arXiv subjects

Zheng Li

Publications and source records attributed to Zheng Li.

At least 127 records · Page 7Linked to original sources

arXiv2026

Long-range orbital transport and inverse orbital Hall effect in Co/Ru-based terahertz emitters

The utilization of terahertz (THz) emission spectroscopy in femtosecond photoexcited spintronic heterostructures has emerged as a versatile tool for investigating ultrafast spin-transport in a noncontact and non-invasive manner. However, the investigation of ultrafast orbital-transport is still in the primitive stage. Here, we experimentally demonstrate the orbital-to-charge current conversion in Co/Ru heterostructures. Time-domain measurements reveal delayed and broadened terahertz waveforms with increasing Ru thickness, consistent with long-range orbital transport. In Co/Pt/Ru trilayers, the terahertz emission is further enhanced through constructive interference between the inverse spin Hall effect (ISHE) in Pt and inverse orbital Hall effect (IOHE) in Ru, while reversed stack structures show suppressed output. Ferromagnetic resonance (FMR) measurements reveal a strong correlation between damping and THz amplitude, highlighting efficient angular momentum conversion. These results position Co/Ru as a promising orbitronic platform for tunable ultrafast THz emission. Our results not only strengthen the physical mechanism of condensed matter physics but also pave the way for designing promising spin-orbitronic devices and terahertz emitters.

cond-mat.mtrl-sci↗

arXiv2026

Boosting Maximum Entropy Reinforcement Learning via One-Step Flow Matching

Diffusion policies are expressive yet incur high inference latency. Flow Matching (FM) enables one-step generation, but integrating it into Maximum Entropy Reinforcement Learning (MaxEnt RL) is challenging: the optimal policy is an intractable energy-based distribution, and the efficient log-likelihood estimation required to balance exploration and exploitation suffers from severe discretization bias. We propose \textbf{F}low-based \textbf{L}og-likelihood-\textbf{A}ware \textbf{M}aximum \textbf{E}ntropy RL (\textbf{FLAME}), a principled framework that addresses these challenges. First, we derive a Q-Reweighted FM objective that bypasses partition function estimation via importance reweighting. Second, we design a decoupled entropy estimator that rigorously corrects bias, which enables efficient exploration and brings the policy closer to the optimal MaxEnt policy. Third, we integrate the MeanFlow formulation to achieve expressive and efficient one-step control. Empirical results on MuJoCo show that FLAME outperforms Gaussian baselines and matches multi-step diffusion policies with significantly lower inference cost. Code is available at https://github.com/lzqw/FLAME.

cs.LG↗

arXiv2026

TriSpec: Ternary Speculative Decoding via Lightweight Proxy Verification

Inference efficiency in Large Language Models (LLMs) is fundamentally limited by their serial, autoregressive generation, especially as reasoning becomes a key capability and response sequences grow longer. Speculative decoding (SD) offers a powerful solution, providing significant speed-ups through its lightweight drafting and parallel verification mechanism. While existing work has nearly saturated improvements in draft effectiveness and efficiency, this paper advances SD from a new yet critical perspective: the verification cost. We propose TriSpec, a novel ternary SD framework that, at its core, introduces a lightweight proxy to significantly reduce computational cost by approving easily verifiable draft sequences and engaging the full target model only when encountering uncertain tokens. TriSpec can be integrated with state-of-the-art SD methods like EAGLE-3 to further reduce verification costs, achieving greater acceleration. Extensive experiments on the Qwen3 and DeepSeek-R1-Distill-Qwen/LLaMA families show that TriSpec achieves up to 35\% speedup over standard SD, with up to 50\% fewer target model invocations while maintaining comparable accuracy.

cs.LG↗

arXiv2026

TeachBench: A Syllabus-Grounded Framework for Evaluating Teaching Ability in Large Language Models

