arXiv subjects

Yi Yang

Publications and source records attributed to Yi Yang.

At least 91 records · Page 5Linked to original sources

arXiv2026

OSCAgent: Accelerating the Discovery of Organic Solar Cells with LLM Agents

Organic solar cells (OSCs) hold great promise for sustainable energy, but discovering high-performance materials is time-consuming and costly. Existing molecular generation methods can aid the design of OSC molecules, but they are mostly confined to optimizing known backbones and lack effective use of domain-specific chemical knowledge, often leading to unrealistic candidates. In this paper, we introduce OSCAgent, a multi-agent framework for OSC molecular discovery that unifies retrieval-augmented design, molecular generation, and systematic evaluation into a continuously improving pipeline, without requiring additional human intervention. OSCAgent comprises three collaborative agents. The Planner retrieves knowledge from literature-curated molecules and prior candidates to guide design directions. The Generator proposes new OSC acceptors aligned with these plans. The Experimenter performs comprehensive evaluation of candidate molecules and provides feedback for refinement. Experiments show that OSCAgent produces chemically valid, synthetically accessible OSC molecules and achieves superior predicted performance compared to both traditional and large language model (LLM)-only baselines. Representative results demonstrate that some candidates achieve predicted efficiencies approaching 18\%. The code will be publicly available.

cs.CE↗

arXiv2026

Cross-Environment Diagnostics for New Physics in Proton-Proton and Heavy-Ion Collisions

Proton-proton and heavy-ion measurements are usually interpreted within separate theoretical and experimental frameworks, even when the same reconstructed final state is measured and the $pp$ result provides or constrains the elementary reference for nuclear observables. This separation creates a potential blind spot: an omitted contribution can be absorbed into production or signal-model parameters in $pp$ collisions and then be reinterpreted through independent nuclear-medium parameters in heavy-ion collisions. We propose cross-environment closure as a complementary search principle. Conventional parameters remain specific to each collision system, while one candidate new-physics contribution must remain coherent across systems and observables. Quarkonium is used as a controlled illustration rather than as the definition of the method. A near-degenerate dimuon component is shown to be absorbable into a retuned $pp$ spectrum and a single-peak mass fit, while the same $p_T$-dependent contribution propagates into representative $R_{pA}$, $R_{AA}$, and $v_2$ relations. These quantities are consistency observables, not assumed new-physics baselines. The numerical examples are stress tests, not claims about an allowed particle or an anomaly in current data. The main result is a transferable diagnostic principle, illustrated here with quarkonium rather than formulated as a universal statistical framework.

hep-ph↗

arXiv2026

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework

As a major contributor to carbon emissions, the decarbonization of power systems has garnered significant societal attention. Nodal carbon intensity (NCI), a critical factor in carbon-oriented demand response, has traditionally been determined through ex-post calculations. However, this ex-post approach introduces latency in low-carbon dispatch. To address this, this paper presents a proactive ex-ante spatial-temporal carbon response framework. At its core, we develop a novel deep learning-based hierarchical design, enhanced by a dual-stage attention mechanism and a large language model (LLM)-based multi-agent cooperation system, to accurately forecast day-ahead NCI. This design effectively mitigates the impact of renewable energy uncertainty and enhances predictive resilience. On the demand side, the framework proposes a spatial-temporal carbon scheduling model that integrates geographically dispatchable loads (GDLs), including mobile energy storage systems (MESSs) and distributed data centers (DDCs). Leveraging high-accuracy day-ahead NCI predictions, the framework can effectively reduce system emissions by quickly responding to carbon intensity fluctuations. The proposed framework is tested on the modified IEEE 33-bus system. According to the simulation results, the impacts of proposed framework on dispatching latency and emission outcomes are analyzed. The results demonstrate that under a one-hour reduction in carbon scheduling latency, the proposed model and methodology can achieve over 30% emission reduction. This research breaks through the limitations of passive carbon accounting, advancing toward proactive carbon management. It offers an intelligent solution that accelerates the transition to cleaner power systems while directly supporting sustainable production goals.

