arXiv subjects

Yang Li

Publications and source records attributed to Yang Li.

At least 325 records · Page 18Linked to original sources

arXiv2026

Convergence in charmonium structure: light-front wave functions from basis light-front quantization and Dyson-Schwinger equations

We present a systematic comparison of charmonium light-front wave functions obtained through two complementary non-perturbative approaches: Basis Light-Front Quantization (BLFQ) and Dyson-Schwinger equations (DSE). Key observables include the charge form factor, gravitational form factors, light-cone distribution amplitudes, decay constants, and two-photon transition form factors. Despite their distinct theoretical foundations and model parameters, the predictions from BLFQ and DSE exhibit remarkable agreement across all observables. This convergence validates both frameworks for studying charmonium structure and highlights the complementary strengths of Hamiltonian-based (BLFQ) and Lagrangian-based (DSE) methods in addressing non-perturbative QCD.

hep-ph↗

arXiv2026

UltraFlwr -- An Efficient Federated Surgical Object Detection Framework

Surgical object detection in laparoscopic videos enables real-time instrument identification for workflow analysis and skills assessment, but training robust models such as You Only Look Once (YOLO) is challenged by limited data, privacy constraints, and inter-institutional variability. Federated learning (FL) enables collaborative training without sharing raw data, yet practical support for modern YOLO pipelines under heterogeneous surgical data remains limited. We present UltraFlwr, an open-source, communication-efficient, and edge-deployable framework that integrates Ultralytics YOLO with the Flower FL platform and supports native Partial Aggregation (PA) of YOLO components (backbone, neck, head). Using two public laparoscopic surgical tool detection datasets, we conduct a systematic empirical study of federated YOLO training under Independent and Identically Distributed (IID) and multiple clinically motivated heterogeneous scenarios, including differences in data curation, video length, and label availability. Results show that standard FL aggregators (e.g., FedAvg) do not consistently match centralized training per client, but reduce inter-client performance variability. Aggregating both backbone and neck components achieves performance comparable to full aggregation with lower communication costs. Also, improving within-client data consistency can benefit FL even when it increases distribution shift across clients. These findings provide practical guidance for deploying federated YOLO-based object detection in heterogeneous surgical environments. UltraFlwr is publicly available at https://github.com/KCL-BMEIS/UltraFlwr.

cs.CV↗

arXiv2026

Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs

Reinforcement learning (RL) has re-emerged as a natural approach for training interactive LLM agents in real-world environments. However, directly applying the widely used Group Relative Policy Optimization (GRPO) algorithm to multi-turn tasks exposes notable limitations, particularly in scenarios requiring long-horizon reasoning. To address these challenges, we investigate more stable and effective advantage estimation strategies, especially for multi-turn settings. We first explore Proximal Policy Optimization (PPO) as an alternative and find it to be more robust than GRPO. To further enhance PPO in multi-turn scenarios, we introduce turn-PPO, a variant that operates on a turn-level MDP formulation, as opposed to the commonly used token-level MDP. Our results on the WebShop and Sokoban datasets demonstrate the effectiveness of turn-PPO, both with and without long reasoning components.

cs.LG↗

arXiv2026

Crystal growth and characterization of a hole-doped iron-based superconductor Ba(Fe$_{0.875}$Ti$_{0.125}$)$_2$As$_2$

We report the crystal growth of a new hole-doped iron-based superconductor Ba(Fe$_{0.875}$Ti$_{0.125}$)$_2$As$_2$ by substituting Ti on the Fe site. The crystals are accidentally obtained in trying to grow Ni doped Ba$_2$Ti$_2$Fe$_2$As$_4$O. After annealing at 500 \textcelsius $ $ in vacuum for one week, superconductivity is observed with zero resistance at $T_{c0} \approx 17.5$ K, and about 20\% diamagnetic volume down to 2 K. While both the small anisotropy of superconductivity and the temperature dependence of normal state resistivity are akin to the electron doped 122-type compounds, the Hall coefficient is positive and similar to the case in hole-doped Ba$_{0.9}$K$_{0.1}$Fe$_2$As$_2$. The density functional theory calculations suggest dominated hole pockets contributed by Fe/Ti 3$d$ orbitals. Therefore, the Ba(Fe$_{1-x}$Ti$_{x}$)$_2$As$_2$ system provides a new platform to study the superconductivity with hole doping on the Fe site of iron-based superconductors.