Large language models (LLMs) show promise as teaching assistants, yet their teaching capability remains insufficiently evaluated. Existing benchmarks mainly focus on problem-solving or problem-level guidance, leaving knowledge-centered teaching underexplored. We propose a syllabus-grounded evaluation framework that measures LLM teaching capability via student performance improvement after multi-turn instruction. By restricting teacher agents to structured knowledge points and example problems, the framework avoids information leakage and enables reuse of existing benchmarks. We instantiate the framework on Gaokao data across multiple subjects. Experiments reveal substantial variation in teaching effectiveness across models and domains: some models perform well in mathematics, while teaching remains challenging in physics and chemistry. We also find that incorporating example problems does not necessarily improve teaching, as models often shift toward example-specific error correction. Overall, our results highlight teaching ability as a distinct and measurable dimension of LLM behavior.

cs.AI↗

arXiv2026

Adaptive Reinforcement and Model Predictive Control Switching for Safe Human-Robot Cooperative Navigation

This paper addresses the challenge of human-guided navigation for mobile collaborative robots under simultaneous proximity regulation and safety constraints. We introduce Adaptive Reinforcement and Model Predictive Control Switching (ARMS), a hybrid learning-control framework that integrates a reinforcement learning follower trained with Proximal Policy Optimization (PPO) and an analytical one-step Model Predictive Control (MPC) formulated as a quadratic program safety filter. To enable robust perception under partial observability and non-stationary human motion, ARMS employs a decoupled sensing architecture with a Long Short-Term Memory (LSTM) temporal encoder for the human-robot relative state and a spatial encoder for 360-degree LiDAR scans. The core contribution is a learned adaptive neural switcher that performs context-aware soft action fusion between the two controllers, favoring conservative, constraint-aware QP-based control in low-risk regions while progressively shifting control authority to the learned follower in highly cluttered or constrained scenarios where maneuverability is critical, and reverting to the follower action when the QP becomes infeasible. Extensive evaluations against Pure Pursuit, Dynamic Window Approach (DWA), and an RL-only baseline demonstrate that ARMS achieves an 82.5 percent success rate in highly cluttered environments, outperforming DWA and RL-only approaches by 7.1 percent and 3.1 percent, respectively, while reducing average computational latency by 33 percent to 5.2 milliseconds compared to a multi-step MPC baseline. Additional simulation transfer in Gazebo and initial real-world deployment results further indicate the practicality and robustness of ARMS for safe and efficient human-robot collaboration. Source code and a demonstration video are available at https://github.com/21ning/ARMS.git.

cs.RO↗

arXiv2026

Design and Empirical Study of a Large Language Model-Based Multi-Agent Investment System for Chinese Public REITs

This study addresses the low-volatility Chinese Public Real Estate Investment Trusts (REITs) market, proposing a large language model (LLM)-driven trading framework based on multi-agent collaboration. The system constructs four types of analytical agents-announcement, event, price momentum, and market-each conducting analysis from different dimensions; then the prediction agent integrates these multi-source signals to output directional probability distributions across multiple time horizons, then the decision agent generates discrete position adjustment signals based on the prediction results and risk control constraints, thereby forming a closed loop of analysis-prediction-decision-execution. This study further compares two prediction model pathways: for the prediction agent, directly calling the general-purpose large model DeepSeek-R1 versus using a specialized small model Qwen3-8B fine-tuned via supervised fine-tuning and reinforcement learning alignment. In the backtest from October 2024 to October 2025, both agent-based strategies significantly outperformed the buy-and-hold benchmark in terms of cumulative return, Sharpe ratio, and maximum drawdown. The results indicate that the multi-agent framework can effectively enhance the risk-adjusted return of REITs trading, and the fine-tuned small model performs close to or even better than the general-purpose large model in some scenarios.

