arXiv subjects

Liang Li

Publications and source records attributed to Liang Li.

At least 37 records · Page 2Linked to original sources

arXiv2026

GRB 220101A: a most energetic $10^{54}$ erg long GRB triggered by two supernovae 3.5 seconds apart

GRB 220101A is a long GRB, with a total energy exceeding $10^{54}$ erg with a redshift $z = 4.61$ and one of the largest ever high-quality multi-wavelength observational coverage, from a large number of space-based and ground-based telescopes. We interpret this source in a doubly Binary driven peta nova (BdP-N) model. The progenitor is composed of a massive CO core of $\sim 10\,M_\odot$, highly magnetized with $B \sim 10^{6}$ G, associated to a neutron star (NS) and a white dwarf (WD) with orbital periods from minutes to hours. The large GRB luminosity is explained by a sequence of 7 episodes: episode 1 is triggered by a new kind of pair supernova (HB) which originates from the collapse of the strongly magnetized CO core. Accretion of the HB supernova ejecta (the ejecta) onto the white dwarf companion triggers after 3.5 sec the episode 2: the second supernova emitting neutrinos and creating a new neutron star ($ν$NS). The ejecta, interacting with the magnetosphere of the binary NS companion originate the episode 3: the Ultra relativistic Prompt Emission (UPE) emission by far the most energetic episode of this GRB, with the formation of a powerful jet normal to the plane of the GRB. Following the UPE energy loss, the accretion of the ejecta on the NS companion leads to the episode 4: the formation of a black hole (BH) of $2.3 \ M_\odot$ leading to the observed GeV afterglow emission. Further accretion of the ejecta spin up the $ν$NS to a period of $1.3$ ms which gives origin to the episode 5: the birth of a pulsar. The interaction of this milli-second pulsar with the remnants lead to the Episode 6: the synchrotron emission observed in the X-ray, optical and radio, The episode 7 is a 56.7 ms pulsar, as observed $10^{10}$ s after the first burst in the crab nebula.

astro-ph.HE↗

arXiv2026

Tilted $p$-wave magnet candidate CeNiAsO

The unexpectedly small ordered moments of CeNiAsO, a candidate for correlated $p$-wave magnet, have posed a serious challenge to the precise determination of its magnetic structure, hindering the understanding of its fundamental properties. By leveraging the high sensitivity to local internal fields, our $^{75}$As nuclear quadrupole / magnetic resonance experiments reveal a commensurate antiferromagnetic order with a small out-of-plane moment $m_z\approx0.05$ $μ_{\mathrm{B}}$. This tilted magnetic configuration not only rotates the spin polarization axis away from the crystallographic $\mathbf{c}$-axis, but also enhances the non-relativistic spin splitting. We refer to this rare paradigm as a \textit{tilted $p$-wave magnet}.

cond-mat.str-el↗

arXiv2026

InstructVVT: Instruction-Driven Video Virtual Try-On without Auxiliary Spatial Priors

Video virtual try-on is a highly constrained editing task requiring the precise replacement of a target person's clothing while strictly preserving the original video's spatial structure and temporal dynamics. Existing methods heavily rely on auxiliary handcrafted spatial priors (e.g., masks, poses) for editing control. However, these priors are prone to failure in unconstrained real-world videos and often compress rich visual context into incomplete structural signals. Furthermore, standard reconstruction objectives fail to fully capture try-on-specific human preferences. To address these challenges, we propose InstructVVT, an instruction-driven and reference-guided video virtual try-on framework based on a Diffusion Transformer (DiT) that operates without inference-time spatial priors. Our core insight is to recover fine-grained control directly from the input triplet (source video, reference garment, and instruction) via a dual-level reference conditioning scheme. Specifically, an MLLM infers semantic edit tokens for target disambiguation and structural preservation, while a lightweight conditioning pathway explicitly injects fine-grained visual garment details. Finally, we design a try-on-specific reward and utilize the DiffusionNFT algorithm to align the model with human preferences. Extensive experiments on ViViD-S and TripVVT-Bench demonstrate that InstructVVT outperforms state-of-the-art open-source methods in garment fidelity, structural preservation, and temporal consistency, despite requiring fewer inference-time controls.