cond-mat.supr-con↗

arXiv2026

Non-perturbative flavor asymmetry in the nucleon and deuteron: The light-front Hamiltonian effective field theory approach

We investigate non-perturbative multi-pion contributions to nucleon flavor asymmetry within the framework of Light-Front Hamiltonian Effective Field Theory (LFHEFT). Utilizing a Fock sector expansion, we systematically incorporate pionic degrees of freedom, with the nucleon-pion interactions governed by a scalar variant of chiral effective field theory. Our results demonstrate that the non-perturbatively calculated longitudinal momentum distributions exhibit significant deviations from leading-order perturbative predictions, emphasizing the importance of higher-order Fock components in describing the proton's sea quark structure. Furthermore, we demonstrate the feasibility of extending this framework to investigate nuclear effects in light nuclei, such as the deuteron. This unified approach provides a consistent basis for analyzing the interplay between intrinsic nucleon structure and nuclear modifications, potentially offering new insights into the flavor asymmetry observed in fixed-target and collider experiments.

hep-ph↗

arXiv2026

Does Motion Intensity Impair Cognition in HCI? The Critical Role of Physical Motion-Visual Target Directional Congruency

Human-computer interaction (HCI) increasingly occurs in motion-rich environments. The ability to accurately and rapidly respond to directional visual cues is critical in these contexts. How whole-body motion and individual differences affect human perception and reaction to these directional cues is therefore a key, yet an underexplored question for HCI. This study used a 6-DOF motion platform to measure task performance on a visual direction judgment task. We analyzed performance by decomposing the complex motion into two distinct components: a task-irrelevant lateral interference component and a task-aligned directional congruency component. Results indicate that increased motion intensity lengthened reaction times. This effect was primarily driven by the lateral interference component, and this detrimental impact was disproportionately amplified for individuals with high motion sickness susceptibility. Conversely, directional congruency, where motion direction matched the visual cue, improved performance for all participants. These findings suggest that motion's impact on cognition is not monolithic, and that system design for mobile HCI can be informed by strategies that actively shape motion, such as minimizing lateral interference while maximizing directional congruency.

cs.HC↗

arXiv2026

Theoretical research on low-frequency drift Alfvén waves in general Tokamak equilibria

We developed kinetic models based on general fishbone-like dispersion relations. Firstly, a general model for arbitrary magnetic configuration and ion orbit width is presented. Then, by disregarding ion orbit width and approximating the magnetic geometry as circular, we introduce a simplified model that fully incorporates circulating/trapped ion effects. Finally, by considering the limit of ions being well-circulating or deeply trapped, the results directly revert to those observed in earlier theoretical studies.

physics.plasm-ph↗

arXiv2026

Two-dimensional Intrinsic Janus Structures: Design Principle and Anomalous Nonlinear Optics

Two-dimensional Janus structures have garnered rapidly growing attention across multidisciplinary fields. However, despite extensive theoretical and experimental efforts, a principle for designing intrinsic Janus materials remains elusive. Here, we propose a first-principles alloy theory based on cluster expansion, incorporating a strong repulsive interaction of a cation-mediated anion-pair cluster and refined short-range cluster-cluster competitions, to unravel the formation mechanism of intrinsic Janus structures with a distorted 1T phase among numerous competing phases. Our theory not only explains why intrinsic Janus structures are accidentally observed in RhSeCl and BiTeI which are composed of alloyed elements from different groups, but also accurately predicts a wide range of 1T-like intrinsic Janus materials that are ready for synthesis. Intriguingly, as demonstrated in the case of RhSeCl, we reveal that intrinsic Janus materials can exhibit anomalous second-harmonic generation (SHG) with a distinct quantum geometric effect, originating from strong lattice and chemical-potential mirror asymmetry. Furthermore, a novel skin effect unexpectedly emerges in finite-thickness RhSeCl, accompanied by a hidden SHG effect within the bulk region. Our theory paves the way for the ab initio design of intrinsic Janus materials, significantly accelerating progress in Janus science.

