arXiv subjects

Shi Liu

Publications and source records attributed to Shi Liu.

At least 19 recordsLinked to original sources

arXiv2026

Skyrmion nucleus resolves the Landauer paradox

Ferroelectric switching is among the most fundamental and widely studied symmetry-breaking processes in physics. However, the Landauer paradox asserts that an ideal single-domain ferroelectric should be kinetically unswitchable, because a three-dimensional reversed nucleus would carry a prohibitive depolarization penalty, leading to unphysically large coercive field (the electric field required to switch the polarization). Here we show that this long-standing paradox is resolved by topology. In defect-free PbTiO3, large-scale molecular dynamics simulations reveal that the intrinsic critical nucleus is a three-dimensional polar skyrmion. Its continuous polarization rotation and N\'eel-type boundary walls self-compensate bound charge, suppressing the depolarization energy by orders of magnitude and bringing the predicted coercive field into agreement with experiment. This topological nucleus further overturns the 60-year-old Janovec-Kay-Dunn law. We derive an analytical thickness-dependent coercive-field law governed by field-induced softening of the parent ferroelectric state and controlled by a single material descriptor. This framework captures diverse experimental trends across ferroelectric families and recasts the Kay-Dunn exponent of -2/3 as an effective finite-window behavior. These results establish polar topology as an organizing principle for intrinsic switching and suggest that hidden topological transition states may broadly shape nonequilibrium phase transformations traditionally understood as Landau-type symmetry breaking.

cond-mat.mtrl-sci↗

arXiv2026

Percolating Multifractal Domains at a Polymorphic Phase Boundary

Giant piezoelectricity in ferroelectrics is commonly associated with phase-boundary instabilities, among which the polymorphic phase boundary (PPB) is a prominent example conventionally attributed to the coexistence of ferroelectric phases. Here, using large-scale molecular dynamics simulations of the lead-free (K,Na)NbO3-(Bi,Na)ZrO3 solid solutions, we show that the PPB hosts a percolating multifractal polar domain which governs the dielectric and piezoelectric responses. By quantifying the global fractal dimension and multifractal spectrum width of this polar network, we identify fractal connectivity and multiscale heterogeneity as microstructural order parameters for the PPB. The maximum reversible piezoelectric response occurs when the fractal-domain volume fraction approaches the three-dimensional percolation threshold, suggesting that near-critical polar connectivity enables giant reversible electromechanical coupling. In this mechanism, the fractal backbone preserves polar memory and provides the restoring force required for reversibility, while the surrounding nonfractal regions supply the polar compliance needed for large polarization rotation and strain. These results establish percolating multifractal polar domains as a microscopic mechanism for PPB-enhanced piezoelectricity and suggest fractal connectivity as a design parameter for high-performance piezoelectrics.

cond-mat.mtrl-sci↗

arXiv2026

Emergent Interfacial Magnetism in Epitaxial RuO$_2$

The magnetic ground state of the altermagnet candidate RuO$_2$ remains controversial, with magnetic signatures observed mainly in epitaxial films. Here we show, using first-principles calculations, that magnetism in epitaxial RuO$_2$ can emerge as an interfacial boundary phase at TiO$_2$/RuO$_2$ interfaces. While TiO$_2$-induced epitaxial strain alone does not make (001)-oriented RuO$_2$ magnetic, explicit TiO$_2$/RuO$_2$ interfaces stabilize sizable Ru moments confined to the first few Ru layers. Charge-density and orbital-resolved analyses reveal interfacial electronic reconstruction, and substrate doping provides a route to tune the induced moments. In symmetric TiO$_2$/RuO$_2$/TiO$_2$ heterostructures, the two magnetic interfaces couple through the metallic RuO$_2$ spacer, producing a thickness-dependent alternation between weak-ferromagnetic and compensated altermagnetic states. Our results identify interface engineering as a practical route to stabilize and control fragile magnetism in RuO$_2$.

cond-mat.mtrl-sci↗

arXiv2026

From Foundation to Application: Improving VLA Models in Practice

Despite recent progress of VLA foundation models, the disparity between laboratory conditions and real-world applications continues to impede their practical implementation. To bridge this gap, we present LingBot-VLA 2.0, which advances LingBot-VLA through improvements in three functional domains. (1) Generalization across tasks and embodiments. Compared to the previous version, we revamp the data processing pipeline and curate around 60,000 hours of data for pretraining, including 50,000 hours of robot trajectories spanning 20 robot configurations and 10,000 hours of egocentric human videos. (2) Expanded action space in addition to dual-arm hardware platforms. In particular, our system accommodates degrees of freedom for the heads, waists, mobile bases, and dexterous hands, thereby empowering the robots to tackle more complex tasks in practical scenarios. (3) Predictive dynamics modeling for improved temporal reasoning. Specifically, we formulate future prediction as a proxy task, facilitated by a video representation model for semantic priors and a depth estimation model for geometric cues. Evaluations on the GM-100 benchmark, conducted in a generalist setting, validate the beneficial impact of these proposed modifications. Furthermore, benefiting from the expanded pretraining data that covers whole-body degrees of freedom, LingBot-VLA-2.0 demonstrates strong cross-embodiment long-horizon mobile manipulation capability across the two robotic platforms.