cs.CV↗

arXiv2026

ROEVO: Robust Organized Edge Feature-based Visual Odometry Using RGB-D Cameras

This work presents a visual odometry (VO) system that leverages image edge features. Edges are spatially expressive cues commonly present across diverse environments, offering rich textural and structural information. However, existing edge-based VO methods often fail to fully exploit this potential. To this end, we introduce a novel feature representation termed \textit{organized edges}, which transforms disjoint edge pixels into sequentialized clusters, enabling more effective retention and utilization of the underlying textural and structural information. Another nice property of this formulation is that organized edges can perform edge-level association across multiple frames, enabling the establishment of a co-visibility graph. To achieve precise and efficient pose estimation, we propose a range of particularly designed tracking and joint optimization methods based on the characteristics of organized edges. For tracking, we formulate edge-wise rather than pixel-wise residuals to achieve robust and accurate inter-frame registration. For joint optimization, we introduce a novel shape-preserving edge-fitting method and an organized edge-based Bundle Adjustment (BA) approach, which decomposes the traditional BA problem into fitting and registration to preserve the structural integrity. Based on these novel techniques, we develop a complete VO system that exclusively employs organized edge features, achieving efficient tracking and precise local mapping. Extensive experiments demonstrate its accuracy and robustness in indoor environments, outperforming or achieving comparable performance to state-of-the-art methods. The source code is publicly available at https://github.com/liumingrui814/ROEVO

cs.RO↗

arXiv2026

Hierarchical rank-evolving representation for physics-informed neural networks

Recently, tensor-based physics-informed neural networks (T-PINNs) have received increasing attention. However, existing T-PINNs still face a fundamental challenge: they mainly rely on pre-specified low-rank tensor decompositions with manually tuned ranks, which limits their ability to capture the underlying structures of multivariate solution functions and hinders their practical deployment. To address this challenge, we propose a hierarchical rank-evolving (abbreviated as HRE) representation for multivariate functions, which endows us to faithfully capture the underlying structure of the targeted multivariate function accompanying with automatic rank determination. Concretely, in the hierarchical design of HRE representation, the target multivariate function is decomposed as a small-scale inner tensor with a set of univariate functions along each mode, where a customized tensor network decomposition can be readily deployed to capture the underlying structure of the small-scale inner tensor. In HRE representation, the crucial hyperparameters, ranks, can be adaptively revealed during the decomposition, freeing us from manual rank tuning and making HRE practically applicable to real-world problems. Besides, we build the HRE-PINNs correspondingly. Extensive numerical experiments, including high-dimensional static problems (Helmholtz equation and Poisson equation), nonlinear time-dependent problems (Klein-Gordon equation), and complex fluid-dynamics problems (flow mixing equation and Navier-Stokes equation), demonstrate that HRE-PINNs consistently outperform existing state-of-the-art approaches in terms of accuracy.

cs.LG↗

arXiv2026

Towards More Expressive Spoken LLMs: Fine-Grained Intent Benchmarking and Acoustic-Lexical Decoupled Policy Optimization

Spoken emotional dialogue requires a model to understand a user's spoken input and generate a response that is both semantically appropriate and emotionally expressive. This is challenging because communicative intent may be stated explicitly in lexical content or conveyed more implicitly through paralinguistic cues, which can complement or diverge from the words themselves. However, two limitations constrain progress in this area: the scarcity of benchmarks that distinguish these intent expressions, and the lack of reinforcement learning objectives that jointly account for response quality and emotional expression. To address the lack of suitable benchmarks, we introduce ParaIntent, a Chinese benchmark comprising 14 intent categories with balanced explicit and implicit samples, together with a multidimensional evaluation protocol covering intent fulfillment, response quality, and emotional expression. For policy optimization, existing approaches either use a shared objective for text and speech or apply reinforcement learning to only one modality, leaving modality-specific learning signals entangled within policy optimization. Motivated by this, we propose Acoustic-Lexical Decoupled Policy Optimization (ALPO), which computes independent textual and acoustic advantages and routes them to the corresponding text and speech tokens within a unified rollout. Under identical reward functions and training budgets, ALPO improves over standard GRPO on most automatic metrics and achieves the best subjective results among the fine-tuned variants, with particularly clear gains in emotional expressiveness on both the synthetic and human-recorded test sets.

