arXiv subjects

Bo Wang

Publications and source records attributed to Bo Wang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Momentum-Space Planar Optics for Long-Range Spin Hamiltonians

Evaluating spin Hamiltonians with dense, long-range interactions requires repeated global summation over many pairwise couplings, making energy evaluation costly on conventional electronic hardware. Here, we realize a planar optical architecture that evaluates such interaction energies directly in momentum space. For translationally structured couplings, the interaction kernel is encoded in the spatial transmission of a subwavelength-thick planar optical element placed in the Fourier plane. Pointwise spectral weighting followed by total-power detection reduces the quadratic spin Hamiltonian to a single optical signal, avoiding repeated digital multiply-accumulate operations during energy evaluation. We experimentally apply this approach to frustrated finite-range models and fully connected spin systems with oscillatory long-range couplings. The measured energies agree closely with numerical calculations, while optical annealing reproduces low-energy configurations and landscape statistics, reaching a normalized solution quality of 99.9%. These results establish momentum-space planar optics as a compact physical computing primitive for global-interaction problems in statistical physics and optimization.

physics.optics↗

arXiv2026

Partial-Scan-and-Move Source Seeking for Mobile Robots

This paper presents a partial-scan-and-move strategy for source seeking with a mobile robot equipped with an offset scalar sensor. At each robot position, the sensor collects source field measurements while the robot rotates. Instead of requiring a complete circular scan before every move, we ask when the measurements collected over only part of the circle are already sufficient to determine the next action. We develop a gradient estimation method for partial scans together with a confidence set that accounts for measurement noise and local field variation. The robot uses this confidence set to decide whether it is close enough to the source or has enough information to move in a descent direction. We show that, under suitable conditions, each decision can be made within a prescribed partial scan and that the robot reaches a desired neighborhood of the source in finitely many moves with high probability.

eess.SY↗

arXiv2026

The PSR J0435+3233 Triple System

The detailed evolution of triple star systems is complicated and poorly known. Using archived optical/infrared and $γ$-ray data, we identified the pulsar PSR~J0435+3233 as a $γ$-ray pulsar in a hierarchical triple system, with a helium white dwarf (WD) as a close inner binary companion and a Sun-like star as the distant tertiary. PSR~J0435+3233 and the WD companion are in a circular orbit with a period of $P_{\rm orb1} = 8.0$~days and an eccentricity of $e=0.00016$. The tertiary is a G-type subgiant with a mass of $0.98(12)\, M_\odot$ at a distance of $2.1(4)$\, kpc from the Earth. By simultaneously fitting the observed spin-period variations of the $γ$-ray emission (over 16.7 years) and radio emission (over 4.9 years) from PSR~J0435+3233, the changes in the inner orbital parameters, the Shapiro delay, Gaia astrometry, and the outer companion mass, we determined the outer elliptical orbit for the tertiary, with a period $P_{\rm orb2} = 73.5$~yrs and an eccentricity $e_2 = 0.598$. The outer orbit is either nearly perpendicular to the inner orbit with a mutual inclination of $\sim 85^\circ$, or moderately inclined by $\sim 54^\circ$. For the former geometry, the pulsar, the WD, and the tertiary star have masses of $1.16(5)\, M_\odot$, $0.273(8)\, M_\odot$, and $0.91(5)\, M_\odot$, respectively; for the latter geometry, the corresponding masses are $1.34(15)\, M_\odot$, $0.305(23)\, M_\odot$, and $1.13(7)\, M_\odot$. This is a unique triple system for detailed multi-band observations and for studying the evolutionary path and dynamic processes of a primordial triple star system. It will ultimately evolve into a system consisting of a neutron star and two white dwarfs.