cs.RO↗

arXiv2026

Unlocking Cryogenic Energy Storage by Constructing Dipole Glass with Unit-cell-level Polar Disorder

Cryogenic energy storage is vital for frontier technologies including deep-space exploration and quantum computing, yet conventional electrochemical energy systems fail below ~230 K due to frozen ion migration. While relaxor-based dielectric capacitors provide high efficiency at room temperature, the intrinsic freezing/growth of polar nanodomains at extended cryogenic regime limits their applications with deteriorated hysteresis losses. Here, we realize superior cryogenic energy-storage performance by designing unit-cell-level disordered dipole-glass state in Pb0.6Sr0.4ZrO3 thin films with composition near antiferroelectric-paraelectric phase boundary. The antiferroelectric-derived dipole-glass introduces enhanced unit-cell-level complexity of dipole interaction that suppresses long-range ferroelectric order. This enables ultralow-hysteresis operation (efficiency > 88%) down to 4 K, delivering record-high energy density (211 J/cm^3) at 9 MV/cm, stability over 10^8 charge/discharge cycles and microsecond-scale charge/discharge capability. This work establishes a dipole-glass paradigm for cryogenic dielectric capacitors, opening a new avenue to highly-efficient energy-storage systems with broad applications in frontier nanoelectronics.

cond-mat.mtrl-sci↗

arXiv2026

StatefulDiscovery: Evidence-Calibrated Claim Formation in Open-Ended Scientific Discovery

Open-ended scientific discovery asks agents to move beyond executing analyses for predefined questions. Across multiple rounds of exploration, a discovery agent must decide which phenomena warrant investigation while avoiding overinterpretation, where emerging claims exceed the evidential scope of the analyses. This creates an evidence-calibration problem: the exploration trajectory must be coupled with claim status so that evidence can guide both what to investigate next and what can be claimed. We introduce \textsc{StatefulDiscovery}, a discovery framework that externalizes investigation state and uses it to coordinate frontier selection, evidence acquisition, and claim adjudication. We evaluate \textsc{StatefulDiscovery} across 40 real-data discovery tasks. Compared with several baselines, \textsc{StatefulDiscovery} produces more claims overall judged to be both well-supported and high-value. Ablations indicate distinct roles for structured hypotheses, local adjudication, frontier control and persistent states. Together, these results suggest that explicit discovery state can couple exploration with evidence-calibrated claim formation. Our code is released at \href{https://github.com/SUSTech-GenAI/StatefulDiscovery.git}{https://github.com/SUSTech-GenAI/StatefulDiscovery.git}.

cs.AI↗

arXiv2026

Notes2Skills: From Lab Notebooks to Certainty-Aware Scientific Agent Skills

Scientific discovery workflows usually contain and rely heavily on lab notes, where researchers record observations, interpret uncertain results, and plan follow-up experiments. Such informative lab notes preserve evolving scientific reasoning and author uncertainty, rather than polished final results exhibited in publications, providing a valuable opportunity for AI to engage in scientific exploration at a more comprehensive and deeper level. However, most prior work on scientific text focuses on papers, protocols, or structured databases, leaving informal laboratory notes underexplored as inputs to AI agents for science. This gap matters because lab notes often intermingle validated observations, tentative judgments, and possible experimental next steps within the same passage. If these signals are conflated, an AI agent may mistake uncertain scientific judgments for confirmed conclusions or executable actions. To this end, we present Notes2Skills, a two-stage framework for turning lab notebooks into verifiable skills for scientific AI agents while preserving the author's certainty. Across seven conditions and three wet-lab sessions, Notes2Skills is the only configuration that neither mistakes uncertain notes for firm instructions nor discards firm ones. We show that certainty preservation is the missing piece between lab notebooks and reliable agent skills, opening a path toward safer AI co-scientist systems.