eess.SY↗

arXiv2026

JWST Spectra Indicate a Large Mass of Postshock Dust Formed by SN 2010jl

We present new JWST mid-IR spectra and ground-based optical spectra of the lingering source at the position of SN 2010jl, which was a relatively nearby superluminous Type IIn supernova having strong interaction with circumstellar material (CSM). Early-time data showed evidence of dust, interpreted as either pre-existing CSM dust, or as newly formed dust in the SN ejecta and post-shock region. At 13 yr post explosion, JWST reveals a strong MIR excess from warm dust, with broad features at 10-15 $μ$m. Our analysis reveals a minimum dust mass of $>$0.11 $M_{\odot}$, and a more likely value of 0.2 $M_{\odot}$ or more because the dust is optically thick. This is among the largest masses of SN-produced dust yet measured without far-IR/submm data, and greatly exceeds SN 2010jl's dust mass inferred around 2-3 yr post-explosion. Ground-based optical spectra confirm the presence of a young massive cluster at the SN position, and confirm that blueshifted line profiles persisting until the latest epochs arise from dust formed in the post-shock region. The warmest dust emitting in the MIR is likely to be the same post-shock dust causing the blueshift. The JWST spectrum also reveals silicate absorption, which may arise from cool SN ejecta dust along the line of sight to the receding shock. The large mass of post-shock dust in SN 2010jl suggests that strong CSM interaction promotes efficient dust production, where the new post-shock dust will survive. If strongly interacting SNe are common in the early Universe, this may contribute significantly to dust seen in infant galaxies.

astro-ph.HE↗

arXiv2026

ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding

Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundamentally a vision-centric challenge: models must absorb knowledge from heterogeneous 2D and 3D medical images, and evaluation protocols must align with radiologists' clinical practice and provide an accurate, fine-grained and factualness-driven assessment. In this paper, we introduce ClinFusion, a vision-centric MLLM designed for holistic medical understanding that systematically addresses these limitations. We propose a compositional and cascaded vision encoder architecture featuring a Cascade Spatial-Aware Locality Fusion operator that unifies diverse 2D and native 3D medical image understanding within a fused encoder. We further introduce a vision-grounded evaluation framework, including MedIF-Bench for instruction-following assessment and a region-of-interest-grounded method for clinically aligned and factualness-driven report generation evaluation. We show that ClinFusion sets a new state-of-the-art across a comprehensive suite of 2D and 3D multimodal medical benchmarks---spanning visual question answering, report generation, and instruction following---as well as textual medical tasks, outperforming leading open-source medical MLLMs (\textit{e.g.}, Hulu-Med, Lingshu) on 20 out of 24 benchmarks and demonstrating multimodal capabilities better than powerful proprietary models such as GPT-5.2 and Gemini-3-Flash on 13 out of 16 benchmarks, and can be further augmented with agentic tool use for retrieval-augmented and tool-assisted clinical workflows. A blinded evaluation by board-certified radiologists confirms that ClinFusion produces the highest-ranked reports, and validates our RoI-grounded metric as achieving the strongest correlation with expert judgment among all automatic evaluation metrics examined.

cs.CV↗

arXiv2026

SN 2022erq: A Superluminous Thermonuclear Supernova with Escalating Preexplosion Mass Loss

We present a photometric and spectroscopic study of the superluminous Type Ia supernova SN 2022erq. Its early spectra, dominated by iron-group elements with weak intermediate-mass features, might indicate highly efficient nuclear burning, broadly similar to that inferred for some overluminous SNe Ia. The rapid emergence and persistence of narrow Balmer emission lines superposed on this iron-rich spectrum provide clear evidence of long-lived interaction with a hydrogen-rich circumstellar medium (CSM), establishing SN 2022erq as a member of the rare Ia-CSM class. SN 2022erq reached a peak bolometric luminosity of about 8 x 10^43 erg/s and exhibited an exceptionally slow post-peak decline, indicating that its light curve is dominated by long-duration ejecta-CSM interaction. By combining H-alpha diagnostics with bolometric light-curve modeling, we reconstruct the pre-explosion mass-loss history of the progenitor. The mass-loss rate escalated by one order of magnitude over the final decades, rising from about 0.04 to about 0.6 solar masses per year. This surge produced a massive, extended CSM shell of about 3 solar masses out to about 3.5 x 10^16 cm. The young stellar environment (about 100 Myr) together with this substantial, extensive CSM points to a progenitor system consisting of a white dwarf and an intermediate-mass companion that underwent increasing mass loss prior to explosion.