astro-ph.HE↗

arXiv2026

Anomalous Criticality of Absorbing State Transition toward Jamming

The jamming transition has traditionally been regarded as a geometric transition governed by static contact networks. Recently, dynamic phase transitions of athermal particles under periodic shear have offered a new perspective on this problem, leading to the conjecture that the jamming transition corresponds to an absorbing-state transition belonging to the conserved directed percolation (C-DP) or Manna universality class. Here, by re-examining the biased random organization (BRO) models on which this conjecture is based, we uncover anomalous criticality at high density. In disordered packings, we find a dynamic transition from absorbing to active-glass states with continuously varying dynamic critical exponents. Near the jamming point, quenched disorder arising from heterogeneous contact networks induces Griffiths effects, smearing the critical point into a pseudo-critical regime. In close-packed crystals, C-DP-like dynamic criticality is restored. We propose a field-theoretic description incorporating dynamical slowing down and quenched disorder to account for this anomalous criticality, providing a unified framework that links glassiness, jamming, and dynamic criticality.

cond-mat.stat-mech↗

arXiv2026

Collider Detector Observables from Lattice Spin Systems

Detector observables pose a fundamental challenge for nonperturbative lattice methods due to their intrinsically real-time and asymptotic nature. We propose quantum simulation platforms as an ideal setting for studying these observables. Using quantum spin systems as a demonstration, we construct lattice detector operators and establish their connection to continuum energy-flow observables. We demonstrate this framework in the $1+1$-dimensional quantum Ising model, where finite lattice calculations show quantitative agreement with conformal field theory expectations at criticality. These results support the interpretation of the lattice flow operators as regulated counterparts of continuum detectors in the scaling and asymptotic limits, providing a concrete route toward realizing high-energy collider physics on table-top quantum platforms.

hep-ph↗

arXiv2026

A prolonged plateau-to-tail transition in the Type II supernova SN2025abyc

We present optical photometric and spectroscopic observations of the Type II supernova SN2025abyc. During the optically thick phase between approximately 10 and 70 d after explosion, its light curves show strongly wavelength-dependent decline rates of approximately 2.7, 2.1, 0.9, and 0.8 mag/100d in the g, c, r, and o bands, respectively. At approximately 70 d, the light curves begin to depart from their nearly linear plateau evolution and gradually transition toward the radioactive tail. A Fermi-Dirac fit to the well-sampled ATLAS o-band light curve yields a transition midpoint of t_PT ~ 100.5d. The interval between the end of the linear plateau and this transition midpoint is approximately 30 d, indicating a prolonged plateau-to-tail transition. This timescale is comparable to those measured for SN2013by, SN2013ej, and SN2014G. Spectroscopically, at +13 d post-explosion, the Halpha profile appears weak and broad, whereas Hbeta and Hgamma display clear P-Cygni profiles. This morphology can be explained by the normal early spectroscopic evolution of SNe II, although partial filling of the Halpha absorption trough by emission associated with circumstellar interaction cannot be excluded. SN2025abyc otherwise follows the general photospheric velocity evolution of SNe II, while remaining toward the high-velocity side of the comparison distribution in Halpha, Hbeta, and FeII. Exploratory light-curve modelling suggests a synthesized Ni mass of approximately 0.03-0.04 solar mass. We suggest that the extended circumstellar environment, Ni distribution, and hydrogen-envelope structure could all play a role in shaping the observed light-curve evolution, particularly the prolonged plateau-to-tail transition.

astro-ph.SR↗

arXiv2026

Construction of Control Lyapunov-Barrier Functions from CLF-CBF Pairs

This paper studies the construction of control Lyapunov-barrier functions (CLBFs) from a given control Lyapunov function (CLF) $V$ and control barrier function (CBF) $h$. We consider functions of the form $W=F(V,h)$ that increase with the CLF value and do not increase with the barrier value, and show that the CLBF decrease condition is completely characterized by a nonnegative scalar weight that governs the relative contributions of the CLF and CBF. Because this weight depends only on $(V,h)$, the same value must satisfy the decrease condition at all states sharing the same CLF and CBF values, which leads to a joint-level-set admissibility condition. We then construct CLBFs from admissible weights through a first-order partial differential equation (PDE) and obtain explicit multiplicative and power-type families as special cases. We further identify an integrability obstruction showing that admissibility alone does not guarantee properness. Two nonlinear examples illustrate the constructions and demonstrate safe stabilization in cases where feedback based on the original CLF violates the safety constraint.