cond-mat.mtrl-sci↗

arXiv2026

STEP3-VL-10B Technical Report

We present STEP3-VL-10B, a lightweight open-source foundation model designed to redefine the trade-off between compact efficiency and frontier-level multimodal intelligence. STEP3-VL-10B is realized through two strategic shifts: first, a unified, fully unfrozen pre-training strategy on 1.2T multimodal tokens that integrates a language-aligned Perception Encoder with a Qwen3-8B decoder to establish intrinsic vision-language synergy; and second, a scaled post-training pipeline featuring over 1k iterations of reinforcement learning. Crucially, we implement Parallel Coordinated Reasoning (PaCoRe) to scale test-time compute, allocating resources to scalable perceptual reasoning that explores and synthesizes diverse visual hypotheses. Consequently, despite its compact 10B footprint, STEP3-VL-10B rivals or surpasses models 10$\times$-20$\times$ larger (e.g., GLM-4.6V-106B, Qwen3-VL-235B) and top-tier proprietary flagships like Gemini 2.5 Pro and Seed-1.5-VL. Delivering best-in-class performance, it records 92.2% on MMBench and 80.11% on MMMU, while excelling in complex reasoning with 94.43% on AIME2025 and 75.95% on MathVision. We release the full model suite to provide the community with a powerful, efficient, and reproducible baseline.

cs.CV↗

arXiv2026

Exploiting Task Relationships in Continual Learning via Transferability-Aware Task Embeddings

Continual learning (CL) has been a critical topic in contemporary deep neural network applications, where higher levels of both forward and backward transfer are desirable for an effective CL performance. Existing CL strategies primarily focus on task models, either by regularizing model updates or by separating task-specific and shared components, while often overlooking the potential of leveraging inter-task relationships to enhance transfer. To address this gap, we propose a transferability-aware task embedding, termed H-embedding, and construct a hypernet framework under its guidance to learn task-conditioned model weights for CL tasks. Specifically, H-embedding is derived from an information theoretic measure of transferability and is designed to be online and easy to compute. Our method is also characterized by notable practicality, requiring only the storage of a low-dimensional task embedding per task and supporting efficient end-to-end training. Extensive evaluations on benchmarks including CIFAR-100, ImageNet-R, and DomainNet show that our framework performs prominently compared to various baseline and SOTA approaches, demonstrating strong potential in capturing and utilizing intrinsic task relationships. Our code is publicly available at https://github.com/viki760/Hembedding_Guided_Hypernet.

cs.LG↗

arXiv2026

Video-MSR: Benchmarking Multi-hop Spatial Reasoning Capabilities of MLLMs

Spatial reasoning has emerged as a critical capability for Multimodal Large Language Models (MLLMs), drawing increasing attention and rapid advancement. However, existing benchmarks primarily focus on single-step perception-to-judgment tasks, leaving scenarios requiring complex visual-spatial logical chains significantly underexplored. To bridge this gap, we introduce Video-MSR, the first benchmark specifically designed to evaluate Multi-hop Spatial Reasoning (MSR) in dynamic video scenarios. Video-MSR systematically probes MSR capabilities through four distinct tasks: Constrained Localization, Chain-based Reference Retrieval, Route Planning, and Counterfactual Physical Deduction. Our benchmark comprises 3,052 high-quality video instances with 4,993 question-answer pairs, constructed via a scalable, visually-grounded pipeline combining advanced model generation with rigorous human verification. Through a comprehensive evaluation of 20 state-of-the-art MLLMs, we uncover significant limitations, revealing that while models demonstrate proficiency in surface-level perception, they exhibit distinct performance drops in MSR tasks, frequently suffering from spatial disorientation and hallucination during multi-step deductions. To mitigate these shortcomings and empower models with stronger MSR capabilities, we further curate MSR-9K, a specialized instruction-tuning dataset, and fine-tune Qwen-VL, achieving a +7.82% absolute improvement on Video-MSR. Our results underscore the efficacy of multi-hop spatial instruction data and establish Video-MSR as a vital foundation for future research. The code and data will be available at https://github.com/ruiz-nju/Video-MSR.