cs.SD↗

arXiv2026

Gaze-DETR: Top-Down Guidance Through Priority Maps for Infrared Weak-Small UAV Detection with DETR

Infrared small target detection (ISTD) remains challenging because tiny, low-contrast targets are easily overwhelmed by clutter, noise, or occlusion. Conventional single-frame and multi-frame detectors rely on bounding-box supervision, which specifies final target locations but offers little explicit guidance for prioritizing candidate regions or preserving weak-target evidence before localization. Task-driven visual search offers such guidance: top-down goals and visual evidence jointly form a spatial priority map that ranks candidate locations. Building on this principle, we propose Gaze-DETR, a bio-inspired detector that learns an internal priority map before localization. First, a priority head predicts a normalized priority map from image features. Second, Residual Priority-Guided Feature Modulation (RPFM) enhances high-priority responses while retaining multi-scale features. Finally, Priority-Guided Anchor Query Injection (PAQI) converts high-priority locations into decoder anchor queries. We train the priority head using three supervision schemes: box-derived Gaussian maps; real-gaze maps constructed from fixation-density maps; and transferred pseudo-gaze maps learned from gaze--box relations in paired annotations and applied to Anti-UAV410 training boxes. To support the latter two schemes, we construct TIR-UAV120-Gaze with paired detection and task-driven eye-tracking annotations. On TIR-UAV120-Gaze, Gaze-DETR achieves 85.76 mAP$_{50}$ and 88.77 F1 with box-derived supervision, and 86.18 mAP$_{50}$ and 89.00 F1 with real-gaze supervision. On Anti-UAV410, it achieves 87.06 mAP$_{50}$ and 90.90 F1 with box-derived supervision, and 87.08 mAP$_{50}$ and 90.43 F1 with transferred pseudo-gaze supervision. These results show that explicit spatial-priority learning provides pre-localization guidance complementary to bounding-box supervision across annotation settings and costs.

cs.CV↗

arXiv2026

Unveiling Complex Collective Behaviors from Simple Rewards

Multi-agent Reinforcement Learning (MARL) holds great potential for robot swarms, but the black-box nature of neural policies complicates strategic analysis, limiting multi-robot applications. Furthermore, complex swarm behaviors can surprisingly emerge from simple rewards without explicit aggregation incentives. Unveiling the mechanisms behind this emergence is critical, but the disconnection between simple rewards and collective behaviors exacerbates interpretability challenges. This paper aims to reveal the hidden mechanisms in this process. We propose a two-stage EEC (\LinkIII) explanatory framework. This includes a novel analytical tool called the Agent Response Map (ARM), which reveals agents' decision-making patterns across space and identifies regions of aggregation and avoidance. ARM reveals that the robots implicitly learn the geometric fields of the environment and utilize these structures as desired targets for coordinated movement. We validate this finding across two distinct tasks: a cooperative multi-robot shape assembly and a competitive predator-prey pursuit-evasion. 1) In the cooperative task, ARM identifies the unoccupied target interior as the desired destination for robot navigation. As the center becomes occupied, this target region automatically shifts toward the boundary, demonstrating the robots' capacity to autonomously explore unoccupied areas. 2) In the competitive task, ARM surprisingly identifies the boundary of the predators' Voronoi diagram as the convergence destination for prey agents. Together, these two tasks demonstrate the capability of ARM to discover the hidden geometric structures underlying MARL policies in robot swarms.