eess.SY↗

arXiv2026

When Ad Networks Misbehave: Understanding Risks of Semi-Drive-By Splash Ads

We investigate the mobile splash ads ecosystem, i.e., full-screen advertisements shown at app launch, where monetization relies on interaction signals that are difficult to verify end-to-end. This setting is especially sensitive because incidental touches and sensor-driven callbacks are common yet easy to misattribute as engagement. Prior work has largely framed mobile ad fraud as a publisher-side problem, while some studies attribute fraudulent operations to embedded ad libraries. Yet an important risk remains underexplored: ad SDKs control how interaction signals are interpreted, measured, and reported, creating an opportunity to reinterpret ambiguous user or device signals as valid advertising interactions. We uncover a previously less-known form of fraud at the ad-network layer in which splash ads are triggered not by intentional user actions but by incidental or indirect interactions, which we term semi-drive-by splash ads. By translating non-ad interactions into billable engagement events, ad networks can inflate performance metrics, overcharge advertisers, and erode user trust. To expose this behavior in the wild, we design AdHive, an automated honeypot-like analysis framework that induces evasive splash-ad delivery and landing behaviors under realistic device conditions. AdHive reproduces human-like activity through LLM-generated usage traces and sensor dynamics, enabling execution paths that remain hidden in conventional analysis environments. Our large-scale measurement across thousands of popular Android applications shows that semi-drive-by splash ads are widespread and are often triggered by subtle signals such as minor sensor variations. We further confirm real-world impact by working with one of China's largest advertisers, identifying multiple ad networks engaging in this fraud and leading to enforced repayments of about 4 million Yuan (approximately US$600,000).

cs.CR↗

arXiv2026

StageWell: A Process-Aligned Chinese Corpus for Positive-Psychology Support Dialogue

Positive psychology dialogue aims to support emotional distress and positive resource building, requiring models to produce not only empathetic replies but also coherent progression through a multi-turn support process. Existing resources often reduce supervision to turn-level strategies or holistic preference labels, leaving process position, support function, and local repair targets implicit. We introduce StageWell, a process-aligned Chinese corpus for positive psychology dialogue, together with HQS, a structured protocol for data construction and evaluation. StageWell organizes support into a six-stage support process and uses a multi-agent whole-dialogue rewriting workflow to construct 12,445 SFT instances, 1,849 DPO preference pairs, and a GroundTruth subset of 120 expert-revised dialogues and 977 QA pairs. Guided by HQS, DPO pairs are built as process-localized repairs: flawed model outputs are used as rejected responses, and targeted rewrites under the same context and stage constraint are used as chosen responses. Across four 9B-14B open-source LLMs, this supervision yields robust gains in process control, response quality, and safety. Averaged across models, BERTScore improves by 0.037, Q-Overall increases by 1.32 points, S-exact increases by 0.236, and the H-critical rate decreases by 0.167. These results highlight the value of modeling supportive dialogue as a structured multi-turn support process rather than as single-turn response generation.

cs.CL↗

arXiv2026

DAGLFNet: Deep Feature Attention Guided Global and Local Feature Fusion for Pseudo-Image Point Cloud Segmentation