cs.CL↗

arXiv2026

Polaron Transport in TiO$_{2}$ from Machine Learning Molecular Dynamics

Transition metal oxides have attracted much attention as photo(electrochemical)-catalysts but practical applications are typically hampered by their low and anisotropic charge mobility. A deep understanding of excess charge carrier transport in these materials requires a dynamical treatment of nuclear motion that goes well beyond standard approaches. Here we introduce DeepPolaron, a machine learning framework boosting the accessible time scale of first principles molecular dynamics of adiabatic polaron transport by three orders of magnitude at a virtually negligible loss in accuracy. We apply our method to excess electron and hole transport in titanium dioxide rutile and anatase. We find that the excess electron in rutile relaxes to a polaron predominantly localized on a single Ti atom with hopping occurring only along the [001] direction, associated with an activation energy of 39 meV and a room temperature mobility of $4.4 \times 10^{-2}$ cm$^2$/Vs in good agreement with experiment. In contrast the hole polaron in anatase is localized on a single O atom, and due to poor O 2p orbital overlap with first nearest neighbors charge transport occurs primarily to second nearest neighbors, with a large activation energy of 139 meV resulting in a small room temperature mobility of $1.4 \times 10^{-3}$ cm$^2$/Vs. This work provides a finite temperature first-principles characterization of small polaron transport in rutile and anatase, with a methodology that is directly transferable to other small polaron forming materials and interfacial charge-transfer processes.

cond-mat.mtrl-sci↗

arXiv2026

When the Manual Lies: A Realistic Benchmark to Evaluate MCP Poisoning Attacks for LLM Agents

The rise of tool-using Large Language Model (LLM) agents, standardized by protocols like the Model Context Protocol (MCP), has unlocked unprecedented autonomous execution capabilities for LLM Agents by integrating external open-domain knowledge and tools. However, this interoperability introduces a covert attack surface targeting the agent's cognitive planning layer. This paper systematically investigates Tool Description Poisoning (TDP), a novel semantic attack. In TDP, malicious instructions are not embedded in a tool's executable code, but rather covertly injected into its descriptive metadata, the very "manual" an agent relies on for secure planning and decision-making. To rigorously and systematically evaluate this emerging threat, we introduce the MCP-TDP Security Benchmark. This high-fidelity sandbox environment comprises 32 realistic, real-world test cases spanning 6 distinct risk categories. Our evaluation of 8 mainstream LLMs reveals severe vulnerabilities, with leading models like GPT-4o exhibiting a nearly 100% Attack Success Rate (ASR) in six high-risk scenarios. Furthermore, our findings demonstrate that common prompt-guardrail defenses are largely ineffective and can, counterintuitively, even be counterproductive (a phenomenon which we term the "Firewall Fallacy"). Crucially, we also propose a defense mechanism: "Reactive Self-Correction," where an agent autonomously detects and reverts its own malicious actions post-execution. This work provides the first specialized security benchmark tailored for TDP, offering essential insights for securing the cognitive and planning layers of advanced agentic systems.

cs.CR↗

arXiv2026

Revisiting Ferroelectricity Beyond Polar Space Groups

Ferroelectricity, a hallmark of spontaneous inversion-symmetry breaking, has been a central concept in condensed matter physics and functional materials research, yet recent discoveries are revealing that switchable polarization can emerge in forms far richer than allowed by the conventional symmetry-based paradigm. Fractional quantum ferroelectricity and ionic-conductor ferroelectricity challenge the long-standing association of ferroelectricity exclusively with polar space groups. In this Review, we reconcile these emerging phenomena within the Berry-phase modern theory of polarization. We emphasize that polarization in insulating periodic crystals is not a single-valued vector, but a multivalued lattice quantity defined modulo a polarization quantum. Consequently, nonpolar crystals may possess nonzero formal polarization, and adiabatic paths connecting symmetry-equivalent structures can produce quantized changes in polarization without violating symmetry principles. The symmetry of this multivalued formal polarization is governed by a generalized Neumann principle. We further show that the large polarization changes induced by long-range ion migration in both fractional quantum ferroelectrics and ionic-conductor ferroelectrics can be naturally understood through the topological definition of oxidation state, which links ionic transport to quantized charge transfer and polarization change. We discuss the physical accessibility of these unconventional polarization states, highlighting the roles of switching pathways, boundary conditions, and domain-wall dynamics, particularly in systems such as $\alpha$-In$_2$Se$_3$. Finally, we suggest that the most promising functionality of these materials may lie not in conventional bulk ferroelectric switching, but in the creation and control of charged interfaces and domain walls arising from discontinuities in formal polarization.