astro-ph.HE↗

arXiv2026

Engineering two-body interaction for the Moore-Read State

Engineering interactions that stabilize non-Abelian fractional quantum Hall phases is a central challenge in strongly correlated topological matter and quantum simulation. We introduce a differentiable framework for inverse Hamiltonian design, in which Haldane pseudopotentials are optimized by gradient-based exact diagonalization to stabilize target fractional quantum Hall phases. In spherical geometry, the Haldane pseudopotentials are treated as variational parameters and optimized in a JAX-based exact-diagonalization framework. By directly maximizing the overlap between the many-body ground state and the Moore-Read state, we obtain a robust pseudopotential profile that has Pfaffian overlaps exceeding $99\%$ for systems up to $N_e=12$, substantially improving over conventional Coulomb interactions. Analyses of the neutral excitation spectrum and orbital entanglement spectrum further confirm that the optimized interaction stabilizes the Pfaffian topological phase. Our results demonstrate that essential features of the three-body Pfaffian parent Hamiltonian can be effectively encoded in a suitably designed two-body interaction. Furthermore, they identify a nearly universal exponentially decaying pseudopotential profile that stabilizes the Pfaffian phase and establishes a general framework toward engineering non-Abelian topological order in quantum simulation.

cond-mat.str-el↗

arXiv2026

Gravity with a Non-Dynamical Metric in a Yang-Mills Framework

General relativity assigns the spacetime metric two logically distinct roles: it defines physical measurements and it carries the propagating degrees of freedom of gravity. We investigate whether these roles must be assigned to the same field. We formulate a classical Yang-Mills framework in which the metric retains its operational role in defining distances, time intervals, causal structure, index contractions, and spacetime volume, but carries no kinetic term and no independent propagating degrees of freedom. The metric is nevertheless varied as an auxiliary field, producing an algebraic consistency condition rather than a propagation equation. The gravity dynamics are instead assigned to a local GL(4,R) Yang--Mills connection with sixteen gauge components. An explicit dictionary relates the Yang-Mills gauge potential to a connection-like geometric variable, under which the field strength is rewritten in curvature form. The distinctive feature of the construction is therefore not the use of GL(4,R) or a Yang-Mills-type gravitational action by themselves, but the variational hierarchy in which a physical measuring metric is non-propagating and algebraically constrained while the connection carries the dynamics. We clarify the relation of this framework to Poincaré gauge theory, metric-affine and Palatini formulations, pure-connection gravity, teleparallel approaches, metric quadratic gravity, and the historical Stephenson-Kilmister-Yang/Fairchild sector. In the restricted torsionless, metric-compatible sector, Ricci-flat geometries including the Schwarzschild metric remain admissible exact solutions, while additional branches are retained only as formal sectors requiring separate phenomenological assessment.

physics.gen-ph↗

arXiv2026

Supercurrent effect in a charge density wave intertwined superconductor

The energy-momentum (E-k) dispersion of quasiparticles constitutes a fundamental concept in condensed matter systems. The ability to modify the E-k dispersion, exemplified by supercurrent-induced Doppler shifts of Bogoliubov quasiparticle spectra in superconductors, enables manipulation of various emergent quantum properties. However, investigations into the supercurrent effect on superconductors intertwined with charge orders remain scarce. Here, we report that the Meissner current, generated by the diamagnetic response to an applied in-plane magnetic field, can tailor Bogoliubov quasiparticle excitations at the precursor charge density wave (CDW) vectors. Our scanning tunneling spectroscopic imaging reveals a field-driven symmetry breaking of CDW modulations, specifically a C3v-to-Cs transition, in superconducting NbSe2. Model calculations suggest that the observed anisotropy originates from a selective Doppler-shift-induced E-k dispersion reconstruction. Furthermore, altering the field direction enables on-demand tuning of anisotropic CDW modulations and visualization of their momentum-space distribution. These results highlight a novel mechanism for controlling emergent electronic phases through momentum-space engineering.

