JWST observations of a Calcium-Strong Transient (CaST), SN 2024uj, a rare class of supernovae (SNe) with observable properties that are consistent with both thermonuclear explosions of white dwarfs (WDs) and the core collapse of massive stars. SN 2024uj is offset by $\sim6.6$ kpc from its host and exhibits a double-peaked light curve consistent with shock cooling of nearby circumstellar material. At early times, its optical spectra resemble those of normal SNe Ib, but strong [Ca II] $λλ$7291, 7324 emission emerges between $+$2 and $+$17 days after maximum light. Radiative-transfer models of a massive stripped He star cannot reproduce this early forbidden Ca emission, even with artificially enhanced surface Ca, whereas it arises naturally in thermonuclear scenarios. The $+$150 d JWST/NIRSpec spectrum reveals highly asymmetric, multicomponent He I at both 1.083 and 2.058 $μ$m. The He extends to $\gtrsim+$5000 km/s, with a strong, narrow peak at $+$1500 km/s, indicating that He is distributed throughout the ejecta with a concentration offset from center. This He distribution overlaps central [Ca II] and [O I], implying a degree of mixing difficult to produce in a massive star explosion. The He peak might further trace interaction with a shocked, ejected companion in a thermonuclear system. The NIRSpec spectrum also shows molecular CO emission and a rising continuum that, together with a 10 $μ$m photometric detection, indicates dust emission extending into the mid-infrared. Given the remote environment, early forbidden Ca, mixed He/Ca/O ejecta, and possible companion signature, we favor a thermonuclear origin for SN 2024uj involving at least one low-mass, partially He-rich WD.

astro-ph.HE↗

arXiv2026

Intuition-Guided Latent Reasoning for LLM-Based Recommendation

Large Language Models (LLMs) have demonstrated impressive reasoning capabilities in complex problem-solving tasks, motivating their use for preference reasoning in recommender systems. Latent reasoning, which operates in continuous hidden spaces rather than discrete tokens, has recently emerged as a promising paradigm for LLM-based recommendation. However, existing methods often start from unconstrained reasoning points, where hidden representations are misaligned with target item embeddings, leading to suboptimal reasoning trajectories. Inspired by cognitive neuroscience, which suggests that human multi-step reasoning is guided by intuition as a latent prior, we propose \emph{IntuRec}, a two-stage framework that anchors latent reasoning with \emph{recommendation intuition}. In the extraction stage, the LLM-based recommender generates a top-$K$ candidate set based on users' histories as the source of intuition. In the injection stage, the candidate set is transformed into a preference-aligned intuition embedding using self- and cross-attention mechanisms, which initializes the reasoning start point and guides subsequent latent reasoning. By providing a semantically grounded starting point, IntuRec efficiently explores the preference space along more accurate reasoning trajectories. Extensive experiments on multiple real-world datasets demonstrate that IntuRec consistently outperforms state-of-the-art baselines. We release our code at https://github.com/Ten-Mao/IntuRec.

cs.IR↗

arXiv2026

Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network

In this work, we propose a flow-matching Markov chain Monte Carlo (FM-MCMC) algorithm for estimating the orbital parameters of exoplanetary systems, especially for those only one exoplanet is involved. Compared to traditional methods that rely on random sampling within the Bayesian framework, our approach first leverages flow matching posterior estimation (FMPE) to efficiently constrain the prior range of physical parameters, and then employs MCMC to accurately infer the posterior distribution. For example, in the orbital parameter inference of beta Pictoris b, our model achieved a substantial speed-up while maintaining comparable accuracy-running 77.8 times faster than Parallel Tempered MCMC (PTMCMC) and 365.4 times faster than nested sampling. Moreover, our FM-MCMC method also attained the highest average log-likelihood among all approaches, demonstrating its superior sampling efficiency and accuracy. This highlights the scalability and efficiency of our approach, making it well-suited for processing the massive datasets expected from future exoplanet surveys. Beyond astrophysics, our methodology establishes a versatile paradigm for synergizing deep generative models with traditional sampling, which can be adopted to tackle complex inference problems in other fields, such as cosmology, biomedical imaging, and particle physics.