cs.RO↗

arXiv2026

A second-scale periodicity in an active repeating fast radio burst source

Fast radio bursts (FRBs) are fierce radio flashes from the deep sky. Abundant observations have indicated that highly magnetized neutron stars might be involved in these energetic bursts, but the underlying trigger mechanism is still enigmatic. Especially, the widely expected periodicity connected to the spin of the central engine has never been discovered, which leads to further debates on the nature of FRBs. Here we report the first discovery of a $\sim$ 1.7 s period in the repeating source of FRB 20201124A. This is an active repeater, from which more than 2800 bursts have been detected over a total of 49 days. The phase-folding method is adopted to analyze the bursts on each day separately. While no significant periodic signal is found in most days, a clear periodicity does appear on two specific days: a period of 1.706024(13) s on MJD 59310, and a slightly larger period of 1.707968(9) s on MJD 59347. A global Monte Carlo analysis based on all single-day datasets yields a significance level of $5.5 σ$ for the periodicity. A period derivative of $6.11(5)\times10^{-10}$ s s$^{-1}$ can be derived from these two periods, which further implies a surface magnetic field strength of $1.03\times10^{15}$ G and a spin-down age of $44$ years for the central engine. It is concluded that FRB 20201124A should be associated with a young magnetar.

astro-ph.HE↗

arXiv2026

Microscopic quasifission dynamics of the ${}^{54}\text{Cr}+{}^{243}\text{Am}$ reaction

Synthesizing superheavy elements (SHEs) like $Z=119$ is severely hindered by the dominant quasifission (QF) channel, which prevents compound nucleus formation. Understanding QF dynamics is thus essential for future experiments. We investigate the QF mechanisms in the $^{54}\text{Cr}+^{243}\text{Am}$ reaction, a key candidate system for SHE 119, emphasizing the roles of projectile orientation and incident energy. Calculations are performed using the fully microscopic time-dependent Hartree-Fock theory based on the Skyrme energy density functional. We conduct systematic simulations covering a broad set of initial orientations of the deformed $^{54}\text{Cr}$ and $^{243}\text{Am}$ nuclei, alongside a finely spaced range of incident energies extending from below to well above the Coulomb barrier. Our fixed-energy results show that projectile side collisions are governed by shell effects driving heavy and light fragments toward spherical $Z=82$ and deformed $N=52\text{--}56$ closures, respectively, whereas tip collisions exhibit weaker shell influence. These shell-dominated reactions are characterized by shorter interaction times, attributed to the enhanced rigidity of shell-stabilized fragments accelerating neck rupture. The energy dependence reveals a complex evolution where the system transitions from an octupole-stabilized regime ($Z \approx 88$) to a spherical shell-driven regime, with specific energy windows exhibiting suppressed shell influence. Our study demonstrates that the manifestation of shell effects in QF is a dynamical outcome sensitively dependent on both collision geometry and incident energy. Systematically probing this energy sensitivity is crucial for identifying optimal incident energies where the QF process exhibits suppressed shell influence, thereby potentially enhancing the fusion probability and improving the prospects for synthesizing new SHEs.

nucl-th↗

arXiv2026

GARDEN: Gravity-Aligned Reconstruction of Disentangled ENvironments from RGB images

Converting multi-view RGB observations into simulation-ready 3D environments remains challenging because current reconstruction pipelines produce monolithic scene representations without explicit physical structure. They are typically defined up to an arbitrary global rotation and entangle rigid foreground objects with background geometry, which hinders stable physical interaction. Existing solutions often recover interactivity by replacing reconstructed objects with retrieved CAD assets, but this introduces a slow retrieval-and-replacement stage and weakens scene-specific geometric fidelity. We propose GARDEN, an RGB-only framework that reformulates reconstruction as physically-grounded scene factorization and outputs a structured hybrid scene representation. The key idea is to use gravity as a universal physical prior: we first align the reconstruction to a unified Gravity-View frame to resolve gauge ambiguity, then recover object-centric rigid meshes with accurate 6-DoF placement, and finally remove duplicate object geometry from the background through conditional 3D point classification. The resulting representation combines explicit rigid bodies with a decoupled background, enabling direct physics simulation while preserving visual realism. Experiments on both simulated and real multi-view scenes show that GARDEN improves object placement reliability, disentanglement quality, and rendering-simulation efficiency compared with retrieval-based baselines. Project page: https://sunjiahaovo.github.io/garden/

cs.CV↗

arXiv2026

Dynamic Scheduling for Flexible Manufacturing Systems Based on Multi-Agent Deep Reinforcement Learning and Petri Nets