Environmental perception systems are crucial for high-precision mapping and autonomous navigation, with LiDAR serving as a core sensor providing accurate 3D point cloud data. Efficiently processing unstructured point clouds while extracting structured semantic information remains a significant challenge. In recent years, numerous pseudo-image-based representation methods have emerged to balance efficiency and performance by fusing 3D point clouds with 2D grids. However, the fundamental inconsistency between the pseudo-image representation and the original 3D information critically undermines 2D-3D feature fusion, posing a primary obstacle for coherent information fusion and leading to poor feature discriminability. This work proposes DAGLFNet, a pseudo-image-based semantic segmentation framework designed to extract discriminative features. It incorporates three key components: first, a Global-Local Feature Fusion Encoding (GL-FFE) module to enhance intra-set local feature correlation and capture global contextual information; second, a Multi-Branch Feature Extraction (MB-FE) network to capture richer neighborhood information and improve the discriminability of contour features; and third, a Feature Fusion via Deep Feature-guided Attention (FFDFA) mechanism to refine cross-channel feature fusion precision. Experimental evaluations demonstrate that DAGLFNet achieves mean Intersection-over-Union (mIoU) scores of 69.9% and 78.7% on the validation sets of SemanticKITTI and nuScenes, respectively. The method achieves an excellent balance between accuracy and efficiency.

cs.CV↗

arXiv2026

Dynamic Water-Wave Tweezers

Following a recent demonstration of stable trapping of floating particles by stationary (monochromatic) structured water waves [Nature 638, 394 (2025)], we report dynamic water-wave tweezers that enable controllable transport of the trapped particle along an arbitrary trajectory on the water surface. Furthermore, we demonstrate simultaneous transport of two trapped particles along different trajectories. We employ a triangular lattice formed by the interference of three plane waves, which can trap particles, depending on the wave frequency and particle parameters, either at intensity maxima or at intensity zeros (vortices). By introducing small frequency detunings of the interfering waves, we control 2D motion of the lattice and trapped particles. This approach is robust and effective over a relatively broad range of particle sizes and wave frequencies, offering remarkable new possibilities for noncontact manipulation of floating (e.g., biological and soft-matter) objects in fluidic environments.

physics.flu-dyn↗

arXiv2026

Optical Second Harmonic Generation in Anisotropic Multilayers with Complete Multireflection of Linear and Nonlinear Waves using #SHAARP.ml Package

Optical second harmonic generation (SHG) is a nonlinear optical effect widely used for nonlinear optical microscopy and laser frequency conversion. Closed-form analytical solution of the nonlinear optical responses is essential for evaluating the optical responses of new materials whose optical properties are unknown a priori. A recent open-source code, SHAARP(si), can provide such closed form solutions for crystals with arbitrary symmetries, orientations, and anisotropic properties at a single interface. However, optical components are often in the form of slabs, thin films on substrates, and multilayer heterostructures with multiple reflections of both the fundamental and up to ten different SHG waves at each interface, adding significant complexity. Many approximations have therefore been employed in the existing analytical approaches, such as slowly varying approximation, weak reflection of the nonlinear polarization, transparent medium, high crystallographic symmetry, Kleinman symmetry, easy crystal orientation along a high-symmetry direction, phase matching conditions and negligible interference among nonlinear waves, which may lead to large errors in the reported material properties. To avoid these approximations, we have developed an open-source package named Second Harmonic Analysis of Anisotropic Rotational Polarimetry in Multilayers (SHAARP(ml)). The reliability and accuracy are established by experimentally benchmarking with both the SHG polarimetry and Maker fringes predicted from the package using standard materials.

physics.optics↗

arXiv2026

ZimaBlue: Evolving Generalizable World Action Models through Scalable Video Pre-training