cs.CV↗

arXiv2026

Time-dependent Hole States in Multiconfigurational Time-Dependent Hartree-Fock Approaches: A Time-Domain Generalization of Extended Koopmans' Theorem

We introduce a framework for resolving electron-hole dynamics within wavefunction-based multiconfigurational time-dependent Hartree-Fock (MCTDHF) theory. Central to this framework is a time-domain generalization of the extended Koopmans' theorem, which rigorously defines time-dependent hole states through single-electron removal. From this foundation, we prove the existence of exact equations of motion for time-dependent Dyson orbitals, enabling instantaneous construction of photofragments' reduced density matrices. The formalism further yields a systematic procedure to extract hole-resolved observables, such as channel-resolved photoelectron momentum distributions, directly from time-dependent \textit{ab initio} wavefunctions. As a demonstration, we employ an attosecond $ω-2ω$ laser strategy to control hole dynamics, thereby resolving a long-standing challenge in MCTDHF simulations. This advance opens a pathway for exploring correlated multielectron dynamics in atoms and molecules under ultrafast laser fields.

physics.atom-ph↗

arXiv2026

Decoupled interband pairing in a bilayer iron-based superconductor evidenced by ultrahigh-resolution ARPES

We present direct experimental evidence of a weakly coupled multiband superconducting state in the bilayer iron-based superconductor ACa$_2$Fe$_4$As$_4$F$_2$ (A = K, Cs) via ultrahigh-resolution angle-resolved photoemission spectroscopy (ARPES). Remarkably, the K-containing compound exhibits two distinct transition temperatures, corresponding to two separate sets of bilayer-split bands, as evidenced by temperature-dependent superconducting gap and spectral weight near the Fermi energy, while its Cs counterpart displays conventional single transition behavior. These experimental observations are well described by the weakly coupled two-band model of Eilenberger theory, which identifies suppressed interband pairing interactions between the bilayer-split bands as the key mechanism. By exploring quantum phenomena in the weak-coupling limit within a multiband system, our findings pave the way for engineering exotic superconductivity via band-selective pairing control.

cond-mat.supr-con↗

arXiv2026

Enhancing Rare Codes via Probability-Biased Directed Graph Attention for Long-Tail ICD Coding

Automated international classification of diseases (ICD) coding aims to assign multiple disease codes to clinical documents and plays a critical role in healthcare informatics. However, its performance is hindered by the extreme long-tail distribution of the ICD ontology, where a few common codes dominate while thousands of rare codes have very few examples. To address this issue, we propose a Probability-Biased Directed Graph Attention model (ProBias) that partitions codes into common and rare sets and allows information to flow only from common to rare codes. Edge weights are determined by conditional co-occurrence probabilities, which guide the attention mechanism to enrich rare-code representations with clinically related signals. To provide higher-quality semantic representations as model inputs, we further employ large language models to generate enriched textual descriptions for ICD codes, offering external clinical context that complements statistical co-occurrence signals. Applied to automated ICD coding, our approach significantly improves the representation and prediction of rare codes, achieving state-of-the-art performance on three benchmark datasets. In particular, we observe substantial gains in macro-averaged F1 score, a key metric for long-tail classification.

cs.LG↗

arXiv2026

Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation

Human-like dexterous hands with multiple fingers offer human-level manipulation capabilities, but training control policies that can directly deploy on real hardware remains difficult due to contact-rich physics and imperfect actuation. We close this gap with a practical sim-to-real reinforcement learning (RL) framework that utilizes dense tactile feedback combined with joint torque sensing to explicitly regulate physical interactions. To enable effective sim-to-real transfer, we introduce (i) a computationally fast tactile simulation that computes distances between dense virtual tactile units and the object via parallel forward kinematics, providing high-rate, high-resolution touch signals needed by RL; (ii) a current-to-torque calibration that eliminates the need for torque sensors on dexterous hands by mapping motor current to joint torque; and (iii) actuator dynamics modeling to bridge the actuation gaps with randomization of non-ideal effects such as backlash, torque-speed saturation. Using an asymmetric actor-critic PPO pipeline trained entirely in simulation, our policies deploy directly to a five-finger hand. The resulting policies demonstrated two essential skills: (1) command-based, controllable grasp force tracking, and (2) reorientation of objects in the hand, both of which were robustly executed without fine-tuning on the robot. By combining tactile and torque in the observation space with effective sensing/actuation modeling, our system provides a practical solution to achieve reliable dexterous manipulation. To our knowledge, this is the first demonstration of controllable grasping on a multi-finger dexterous hand trained entirely in simulation and transferred zero-shot on real hardware.