astro-ph.EP↗

arXiv2026

Wave-Particle Decomposition for Kinetic Equations I: Theory and Numerics

This paper presents a wave-particle decomposition (WPD) for kinetic relaxation equations, formulated around a local evolution timescale and its associated kinetic horizon. By leveraging the characteristic integral solution, we decompose the distribution function into an analytically accumulated wave component and a purely kinetic particle component. The latter is defined by the collisionless transport that survives beyond a prescribed local domain of influence, termed the horizon. This continuous formulation yields a unified wave-particle system valid across the entire Knudsen spectrum, comprising a source-free total conservation law, a wave equation, and a particle equation. The wave operator admits a Chapman--Enskog expansion, whose moments yield Euler and Navier--Stokes fluxes with horizon-dependent coefficients, while the particle equation governs the remaining non-equilibrium kinetic transport. At the algorithmic level, this system is discretized by a conservative macro-micro method. The total conservative variables are advanced by a finite-volume update using the sum of a Navier--Stokes gas-kinetic wave flux and a particle flux computed by either a deterministic discrete-ordinate $(S_N)$ method or a Monte Carlo representation. Unlike the global time-step splitting in the unified gas-kinetic wave-particle (UGKWP) method, the present partition is defined at the PDE level and governed by the local ratio of evolution timescale to relaxation time. The particle component is therefore a fractional kinetic population generated by the collisionless factor of the integral solution. Formal analysis establishes the asymptotic-preserving continuum limit, rarefied-regime consistency, and regime-adaptive scaling of active kinetic degrees of freedom. Numerical tests in one, two, and three dimensions validate the accuracy, multiscale capability, and efficiency of the framework.

math-ph↗

arXiv2026

SAM2Matting: Generalized Image and Video Matting

Despite impressive advances in image matting, video matting remains challenging due to the inherent gap between high-level tracking, which requires frame-wise understanding, and low-level matting, which focuses on extremely fine-grained details. Existing methods attempt this with expensive and narrowly-scoped video matting datasets, which may limit out-of-domain generalization and compromise tracking robustness. We rethink the paradigm with SAM2Matting, a tracker-to-matting framework that advances VOS trackers to high-fidelity video matting. Specifically, it decouples the task by enhancing a foundational tracker (e.g., SAM2, SAM3) with a region-proposal bridge and dedicated matting heads, enabling the uncompromised tracker to handle temporal consistency while the matting components resolve fine-grained details. Notably, despite being trained only on images, SAM2Matting establishes new state-of-the-art performance on video matting, supports diverse prompt types, maintains strong temporal consistency, and demonstrates robust generalization across both human-centric and in-the-wild scenarios.

cs.CV↗

arXiv2026

Inclusive Interactive Collisions for Multi-View Consistent Compositional 3D Generation

Recent breakthroughs in 3D generation have advanced notably with the development of text-to-image diffusion model. However, existing methods remain two practical challenges: (1) They primarily generate single 3D object, but struggle to generate multi-object compositional 3D assets due to the lack of the modeling for Gaussian primitives in reasonable interactions. (2) They often suffer from cross-view inconsistency during 3D optimization, as Score Distillation Sampling inherently performs on each single view, inevitably resulting in cross-view hallucinations. To solve above issues, we propose I2C-3D, a novel optimization-based method to generate multi-view consistent compositional 3D assets with reasonable interactions. Specifically, we propose an Inclusive Interactive Collisions strategy to guide Gaussian primitives appearing in reasonable interaction regions naturally, thereby ensuring objects in the compositional scene interact in a physically plausible and visually coherent way. Additionally, to enhance multi-view consistency, Multi-View Adaptive Score Distillation Sampling is devised to distill multi-view consistency prior and layout prior from pre-trained diffusion model by modulating attention map of instance token and spatial token across viewpoints. Benefiting from above elaborate designs, I2C-3D not only generates high-fidelity multi-view consistent compositional 3D assets but also supports 3D editing flexibly, facilitating complex scene generation. Extensive experiments demonstrate our I2C-3D outperforms existing methods in generation quality and multi-view consistency.

cs.CV↗

arXiv2026

Shellular Metamaterial Design via Compact Electric Potential Parametrization

We present a compact yet highly expressive design space for shellular metamaterials that support both interactive exploration and inverse design. With only a few dozen charges, our representation generates a wide family of periodic shells, spanning from simple planar configurations to complex TPMS-like morphologies. To enable rapid evaluation, we introduce an efficient GPU-based homogenization pipeline that computes the effective elastic tensor of a candidate design in near real time (~ 0.4), making interactive shellular design practical. Across a large set of synthesized structures, our design space exhibits geometric diversity and spans a broad spectrum of mechanical responses, covering a wide range of effective material properties. This fast evaluation further enables inverse design for target macroscopic properties. In the low-solid-volume regime, the resulting shellular structures achieve performance competitive with state-of-the-art shell-based metamaterials in multiple material properties. Finally, we validate manufacturability by fabricating tiled prototypes via additive manufacturing, demonstrating the potential of our approach for real-world engineering applications.