This paper investigates dynamic scheduling for flexible manufacturing systems (FMSs) subject to dynamic events, such as new order arrivals, temporary order cancellations, and machine failures. Traditional methods often face significant challenges in achieving real-time responsiveness under such conditions. To address this issue, the scheduling problem is formulated as a Markov decision process (MDP) with timed Petri nets, where the future evolution of the system depends exclusively on the current marking and the subsequently executed transitions, independent of historical trajectories. The state space and action space of the MDP are constructed using the notion of basis reachability graph (a compact state space representation) of Petri nets to alleviate the state explosion problem, thereby accelerating model training convergence. Meanwhile, a hierarchical dense reward function is constructed by integrating stepwise guidance with terminal evaluation. Then, a multi-agent proximal policy optimization algorithm is employed for model training under the centralized training and decentralized execution paradigm to improve scheduling efficiency. Numerical experiments are conducted involving typical dynamic events, and the results demonstrate that the proposed method can effectively handle dynamic events and achieve superior scheduling performance compared with conventional approaches.

eess.SY↗

arXiv2026

Density-wave order enhances the phonon thermal Hall effect in a trilayer nickelate

Ruddlesden--Popper nickelates have emerged as a promising platform for high-temperature superconductivity, yet the role of lattice degrees of freedom in their correlated normal state remains largely unexplored. Here, we report the observation of a finite phonon thermal Hall effect in the trilayer nickelate La$_4$Ni$_3$O$_{10}$ at ambient pressure. Remarkably, the thermal Hall response is strongly enhanced below the density-wave transition at $T^*\approx140$ K, exhibiting two distinct plateaus in the thermal Hall resistivity. The characteristic energy scale extracted from the thermal Hall response ($\sim4.1$ meV) closely matches the magnon--phonon crossing span energy ($\sim3.2$ meV), pointing to magnon--phonon hybridization as the primary mechanism enhancing the thermal Hall effect. These results provide new insight into the interplay between lattice and spin excitations in nickelates, with implications for understanding both their superconductivity and the multiple possible origins of insulating thermal Hall signals.

cond-mat.str-el↗

arXiv2026

Decentralized Pose Graph Riemannian Optimization for Object-based Multi-Robot SLAM

Pose graph optimization (PGO) is a key back-end component for state estimation in networked multi-robot simultaneous localization and mapping (SLAM). In object-based multi-robot SLAM, the problem becomes more tightly coupled because robots must jointly estimate both their trajectories and the poses of persistent objects observed by multiple agents. Existing decentralized solutions often assume that the communication graph closely matches the physical interaction topology, which is restrictive in realistic deployments where communication is sparse, intermittent, or time-varying. This paper presents a fully decentralized Riemannian optimization framework for object-based multi-robot PGO that decouples the coupled estimation problem via a consensus mechanism, enabling flexible communication topologies. To improve convergence under limited communication budgets, we further develop a distributed approximate-Newton scheme that exploits local second-order information while operating directly on the SE(d) manifold to preserve geometric consistency, and we establish the convergence to Riemannian first-order stationary points and provide a local condition-number analysis explaining the benefit of approximate second-order information over first-order Riemannian descent. The resulting method reduces iteration count and communication overhead without sacrificing estimation accuracy. Extensive evaluations on public benchmarks, large-scale simulations, and real-world multi-robot experiments demonstrate improved accuracy, runtime efficiency, scalability across network topologies, and robustness to communication failures.

cs.RO↗

arXiv2026

Electric-field-driven magnetic switching and tightly bound interlayer excitons in bilayer CrSBr

Electric field control of magnetic order in two-dimensional (2D) van der Waals magnets is a central goal for low-power spin-based technologies. In the ambient-stable antiferromagnet CrSBr, strong magnetic anisotropy and robust exciton-spin coupling provide a favorable platform, yet deterministic electric field control of its magnetic phases has not been achieved. Here we demonstrate electric-field-driven reversible switching between antiferromagnetic and ferromagnetic states in dual-gated bilayer CrSBr without intentional carrier doping. In parallel, photoluminescence measurements resolve a tightly bound interlayer exciton with an intrinsic dipole moment of only ~1 e angstrom. The electric field dependence of the magnetic phase transition reveals two coexisting mechanisms: a linear magnetoelectric effect in the antiferromagnetic state and an electric-field-modulated interlayer exchange coupling. Their interplay accounts for the asymmetric evolution of the critical magnetic field. Our results establish bilayer CrSBr as a promising 2D material for electrically controlled spin-optoelectronic functionalities.