Robotic manipulation faces a fundamental scaling challenge: robust generalization demands broad physical experience, yet action-labeled robot trajectories are expensive to collect and inherently limited in diversity. Egocentric videos offer a far more scalable source of embodied experience, capturing object interactions, contact dynamics, tool use, and long-horizon behaviors across diverse environments. The central challenge is how to convert this abundant but action-free experience into effective robot control. We introduce ZimaBlue, a scalable framework for learning generalizable World Action Models (WAMs) from large-scale video. ZimaBlue follows a three-stage training curriculum: it first performs causal embodied video pre-training on large-scale human and robot egocentric videos, then grounds the learned visual dynamics in heterogeneous robot trajectories through video-action mid-training with a unified action representation, and finally specializes the model to a target robot for deployment. To make generative WAMs practical for real-time control, ZimaBluefurther adopts an asynchronous Slow-Fast dual-system architecture, where a high-capacity Slow world model provides generalizable spatiotemporal representations and a lightweight Fast branch enables 30 Hz action prediction on NVIDIA RTX 4090. On real-robot zero-shot evaluations, scaling from target-robot data alone to over 120,000 hours of embodied video improves success from 36.1% to 77.8%. ZimaBlue further delivers strong performance across multiple benchmarks, with particularly pronounced gains on unseen tasks.

cs.CV↗

arXiv2026

SHAARP: An Open-Source Package for Analytical and Numerical Modeling of Optical Second Harmonic Generation in Anisotropic Crystals

Optical second harmonic generation is a second-order nonlinear process that combines two photons of a given frequency into a third photon at twice the frequency. Due to the symmetry constraints, it is widely used as a sensitive probe to detect broken inversion symmetry and local polar order. Analytical modeling of the electric-dipole SHG response is essential to extract fundamental properties of materials from experiments. However, complexity builds up dramatically in the analytical model when the probed crystal is of a low bulk crystal symmetry, with a low-symmetry surface orientation, exhibits absorption and dispersion, and consists of multiple interfaces. As a result, there is a largely uneven landscape in the literature on the SHG modeling of new materials, involving numerous approximations and a wide range of (in)accuracies, leading to a rather scattered dataset of reported SHG nonlinear susceptibility. Towards streamlining the reliability and accuracy of this process, we have developed an open-source package called the Second Harmonic Analysis of Anisotropic Rotational Polarimetry (SHAARP) which derives analytical solutions and performs numerical simulations of reflection SHG from a single interface for homogeneous crystals. Five key generalizations in SHG modeling are implemented, including all crystal symmetries down to triclinic, any crystal orientation, complex dielectric tensor (refractive indices) with frequency dispersion, and general polarization states of the light. SHAARP enables accurate anisotropic modeling of SHG response for a broad range of materials systems. The method is extendible to multiple interfaces. The code is free to download from https://github.com/Rui-Zu/SHAARP

physics.optics↗

arXiv2026

SUP-MIMIC: A Multi-Task Clinical Diagnosis Benchmark for Evaluating LLMs' Robustness to Contradictory Evidence

Current evaluations of large language models (LLMs) primarily focus on factual knowledge retrieval, overlooking the fundamental challenge of navigating the complex, non-bijective mappings between clinical indicators and diagnoses. Existing benchmarks fail to assess whether large language models truly possess the reasoning capability required for diagnostic ambiguity scenarios, where identical clinical presentations may correspond to different etiologies, and diagnostic convergence scenarios, where heterogeneous symptoms ultimately indicate the same disease. To address this issue, we propose SUP-MIMIC, a multi-task framework utilizing MIMIC-IV-v3.1 that comprises Basic Assessment (BA), Diagnostic Divergence Task (DDT), and Diagnostic Convergence Task (DCT). Specifically, DDT is designed to evaluate the model's "one-to-many" disambiguation capability among phenotypically similar cases, while DCT assesses the model's ability to identify "many-to-one" diagnostic patterns across different pathophysiological pathways. Comprehensive evaluation of state-of-the-art LLMs reveals substantial performance degradation on DDT and DCT compared to baseline tasks, exposing a systemic reliance on statistical shortcuts over genuine causal reasoning. Our findings further highlight a conservative bias toward "healthy" predictions, implying non-trivial risks for missed diagnoses in realistic medical settings. This work establishes a rigorous methodology for quantifying clinical reasoning robustness and provides a roadmap for enhancing the safety of language models in clinical medicine.