cs.GR↗

arXiv2026

Federated Bilevel Performative Prediction

Federated bilevel optimization is widely used for nested learning problems across distributed clients, such as federated hyperparameter tuning and meta-learning under privacy and communication constraints. Most existing formulations assume fixed client data distributions, which can be violated by performativity, where deployed decisions reshape client behavior and data collection, inducing client-specific, decision-dependent distribution shift. We study federated bilevel performative prediction, where both upper-level (UL) and lower-level (LL) objectives are evaluated under client-dependent, decision-dependent distributions. We formalize the federated bilevel performatively stable (FBPS) point under a decoupled-risk perspective and provide sufficient conditions for its existence and uniqueness. We then develop two federated methods to compute the FBPS solution: FBi-RRM, which converges linearly under a contraction condition, and FBi-SGD, a communication-efficient stochastic method based on federated hypergradient estimation with convergence guarantees under diminishing step sizes when sensitivities are sufficiently small. Experiments on strategic regression and meta strategic classification validate the predicted stability thresholds and demonstrate improved meta-generalization over non-performative baselines, and CNN-based classification further demonstrates the practical effectiveness of the proposed methods in nonconvex neural network settings.

cs.LG↗

arXiv2026

Efficient Image Registration for Ultrasound Localization Microscopy by Obtaining Gradients via Integration Across Iterations

Tissue motion correction through image registration is essential for ultrasound localization microscopy (ULM). Parametric image registration is commonly formulated as an optimization problem where motion parameters are iteratively updated to maximize image similarity, and used optimization algorithms typically rely on gradient information, the explicit evaluation of which can become computationally demanding. This work investigates Extremum Seeking Control (ESC) as an alternative to explicit derivative evaluation in image registration. By obtaining descent information via integrating perturbed and demodulated image similarity metric across iterations, ESC avoids differentiation of the image similarity metric with respect to motion parameters in each iteration. The classical ESC, whose optimization behavior approximates that of classical gradient descent (GD), is first compared with GD for affine image registration using simulated ground-truth motions derived from a beating ex vivo porcine heart dataset. The results show that ESC achieves registration accuracy and convergence behavior comparable to GD while reducing per-iteration computational cost by approximately 3.5-fold. ESC is subsequently employed in a two-stage motion correction pipeline, where affine registration compensates for global tissue motion and B-spline registration corrects residual local deformation. The proposed method is applied to ULM imaging of a beating ex vivo porcine heart and achieves a spatial resolution of 219 um, substantially below the half-wavelength diffraction limit of 321 um associated with 2.4 MHz diverging-wave imaging. These results demonstrate that ESC provides an effective alternative to explicit derivative evaluation in ULM image registration, enabling accurate motion correction and high-quality super-resolution imaging.

eess.IV↗

arXiv2026

SPICE-Q and Large-Scale Quantum Chip Production

We propose SPICE-Q, a SPICE-inspired design-technology co-optimization framework for superconducting quantum processors. Rather than replacing tools such as HFSS, Qiskit Metal, pyEPR, SQcircuit, SQuADDS, scqubits, or QuTiP, SPICE-Q aims to connect them through a unified, traceable data chain spanning process rules, layout, electromagnetic simulation, energy-participation-ratio and circuit quantization, Hamiltonian extraction, noise analysis, cryogenic test, and manufacturing feedback. The central mapping is from process and PDK constraints to layout geometry, electromagnetic modes, equivalent circuit parameters, effective Hamiltonians, and finally metrics such as frequency, coupling, anharmonicity, decoherence, readout performance, and yield. This flow must capture Josephson-junction variability, transmon frequency allocation, resonator and Purcell constraints, coupler crosstalk, microwave routing, 3D interconnects, material/interface loss, package modes, and wafer-scale process statistics. By introducing standardized model interfaces, statistical parameter models, model cards, version governance, and closed-loop calibration from cryogenic and fabrication data, SPICE-Q frames superconducting quantum-chip design as an engineering workflow rather than a collection of isolated simulations. We argue that scalable and fault-tolerant quantum processors will require such a continuous model chain from device physics and electromagnetic fields to quantum dynamics, noise, manufacturability, and system-level yield.

quant-ph↗

arXiv2026

How to Train a Real-World Silicon Concierge? Internalizing Complex Business Workflow to Only OneModel

Traditional industrial agents rely on modular pipelines, including Router, Retriever, Planner, Executor, Responder, Reviewer, and other components. These systems often fracture into a labyrinth of ad-hoc patches, leading to cascading errors and high latency. We propose OneModel, an applicable paradigm shift from external workflows to internalized knowledge representation. Unlike modular systems that slice fluid user intents into static steps, OneModel consolidates complex business logic and SOPs directly into the model parameters. Through Continual Pre-training (CPT) and logic-compilation SFT, we transform fragmented business rules into intuitive model reasoning within a unified attention space. Deployed in our global financial service system, OneModel effectively breaks the trade-off between latency, accuracy, and complexity. Online A/B testing demonstrates an end-to-end latency reduction of more than 50 percent, from 18.7 seconds to 8.0 seconds, while the Intelligent Resolution Rate (IRR) increases from 64.3 percent to 83.3 percent. The results show that OneModel can replace brittle engineering logic with internalized cognitive intuition, offering a scalable blueprint for transitioning industrial agents from complex, error-prone workflows to unified model architectures.