cond-mat.mes-hall↗

arXiv2026

UniDDT: Unifying Multimodal Understanding and Generation with Decoupled Diffusion Transformer

Unified Multimodal Models (UMMs) have emerged as a critical direction for general-purpose multimodal intelligence, integrating understanding and generation into a single framework. However, existing UMMs face prominent challenges: (1) the inherent learning conflicts between visual understanding and generation tasks, leading to suboptimal modeling in both tasks; (2) different understanding and generation visual spaces impeding scalability; (3) over-reliance on task-specific data that neglects the duality of text-image understanding and generation. To address these challenges, we propose UniDDT, which leverages a Noisy ViT encoder along with an LLM to unify semantic encoding for visual generation and understanding tasks, while employing a separate diffusion decoder to decouple diffusion decoding from text decoding. With this Noisy ViT encoder, UniDDT is able to leverage the latent space as a unified visual representation, enabling seamless compatibility between understanding and generation tasks. Thus, the scalability within the generation tasks and the semantic expressiveness within understanding tasks can be balanced. Also, we construct dual data structures from the same image-text pairs, fostering interdependence between the generation and understanding data to exploit their inherent duality. Extensive experiments demonstrate that UniDDT achieves effective unification of multimodal understanding and generation with enhanced semantic consistency and scalability. For visual generation tasks, our UniDDT achieves 0.87 GenEval score and 86.9 DPG overall score. For multimodal understanding tasks, our UniDDT achieves 1699.5 score on MME benchmark and 76.5 overall score on SEEDbench.

cs.CV↗

arXiv2026

VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands

Humanoid robots hold immense potential for real-world assistance, yet agile interaction with objects in unstructured environments demands tightly coupled whole-body coordination. Despite recent advancements, current controllers face a critical deployment gap. They rely heavily on dense reference trajectories and perfect state observability, which inherently limits physical generalization. We present Vision Guided Agile Interaction Control (VAIC), a unified framework that bridges this gap by operating exclusively on onboard depth, historical proprioception, and a decoupled user command interface. VAIC employs a two-stage distillation paradigm. First, a privileged teacher policy masters diverse interaction skills using precise object kinematics and exact environmental states. Second, a deployable student policy distills these capabilities by replacing full body tracking with velocity targets across multiple axes and an interaction indicator for each frame. The student utilizes a recurrent object adaptation module to implicitly infer unobservable object dynamics from raw depth streams and proprioception. Evaluations and real-world deployments on the humanoid robot demonstrate that a single VAIC policy successfully executes highly diverse dynamic tasks. These tasks include box carrying, cart interaction, and skateboarding, consistently outperforming baselines and advancing autonomous humanoid deployment.

cs.RO↗

arXiv2026

MetaPoint: Unlocking Precise Spatial Control in Agentic Visual Generation

Generative visual models fundamentally struggle with precise spatial control. This arises from a core disconnect: models can process textual descriptions of space but cannot directly map numerical coordinates onto the 2D image canvas. We introduce MetaPoint, a method that bridges this gap by representing a continuous 2D coordinate as a single, special token. Crucially, MetaPoint requires no new architectural components; it directly leverages the model's inherent positional encoding schemes to interpret these coordinates, treating our token as a virtual point on the canvas. This lightweight approach enables pixel-level control of an object's position with one token or its bounding box with two, all without requiring architectural changes or bespoke attention masking. The MetaPoint tokens are designed to be compositional, serving as spatial primitives. This allows a planner agent to decompose a high-level user request into a structured sequence of primitives for the generator. By providing a simple, precise, and scalable building block for spatial control, MetaPoint unlocks more powerful compositional generative agents and enables intuitive, interactive editing systems.

cs.CV↗