cs.CL↗

arXiv2026

OptiGeo: Efficient Monocular Geometry for Embodied Perception in Optically Challenging Scenes

Monocular depth estimation has achieved strong open-domain generalization, yet reliable robotic deployment remains difficult in transparent, reflective, and specular environments, where depth sensors often produce missing or biased depth. Existing methods often handle such optical failures with scene-specific preprocessing, auxiliary modules, or post-hoc fine-tuning. While effective in constrained settings, these designs increase architectural redundancy and can over-specialize general geometry models to narrow optical scenarios. We revisit this problem as a localized failure mode within base-model training and identify sensor-induced supervision bias as a key bottleneck: models inherit sensor failure patterns from biased real-depth supervision in optically challenging regions. We then introduce OptiGeo, a bias-aware training framework that rehabilitates biased real supervision using a clean-geometry teacher and residual-trimmed alignment. We redefine transparency-targeted rendering as a compact source of clean optical geometry, rather than a large domain-specific fine-tuning set. With only a small targeted rendering set, OptiGeo learns the geometric structure of transparent objects and regions, correcting local geometry distortions that real sensors cannot reliably supervise. Despite only 30M parameters, OptiGeo outperforms substantially larger 300M-scale monocular models and billion-scale multi-view baselines on transparent-scene benchmarks, while remaining competitive on general zero-shot depth and boundary sharpness. Real-world navigation cases further validate its practicality as an efficient perception module in optically challenging scenes.

cs.CV↗

arXiv2026

EmoTrace: An Emotion Trajectory-Centered Framework for Psychological Support Dialogue Generation

Using large language models (LLMs) to assist psychological counseling is an important task in the field of natural language processing. The construction of high-quality psychological support dialogue corpora serves as a critical foundation for training counseling-oriented conversational models. However, existing data generation approaches generally suffer from several limitations, including emotionally stable seekers, limited variation in emotional dynamics, and a high degree of compliance with counselors' guidance. These issues result in LLM that lack the capability to effectively respond to emotionally unstable scenarios. In addition, counselor responses are typically driven by problem-solving objectives, thereby overlooking the role of emotion-focused interaction, which are essential in psychological counseling. To address these gaps, we propose EmoTrace, a multi-turn dialogue corpus generation framework centered on modeling seekers' emotional trajectories. we construct seekers' cognitive profile and introduce a seeker module with emotional schemas and an associated activation mechanism, a counselor module, and an emotional trajectory control module, thereby enhancing the layering of the seeker's emotional expression and the counselor's targeted empathic expression. Experimental results demonstrate that the proposed method outperforms existing approaches in terms of emotional richness and empathy quality.

cs.CL↗

arXiv2026

NestedKV: Nested Memory Routing for Long-Context KV Cache Compression

Long-context language models are limited by the memory footprint of the key-value (KV) cache. Existing training-free KV compression methods usually rank tokens by one importance signal -- attention, recency, layer-wise allocation, or key distinctiveness -- which becomes brittle when useful context is globally distinctive, locally episodic, or immediately relevant. We introduce NestedKV, a key-only KV cache compression method inspired by the Continuum Memory System in Nested Learning. NestedKV maintains global, block-level, and sliding-window key anchors, scores tokens by multi-time-scale cosine anomaly, and combines the resulting rankings with a training-free outer learner using head-adaptive mixing and surprise-gated token routing. The score is paired with adaptive per-head budgets and requires no training or LLM modification. Across RULER (4k--32k), LooGLE, LongBench, LongBench-E, InfiniteBench, and MMLU-Pro on Qwen3 and Llama-3.2 models, NestedKV is strongest when the retained cache is small. On Qwen3-4B, it improves over KeyDiff by up to 19.10 points on RULER and 19.29 on LongBench at $r=0.75$; at $r=0.95$, it retains 37.32 on LongBench versus 17.55 for KeyDiff.

cs.CL↗