cs.CL↗

arXiv2026

Game-Theoretic Multi-Agent Reinforcement Learning for Swarm Trajectory Planning in Low-Altitude Wireless Networks

The Low-Altitude Economy (LAE) is rapidly expanding, giving rise to low-altitude wireless networks (LAWNs), where large-scale cellular-connected unmanned aerial vehicle (UAV) deployments support heterogeneous mission-critical applications over multi-cell ground base station (GBS) infrastructures. To ensure mission success, each UAV must jointly optimize communication throughput and mission completion efficiency. In fifth-generation (5G) new radio (NR) systems, the equal resource block (RB) allocation policy induces strong strategic coupling among UAV trajectories: when a UAV enters a GBS cell, it reduces the RB share available to all co-served UAVs, thereby altering their achievable rates and trajectory incentives through shared wireless resources. Existing studies either ignore this coupling or focus on single-cell infrastructure, leaving the multi-cell, congestion-aware UAV trajectory planning problem insufficiently addressed. To fill this gap, we formulate the problem as a cooperative stochastic congestion game with a communication-and-mission-aware utility function, and propose a centralized-training decentralized-execution multi-agent proximal policy optimization (CTDE-MAPPO) algorithm to maximize social welfare under multi-cell RB congestion. Simulation results show that the proposed method outperforms QMIX, independent Q-learning, and random baselines in terms of aggregate utility and mission success rate, while achieving stable convergence within practical training budgets.

cs.GT↗

arXiv2026

Detection of persistent helium absorption in the 91bg-like type Ia Supernova 2022an

We present optical and near-infrared observations of the fast-declining Type Ia supernova (SN Ia) 2022an. The photometric and spectroscopic properties identify it as a standard 91bg-like event; however, our data reveal a relatively narrow absorption feature with a full width at half maximum (FWHM) of 75 angstroms near $1.037\,μ$m in the rest frame of the observed spectra that persists from around 30 days to nearly 90 days after maximum light. We attribute this feature to He I $1.083\,μ$m line with a blueshifted velocity of $1.3\times10^{4}$ km s$^{-1}$ and a FWHM of $2.1\times10^{3}$ km s$^{-1}$, supported by the detection of multiple optical He I transitions in earlier epochs at a higher velocity around $1.5\times10^{4}$ km s$^{-1}$. The high velocity of the helium could not be explained by helium external to the progenitor at the explosion, such as the stripped surface helium from a companion star. The properties of the helium absorption in SN 2022an spectra instead point to unburnt material in the outer ejecta, thus providing the most compelling evidence to date for helium-bearing ejecta in a 91bg-like SN Ia. Such helium has been predicted for sub-Chandrasekhar-mass double-detonation explosions involving a surface helium shell. No theoretical calculations of modern helium-shell double detonation have been performed at epochs similar to those observed for SN 2022an to study the effect of helium on their spectra, revealing a gap between observations and theoretical calculations in understanding the manifestation of helium in SNe Ia. Nevertheless, the discovery of persistent helium absorption in SN 2022an demonstrates the diagnostic power of NIR spectroscopy for understanding thermonuclear supernova explosions by probing the abundance and structure of their ejecta.

astro-ph.HE↗

arXiv2026

Focus, Align, and Sustain: Counteracting Gradient Dilution in Incremental Object Detection

Adapting Detection Transformers to Incremental Object Detection (IOD) poses a systemic challenge, as set-based optimization is inherently destabilized by sequential learning. In this work, we identify Gradient Dilution as the root cause of performance degradation, wherein optimization signals required to preserve old knowledge are progressively weakened. This phenomenon manifests as a cascading erosion of preservation gradients in magnitude, direction, and support coverage, driven by three tightly coupled factors: Signal Dispersion, where foreground gradients are overwhelmed by background noise; Assignment Drift, where stochastic query-target matching induces inconsistent gradient trajectories; and Support Attrition, where gradients from retained samples insufficiently cover the old-class feature space, weakening decision boundaries under interference from new classes. To counteract this, we propose FAS, a unified framework that Focuses, Aligns, and Sustains gradient flow throughout incremental learning. Specifically, we introduce prior-injected queries to focus discriminative signals by filtering background interference at the source. We further propose deterministic anchor distillation to align query-target assignments and enforce semantic consistency across stages under unstable matching. Finally, we devise manifold-support replay to sustain distributional support of old classes, counteracting representational erosion induced by continual updates. Extensive experiments show that FAS restores robust optimization dynamics and outperforms state-of-the-art methods, achieving over 5.0 AP improvement in the challenging 40+10x4 incremental setting.

cs.CV↗