q-fin.ST↗

arXiv2026

Biphasic Meniscus Coating for Scalable and Material Efficient Quantum Dot Films

Colloidal quantum dots (cQDs) have emerged as a cornerstone of next-generation optoelectronics, offering unparalleled spectral tunability and solution-processability. However, the transition from laboratory-scale devices to sustainable industrial manufacturing is fundamentally hindered by spin-coating workflows, which are intrinsically wasteful and restricted to planar geometries. These limitations are particularly acute for high-performance cQDs containing regulated elements such as lead, cadmium, or mercury, where poor material utilization exacerbates both environmental burden and cost. Here we report a biphasic dip-coating strategy that redefines the material efficiency of nanocrystal film fabrication. By utilizing an immiscible underlayer to displace ~88% of the active reservoir volume, we demonstrate a deposition geometry that decouples material consumption from total precursor volume. Infrared PbS photodetectors fabricated via this approach maintain their performance against spin-coated benchmarks while reducing ink consumption by up to 20-fold. Our technoeconomic analysis reveals that this biphasic architecture achieves cost parity at film thicknesses an order of magnitude lower than conventional monophasic dip-coating. Our results establish a low-waste framework for solution-processed materials, providing a viable pathway for the resource-efficient manufacturing of optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2026

Beyond the Safety Tax: Mitigating Unsafe Text-to-Image Generation via External Safety Rectification

Text-to-image (T2I) generative models have achieved remarkable visual fidelity, yet remain vulnerable to generating unsafe content. Existing safety defenses typically intervene internally within the generative model, but suffer from severe concept entanglement, leading to degradation of benign generation quality, a trade-off we term the Safety Tax. To overcome this limitation, we advocate a paradigm shift from destructive internal editing to external safety rectification. Following this principle, we propose SafePatch, a structurally isolated safety module that performs external, interpretable rectification without modifying the base model. The core backbone of SafePatch is architecturally instantiated as a trainable clone of the base model's encoder, allowing it to inherit rich semantic priors and maintain representation consistency. To enable interpretable safety rectification, we construct a strictly aligned counterfactual safety dataset (ACS) for differential supervision training. Across nudity and multi-category benchmarks and recent adversarial prompt attacks, SafePatch achieves robust unsafe suppression (7% unsafe on I2P) while preserving image quality and semantic alignment.

cs.CV↗

arXiv2026

Engineering of Orbital Hybridization: An Exotic Strategy to Manipulate Orbital Current

Current-induced spin-orbit torque (SOT) plays a crucial role in the next-generation spin-orbitronics. Enhancing its efficiency is both fundamentally and practically interesting and remains a challenge to date. Recently, orbital counterparts of spin effects that do not rely on the spin-orbit coupling (SOC) have been found as an alternative mechanism to realize it. This work highlights the engineering of copper oxidation states for manipulating the orbital current and its torque in the CuO$_x$-based heterostructures. The orbital hybridization and thus the orbital-Rashba-Edelstein effect at the CuO$_x$/Cu interfaces are significantly enhanced by increasing the copper oxidation state, yielding a torque efficiency that is almost ten times larger than the conventional heavy metals. The Cu$_4$O$_3$/Cu interface, rather than the widely accepted CuO/Cu interface, is revealed to account for the enhanced SOT performance in the CuO$_x$-based heterostructures. In addition, the torque efficiency can be alternatively switched between high and low thresholds through the redox reaction. The current results establish an exotic and robust strategy for engineering the orbital current and SOT for spin-orbitronics, which applies to other weak-SOC materials.