cs.RO↗

arXiv2026

Unique Decoding of Extended Subcodes of GRS Codes Using Error-Correcting Pairs

Extended Han-Zhang codes are a class of linear codes where each code is either a non-generalized Reed-Solomon (non-GRS) maximum distance separable (MDS) code or a near MDS (NMDS) code. They have important applications in communication, cryptography, and storage systems. While many algebraic properties and explicit constructions of extended Han-Zhang codes have been well studied in the literature, their decoding has been unexplored. In this paper, we focus on their decoding problems in terms of $\ell$-error-correcting pairs ($\ell$-ECPs) and deep holes. On the one hand, we determine the existence and specific forms of their $\ell$-ECPs, and further present an explicit decoding algorithm for extended Han-Zhang codes based on these $\ell$-ECPs, which can correct up to $\ell$ errors in polynomial time, with $\ell$ about half of the minimum distance. On the other hand, we determine the covering radius of extended Han-Zhang codes and characterize two classes of their deep holes, which are closely related to the maximum-likelihood decoding method. By employing these deep holes, we also construct more non-GRS MDS codes with larger lengths and dimensions, and discuss the monomial equivalence between them and the well-known Roth-Lempel codes. Some concrete examples are also given to support these results.

cs.IT↗

arXiv2026

AT$^2$PO: Agentic Turn-based Policy Optimization via Tree Search

LLM agents have emerged as powerful systems for tackling multi-turn tasks by interleaving internal reasoning and external tool interactions. Agentic Reinforcement Learning has recently drawn significant research attention as a critical post-training paradigm to further refine these capabilities. In this paper, we present AT$^2$PO (Agentic Turn-based Policy Optimization via Tree Search), a unified framework for multi-turn agentic RL that addresses three core challenges: limited exploration diversity, sparse credit assignment, and misaligned policy optimization. AT$^2$PO introduces a turn-level tree structure that jointly enables Entropy-Guided Tree Expansion for strategic exploration and Turn-wise Credit Assignment for fine-grained reward propagation from sparse outcomes. Complementing this, we propose Agentic Turn-based Policy Optimization, a turn-level learning objective that aligns policy updates with the natural decision granularity of agentic interactions. ATPO is orthogonal to tree search and can be readily integrated into any multi-turn RL pipeline. Experiments across seven benchmarks demonstrate consistent improvements over the state-of-the-art baseline by up to 1.84 percentage points in average, with ablation studies validating the effectiveness of each component. Our code is available at https://github.com/zzfoutofspace/ATPO.

cs.AI↗

arXiv2026

Decide Then Retrieve: A Training-Free Framework with Uncertainty-Guided Triggering and Dual-Path Retrieval

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge, but existing approaches indiscriminately trigger retrieval and rely on single-path evidence construction, often introducing noise and limiting performance gains. In this work, we propose Decide Then Retrieve (DTR), a training-free framework that adaptively determines when retrieval is necessary and how external information should be selected. DTR leverages generation uncertainty to guide retrieval triggering and introduces a dual-path retrieval mechanism with adaptive information selection to better handle sparse and ambiguous queries. Extensive experiments across five open-domain QA benchmarks, multiple model scales, and different retrievers demonstrate that DTR consistently improves EM and F1 over standard RAG and strong retrieval-enhanced baselines, while reducing unnecessary retrievals. The code and data used in this paper are available at https://github.com/ChenWangHKU/DTR.

cs.CL↗