cond-mat.supr-con↗

arXiv2026

Characterization of the Scattered Light Noise in KAGRA Interferometer

In a gravitational wave (GW) telescope, non-stationary noise can prevent stable interferometer operation and limit the GW analyses. Among the non-stationary noise, we focus on the scattered light noise. It fluctuates across a wide frequency band within a short time. Therefore, the scattered light noise can mimic the GW signal, increase the false alarm rate, or bias the parameter estimation results. In this work, we propose a new analysis method, named f-cluster, to characterize these features without prior assumptions about the scattering source. The scattered light noise shows characteristic arch-shaped features in time-frequency maps. Therefore, the algorithm extracts the periodicity of the arch shapes from the spectrogram. The algorithm detects the occurrence times and periodicity of excess-power regions in the spectrogram to estimate the oscillation frequency of the scattering object. To demonstrate the algorithm, we applied this method to KAGRA data from April 1 to 30, 2025, and successfully identified a scattering frequency of $f_{sc} = 0.432$ Hz. Furthermore, we found a strong correlation between the occurrence rate of the scattered light noise and the amplitude of ground motion in the $0.3-1.0$ Hz band with a Pearson correlation coefficient of 0.82. This correlation showed that the occurrence of scattered light noise can be forecast from the seismic motion. We confirmed that our algorithm identifies the frequency of the scattered light noise and its occurrence time, without making assumptions about the scattering object, thereby contributing to the characterization of the scattered light noise in real data. Our algorithm provides information that leads to further investigation into the noise hunting to specify the source of scattered light noise.

gr-qc↗

arXiv2026

CRB-Driven Beamforming and Trajectory Optimization for UAV-assisted ISAC System

In this paper, we study an unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) system, where a UAV enhances the sensing capability of a base station (BS) towards a target while ensuring reliable communication towards a downlink user. This architecture is practically attractive for future wireless networks due to the UAV's controllable mobility and adaptive sensing coverage in wireless environments. The sensing performance is characterized by the average Cramér-Rao bound (CRB), which quantifies the minimum variance of the unbiased angle-of-arrival estimation. To enhance the sensing performance, the UAV trajectory and beamforming parameters are jointly optimized under power and mobility constraints, while satisfying communication requirements to the downlink user. To address the resulting non-convex problem, we employ null-space projection for beamforming design and adopt deep reinforcement learning for the trajectory optimization over a discrete-time scale. In each time slot, beamforming is optimized based on the channel state information to improve CRB performance while mitigating interference between the BS and the communication user. Simulation results demonstrate that the proposed method significantly reduces the time-averaged CRB by over 10%, compared with the ISAC system without UAV assistance, and also achieves a higher sensing accuracy than both the fixed-UAV-trajectory and the maximum-ratio-transmission-based beamforming benchmarks.

cs.IT↗

arXiv2026

Quasinormal modes of Kerr-like black bounce spacetime

We investigate the quasinormal mode (QNM) spectrum of a Kerr-like black-bounce spacetime under massive scalar-field perturbations. Starting from the Kerr-like deformation of the Simpson--Visser black-bounce geometry, we derive the corresponding radial and angular equations and obtain the effective potential governing scalar perturbations. The QNM frequencies are computed by means of the Pöschl--Teller potential approximation and the semi-analytic WKB method (up to sixth order), and we demonstrate reasonable agreement between these two approaches. We then analyze in detail how the QNM spectrum depends on the spin parameter $a$, the bounce parameter $p$ that interpolates between black-hole and wormhole geometries, and the scalar-field mass $μ$. Our results show that increasing the spin parameter $a$ raises the oscillation frequency, while increasing the bounce parameter $p$ lowers it, and in both cases the damping rate decreases. Moreover, the mass of the scalar field has a non-negligible impact on the ringdown spectrum. These features suggest that rotating black-bounce geometries may leave distinct imprints in the ringdown phase of gravitational-wave signals, and motivate future studies of echoes and parameter estimation in the context of present and upcoming detectors.

gr-qc↗

arXiv2026

Oval-shaped resonance distortion as a signature of quasiparticle heating effect in a niobium superconducting resonator