cond-mat.mtrl-sci↗

arXiv2026

Unusual antiferromagnetic order and fluctuations in RbMn$_{6}$Bi$_{5}$

Quasi-one-dimensional RbMn$_{6}$Bi$_{5}$, the first pressure-induced ternary Mn-based superconductor, exhibits a phase diagram analogous to those of cuprate and iron-based superconductors, with superconductivity neighboring antiferromagnetic order. Here, we use $^{55}$Mn and $^{87}$Rb nuclear magnetic resonance (NMR) to unravel its magnetic structure and fluctuations. Above the Néel temperature ($T_{\rm N}$), strong antiferromagnetic fluctuations dominate, characteristic of a paramagnetic state with pronounced spin-lattice relaxation rate enhancement. Below $T_{\rm N}$, a first-order phase transition establishes a commensurate antiferromagnetic order, where Mn atoms at the pentagon corners exhibit distinct magnetic moments with different orientations, while the central Mn atom carries no magnetic moment. The complex magnetic architecture, revealed by zero-field and high-magnetic-field NMR spectra, contrasts with earlier neutron diffraction models proposing uniform spin density waves, instead supporting localized moments ordering with charge rearrangement. The proximity of robust antiferromagnetic fluctuations to the high-pressure superconducting phase suggests a potential role for magnetic excitations in mediating unconventional Cooper pairing, akin to paradigmatic high-$T_c$ systems. These findings provide critical insights into the interplay between geometric frustration, magnetic order, and superconductivity in manganese-based materials.

cond-mat.supr-con↗

arXiv2026

Beyond Known Fakes: Generalized Detection of AI-Generated Images via Post-hoc Distribution Alignment

The rapid proliferation of highly realistic AI-generated images poses serious security threats such as misinformation and identity fraud. Detecting generated images in open-world settings is particularly challenging when they originate from unknown generators, as existing methods typically rely on model-specific artifacts and require retraining on new fake data, limiting their generalization and scalability. In this work, we propose Post-hoc Distribution Alignment (PDA), a generalized and model-agnostic framework for detecting AI-generated images under unknown generative threats. Specifically, PDA reformulates detection as a distribution alignment task by regenerating test images through a known generative model. When real images are regenerated, they inherit model-specific artifacts and align with the known fake distribution. In contrast, regenerated unknown fakes contain incompatible or mixed artifacts and remain misaligned. This difference allows an existing detector, trained on the known generative model, to accurately distinguish real images from unknown fakes without requiring access to unseen data or retraining. Extensive experiments across 16 state-of-the-art generative models, including GANs, diffusion models, and commercial text-to-image APIs (e.g., Midjourney), demonstrate that PDA achieves average detection accuracy of 96.69%, outperforming the best baseline by 10.71%. Comprehensive ablation studies and robustness analyses further confirm PDA's generalizability and resilience to distribution shifts and image transformations. Overall, our work provides a practical and scalable solution for real-world AI-generated image detection where new generative models emerge continuously.

cs.CR↗

arXiv2026

VidLeaks: Membership Inference Attacks Against Text-to-Video Models

The proliferation of powerful Text-to-Video (T2V) models, trained on massive web-scale datasets, raises urgent concerns about copyright and privacy violations. Membership inference attacks (MIAs) provide a principled tool for auditing such risks, yet existing techniques - designed for static data like images or text - fail to capture the spatio-temporal complexities of video generation. In particular, they overlook the sparsity of memorization signals in keyframes and the instability introduced by stochastic temporal dynamics. In this paper, we conduct the first systematic study of MIAs against T2V models and introduce a novel framework VidLeaks, which probes sparse-temporal memorization through two complementary signals: 1) Spatial Reconstruction Fidelity (SRF), using a Top-K similarity to amplify spatial memorization signals from sparsely memorized keyframes, and 2) Temporal Generative Stability (TGS), which measures semantic consistency across multiple queries to capture temporal leakage. We evaluate VidLeaks under three progressively restrictive black-box settings - supervised, reference-based, and query-only. Experiments on three representative T2V models reveal severe vulnerabilities: VidLeaks achieves AUC of 82.92% on AnimateDiff and 97.01% on InstructVideo even in the strict query-only setting, posing a realistic and exploitable privacy risk. Our work provides the first concrete evidence that T2V models leak substantial membership information through both sparse and temporal memorization, establishing a foundation for auditing video generation systems and motivating the development of new defenses. Code is available at: https://zenodo.org/records/17972831.

cs.CR↗

arXiv2026

Field-induced magnetic phase transitions and transport anomalies in GdAlSi

Magnetic topological materials hosting non-zero Berry curvature have emerged as a focus of intensive research due to their exceptional magnetoelectric coupling phenomena and potential applications in next-generation spintronic devices. In this work, we successfully synthesized high-quality GdAlSi single crystals, a prototypical member of RAlX (R = rare earth elements; X = Si/Ge) family that has been theoretically predicted to sustain a non-trivial Weyl semimetal state. Through systematic investigations of magnetic and transport properties, we identified two successive antiferromagnetic transitions at critical temperatures TN1 31.9 K and TN2 31.1 K, as evidenced by temperature-dependent resistivity, magnetic susceptibility, and specific heat measurements. Notably, applied magnetic fields exceeding 8 T induce a third magnetic transition (TN3), generating a cascade of metamagnetic transitions that collectively form a dendritic phase diagram. This complex magnetic behavior is attributed to the interplay between localized Gd-4f moments and itinerant conduction electrons, possibly mediated by Dzyaloshinskii-Moriya interactions. Transport measurements revealed striking stepwise anomalies in magnetoresistance when crossing phase boundaries, accompanied by pronounced hysteresis loops arising from magnetic moment flopping processes. Our results not only establish GdAlSi as a rich platform for investigating correlated topological states, but also demonstrate its potential for engineering topological phase transitions through magnetic symmetry manipulation in Weyl semimetals.

cond-mat.str-el↗

arXiv2026

Pressure-Free Surface-Induced Flow by Geometric Rectification

Pressure-driven flow collapses when confined ($u\propto r^{2}$). Asymmetry rectifies surface activity (exchange or slip gradients) into axial flux at $ΔP=0$ despite zero net exchange. Lorentz reciprocity yields a projection law: throughput is the inner product of source with a geometry kernel. Signatures include inverted ``narrower-is-faster'' scaling ($u\propto r^{-1}$), leading-order viscosity independence, length amplification ($Q\propto L$), and linear superposition, defining surface-induced flow as a pressure-free Stokes-transport mode from microfluidics to physiology.

physics.flu-dyn↗

arXiv2025

Kinetic Catalysis of Spontaneous Knotting: How Free Particles Modulate Filament Entanglement

Entangled knots form spontaneously in flexible filaments, yet the influence of the surrounding environment on this process is poorly understood. Here we demonstrate that free-moving particles act as kinetic catalysts for spontaneous knotting. Through controlled agitation experiments, we find that a small number of inert beads substantially enhance the probability and accelerate the rate of knot formation. This catalytic effect is non-monotonic: an optimal particle size and concentration that maximizes entanglement, while an excess of particles suppresses knotting by impeding the filament's dynamics. We develop a stochastic model that quantitatively reproduces this behavior, attributing it to a competition between entanglement-promoting collisions and motion-suppressing drag. Our findings reveal a mechanism for tuning topological complexity, whereby adjusting these environmental agitators can either promote rapid self-assembly or inhibit unwanted entanglement. This work suggests new strategies for controlling filament topology in settings ranging from crowded biological environments to advanced materials processing.