We investigate the nonlinear behavior of a superconducting microwave resonator subjected to a dissipative mechanism where the associated quality factor (Q factor) decreases with increasing dissipated power, leading to a dissipative feedback effect. By modifying the Rothwarf-Taylor equations, we establish a macroscopic quasiparticle heating (QPH) model that directly links the quality factor to the microwave readout power. The key finding is the identification of a distinctive oval-shaped distortion in the resonance circle in the complex plane. This distortion serves as a practical experimental signature for identifying the readout power regime in which QPH dominates the loss, under conditions where other nonlinear mechanisms are sufficiently weak. To validate the model, we design and fabricate a niobium (Nb) half-wavelength coplanar waveguide (CPW) resonator and conduct systematic bath temperature and readout power sweeps. The model provides a well fit to the observed oval-shaped resonance circle distortion across a wide range of operating conditions, confirming the QPH mechanism as the primary source of the dissipative non-linearity in the parameter space investigated.

cond-mat.supr-con↗

arXiv2026

FUSAR-R1: A Large-Scale Reasoning Model for Intelligent Interpretation of SAR Images

In recent years, large-scale vision-language models have been driving a paradigm shift in intelligent remote sensing image interpretation. By incorporating textual semantic information, the cognitive expression, semantic understanding, and human-computer interaction capabilities of interpretation models have been significantly improved, achieving initial progress in the field of Synthetic Aperture Radar (SAR) image interpretation. However, SAR images are affected by factors such as coherent imaging mechanisms, complex scattering characteristics, speckle noise interference, and target-background coupling, resulting in complex and variable image features with significant uncertainties and specializations. Existing SAR vision-language models do not yet possess the step-by-step analysis, logical judgment, and self-correction capabilities of human experts, making it difficult to support reliable intelligent interpretation in complex scenarios. To address this issue, this paper proposes a large-scale reasoning model, FUSAR-R1, for intelligent interpretation of SAR images. The model first constructs explicit chain-of-thought reasoning data by simulating the interpretation process of human experts and uses this data to guide instruction learning, thereby endowing the model with basic reasoning capabilities. Subsequently, a reinforcement learning strategy is introduced to optimize the model's outputs based on inference results, enabling self-correction and more reliable reasoning. Experimental results demonstrate that FUSAR-R1 consistently outperforms existing multimodal large-scale models across various SAR interpretation tasks, including target detection, target counting and classification, and land-cover category recognition.

cs.AI↗

arXiv2026

Clarify Before Executing: A Self-Evolving Agent for Resolving Intent Asymmetry in 3D Tool Orchestration

A fundamental intent asymmetry plagues modern 3D asset creation: while state-of-the-art 3D toolchains demand precise, executable parameters, ordinary users typically provide vague, underspecified instructions. Current 3D agents treat this ambiguity as noise, defaulting to blind execution under a single-turn assumption. To address this limitation, we introduce CLARE, a clarification-aware and evolutionary 3D agent that treats intent asymmetry not as an execution error, but as an opportunity for strategic dialogue. By decoupling the generation pipeline into four specialized cognitive roles, CLARE intercepts and resolves underspecified instructions before invoking computationally expensive 3D tools to seamlessly execute tasks across five diverse domains: text-to-3D generation, single-view reconstruction, multi-view reconstruction, point cloud editing, and post-processing. Crucially, rather than relying on rigid manual rules, CLARE self-evolves its clarification policy via simulated multi-turn interactions. By optimizing a Multi-turn Reward, the agent internalizes the delicate balance between interaction efficiency and task completion. To rigorously test this, we construct 3D-Clarify, a comprehensive benchmark comprising 620 interaction scenarios with systematically injected ambiguity, missing information, and mistaken details. CLARE achieves state-of-the-art performance, with 60.40% and 43.34% success rates on single-step and multi-step tasks, respectively, more than doubling existing baselines. Both quantitative and qualitative results demonstrate that proactive clarification is the missing key to robust 3D execution. Code is available at https://github.com/xyzhu1225/CLARE.

cs.CV↗

arXiv2026

Ultraviolet to Infrared Spectroscopy of the Type Ibn SN 2023tsz Suggests a Lower-mass Progenitor