cond-mat.soft↗

arXiv2025

HY-MT1.5 Technical Report

In this report, we introduce our latest translation models, HY-MT1.5-1.8B and HY-MT1.5-7B, a new family of machine translation models developed through a holistic training framework tailored for high-performance translation. Our methodology orchestrates a multi-stage pipeline that integrates general and MT-oriented pre-training, supervised fine-tuning, on-policy distillation, and reinforcement learning. HY-MT1.5-1.8B, the 1.8B-parameter model demonstrates remarkable parameter efficiency, comprehensively outperforming significantly larger open-source baselines (e.g., Tower-Plus-72B, Qwen3-32B) and mainstream commercial APIs (e.g., Microsoft Translator, Doubao Translator) in standard Chinese-foreign and English-foreign tasks. It achieves approximately 90% of the performance of ultra-large proprietary models such as Gemini-3.0-Pro, while marginally trailing Gemini-3.0-Pro on WMT25 and Mandarin-minority language benchmarks, it maintains a substantial lead over other competing models. Furthermore, HY-MT1.5-7B establishes a new state-of-the-art for its size class, achieving 95% of Gemini-3.0-Pro's performance on Flores-200 and surpassing it on the challenging WMT25 and Mandarin-minority language test sets. Beyond standard translation, the HY-MT1.5 series supports advanced constraints, including terminology intervention, context-aware translation, and format preservation. Extensive empirical evaluations confirm that both models offer highly competitive, robust solutions for general and specialized translation tasks within their respective parameter scales.

cs.CL↗

arXiv2025

RESPOND: Risk-Enhanced Structured Pattern for LLM-driven Online Node-level Decision-making

Current LLM-based driving agents that rely on unstructured plain-text memory suffer from low-precision scene retrieval and inefficient reflection. To address this limitation, we present RESPOND, a structured decision-making framework for LLM-driven agents grounded in explicit risk patterns. RESPOND represents each ego-centric scene using a unified 5 by 3 matrix that encodes spatial topology and road constraints, enabling consistent and reliable retrieval of spatial risk configurations. Based on this representation, a hybrid rule and LLM decision pipeline is developed with a two-tier memory mechanism. In high-risk contexts, exact pattern matching enables rapid and safe reuse of verified actions, while in low-risk contexts, sub-pattern matching supports personalized driving style adaptation. In addition, a pattern-aware reflection mechanism abstracts tactical corrections from crash and near-miss frames to update structured memory, achieving one-crash-to-generalize learning. Extensive experiments demonstrate the effectiveness of RESPOND. In highway-env, RESPOND outperforms state-of-the-art LLM-based and reinforcement learning based driving agents while producing substantially fewer collisions. With step-wise human feedback, the agent acquires a Sporty driving style within approximately 20 decision steps through sub-pattern abstraction. For real-world validation, RESPOND is evaluated on 53 high-risk cut-in scenarios extracted from the HighD dataset. For each event, intervention is applied immediately before the cut-in and RESPOND re-decides the driving action. Compared to recorded human behavior, RESPOND reduces subsequent risk in 84.9 percent of scenarios, demonstrating its practical feasibility under real-world driving conditions. These results highlight RESPONDs potential for autonomous driving, personalized driving assistance, and proactive hazard mitigation.

cs.HC↗

arXiv2025

Interaction Dataset of Autonomous Vehicles with Traffic Lights and Signs

This paper presents the development of a comprehensive dataset capturing interactions between Autonomous Vehicles (AVs) and traffic control devices, specifically traffic lights and stop signs. Derived from the Waymo Motion dataset, our work addresses a critical gap in the existing literature by providing real-world trajectory data on how AVs navigate these traffic control devices. We propose a methodology for identifying and extracting relevant interaction trajectory data from the Waymo Motion dataset, incorporating over 37,000 instances with traffic lights and 44,000 with stop signs. Our methodology includes defining rules to identify various interaction types, extracting trajectory data, and applying a wavelet-based denoising method to smooth the acceleration and speed profiles and eliminate anomalous values, thereby enhancing the trajectory quality. Quality assessment metrics indicate that trajectories obtained in this study have anomaly proportions in acceleration and jerk profiles reduced to near-zero levels across all interaction categories. By making this dataset publicly available, we aim to address the current gap in datasets containing AV interaction behaviors with traffic lights and signs. Based on the organized and published dataset, we can gain a more in-depth understanding of AVs' behavior when interacting with traffic lights and signs. This will facilitate research on AV integration into existing transportation infrastructures and networks, supporting the development of more accurate behavioral models and simulation tools.

cs.RO↗