Type Ibn supernovae are stripped-envelope explosions whose spectra indicate interaction with dense, helium-rich and hydrogen-poor circumstellar material (CSM), making them important probes of late-stage mass loss and progenitor stripping. We present extensive ultraviolet-to-near-infrared spectrophotometry of the Type Ibn SN 2023tsz, including two epochs of HST/STIS ultraviolet (UV) spectroscopy and ground-based optical and near-infrared follow-up observations. The spectra are dominated by intermediate-width emission lines at all phases after maximum light, suggesting that much of the luminosity originates in a cold dense shell (CDS) formed by interaction between the ejecta and CSM. We compare the observations to one-dimensional non-local-thermodynamic-equilibrium radiative-transfer models of a helium-star explosion with a mass of $4 M_{\odot}$ at the onset of helium burning. The models reproduce the strong optical and near-infrared He I lines and require an added X-ray irradiation field to match the highly ionized UV features. The spectra are best reproduced by models with an X-ray irradiation power of $L_X \approx 10^8 L_{\odot}$, with the preferred models favoring CDS radii of order $(1.5$--$2) \times 10^{15}$ cm, velocities of $\sim 5 \times 10^7$ cm s$^{-1}$, and interaction powers of a few times $10^{42}$ erg s$^{-1}$. In the optical, the preferred models shift from higher interaction power and smaller radii at early times to lower power and larger radii at later times. These results add to the growing evidence that at least some SNe Ibn arise from lower-mass helium stars whose final evolution is shaped by binary interaction.

astro-ph.HE↗

arXiv2026

Universal thermodynamic topological classes of the charged dRGT black string

In this study, we explore universal thermodynamic topological classes of charged dRGT black string within both the canonical ensemble and grand canonical ensemble frameworks, and further analyze its asymptotic behavior under limiting parameter regimes. We demonstrate that, while the outermost large black string branch remains thermodynamically stable in both ensembles, the innermost small black string branch exhibits distinctly different stability properties: it is stable in the canonical ensemble but becomes thermodynamically unstable in the grand canonical ensemble, corresponding to the $W^{1+}$ and $W^{0-}$ topological categories, respectively. Furthermore, the local thermodynamic stability of the charged dRGT black string is investigated through the behavior of the heat capacity. These findings demonstrate that the selection of thermodynamic ensemble has a significant influence on the thermodynamic configuration of the charged dRGT black string. In the limit where gravitational effects are neglected, the charge contribution does not modify the underlying topological classification. This implies that the coupling between dRGT massive gravity and the electromagnetic sector is essential for the emergence of nontrivial thermodynamic topology. These results contribute to a deeper understanding of the black string thermodynamics and provide a novel theoretical basis for exploring the basic properties of quantum gravity.

gr-qc↗

arXiv2026

Large language model agents accelerate inverse design of metal-organic frameworks for gas separation

Metal-organic frameworks (MOFs) offer a highly modular platform for adsorptive gas separation, yet their vast reticular design space makes inverse design difficult under simultaneous constraints of chemical validity, separation performance, and structural diversity. Here, we present LEMO Agent, a large-language-model agent framework for closed-loop inverse design of gas-separation MOFs in MOFid space. LEMO Agent couples language-based candidate generation with MOFid standardization, explicit validity checking, Transformer-based property prediction, structured design memory, and multi-island exploration. Through iterative generate--validate--evaluate--remember cycles, the agent uses feedback from both successful and failed candidates to guide chemically constrained search across linker, metal, and topology choices. We evaluate LEMO Agent on CH$_4$/N$_2$ and CO$_2$/N$_2$ separation tasks. Compared with representative generative, optimization, and agentic baselines, LEMO Agent enriches high-performing candidates, improves predicted separation performance, and maintains broad chemical and topological diversity. Selected candidates are further reconstructed, evaluated by GCMC simulations, and passed through an experimental down-selection workflow based on chemical feasibility and ligand purchasability, leading to initial wet-lab synthesis and SEM characterization. These results demonstrate that large language model agents can serve as interpretable and scalable design engines for accelerating MOF discovery beyond conventional fixed-library screening.

cs.AI↗