cond-mat.mtrl-sci↗

arXiv2026

Proximity Ferroelectricity Driven by Mobile High-Miller-Index Domain Walls

Wurtzite ferroelectrics such as scandium-doped aluminum nitride (AlScN) are promising for next-generation memory because of their compatibility with semiconductor processes and strong spontaneous polarization. Ferroelectric switching in these materials is typically attributed to doping-induced softening of the bulk switching barrier. However, recent reports of proximity ferroelectricity, in which undoped AlN layers up to 500 nm thick fully switch in AlN/AlScN multilayers, challenge this view. Here, we reveal an alternative switching mechanism mediated by high-Miller-index domain walls, long overlooked due to their complex geometry and presumed instability. Using first-principles calculations and machine-learning molecular dynamics simulations, we show that these walls, once nucleated, migrate with exceptionally low barriers. The Sc dopants play a dual role: they stabilize high-index walls and thereby promote nucleation, while also introducing pinning that hinders wall motion. In multilayers, our simulations demonstrate that mobile domain walls nucleated in AlScN can propagate deep into adjacent AlN, where they move easily without dopant pinning, enabling low-field switching across thick undoped layers. This microscopic divide-and-conquer mechanism resolves the puzzle of proximity ferroelectricity and highlights high-index interfaces as an underexplored lever for controlling ferroelectric switching.

cond-mat.mtrl-sci↗

arXiv2026

Unlocking Static Polarization and Strain Density Waves in Perovskites by Softening a Hidden Antiferrodistortive Tilt Gradient Mode

Spin density waves (SDWs) represent a fundamental paradigm of spatially modulated order in condensed matter systems, yet their electrical and mechanical analogues polarization and strain density waves (PDWs and StDWs) have remained elusive as equilibrium phases. Here, we introduce a general, symmetry-driven strategy to unlock static PDWs and StDWs in perovskites SrTiO3 and SrMnO3. Using first-principles calculations, we uncover a previously overlooked soft antiferrodistortive tilt gradient mode at small-q wavevector in the phonon dispersion of their presumed Ima2 ground state under moderate tensile strain. Group-theory analysis reveals that a hard polaracoustic phonon, which intrinsically carries PDWs and StDWs, is improperly destabilized by a trilinear coupling with this modulated tilt mode and an inherently uniform tilt mode. This interaction drives a structural transition from the Ima2 phase to a novel lower-energy Pmn21 phase that hosts long-range-ordered PDWs and StDWs. Strikingly, the engineered StDWs in SrMnO3 activate an electrically tunable SDW via the flexomagnetic effect. These discoveries fundamentally revise the strain-phase diagrams of prototypical perovskites and establish a unified phonon-engineering framework that links modulated phonon instabilities to targeted density-wave order, offering new pathways for designing advanced electromechanical and magnetoelectric functionalities.

cond-mat.mtrl-sci↗

arXiv2026

UT-ACA: Uncertainty-Triggered Adaptive Context Allocation for Long-Context Inference

Long-context inference remains challenging for large language models due to attention dilution and out-of-distribution degradation. Context selection mitigates this limitation by attending to a subset of key-value cache entries, yet most methods allocate a fixed context budget throughout decoding despite highly non-uniform token-level contextual demands. To address this issue, we propose Uncertainty-Triggered Adaptive Context Allocation (UT-ACA), an inference-time framework that dynamically adjusts the context window based on token-wise uncertainty. UT-ACA learns an uncertainty detector that combines semantic embeddings with logit-based confidence while accounting for uncertainty accumulation across decoding steps. When insufficient evidence is indicated, UT-ACA selectively rolls back, expands the context window, and regenerates the token with additional support. Experiments show that UT-ACA substantially reduces average context usage while preserving generation quality in long-context settings. Code and dataset are available at https://github.com/Tommy307/UT-ACA.

cs.CL↗

arXiv2026

GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP

Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum-mechanical accuracy at empirical-potential speeds. The graphics processing unit molecular dynamics (GPUMD) package, featuring the highly efficient neuroevolution potential (NEP) framework, has emerged as a powerful tool in this domain. However, the complexity of force field development, active learning, and trajectory post-processing often requires extensive manual scripting, imposing a steep learning curve on new users. To address this, we present GPUMDkit, a comprehensive and user-friendly toolkit that streamlines the entire simulation workflow for GPUMD and NEP. GPUMDkit integrates a suite of essential functionalities, including format conversion, structure sampling, property calculation, and data visualization, accessible through both interactive and command-line interfaces. Its modular, extensible architecture ensures accessibility for users of all experience levels while allowing seamless integration of new features. By automating complex tasks and enhancing productivity, GPUMDkit substantially lowers the barrier to using GPUMD and NEP programs. This article describes the program architecture and demonstrates its capabilities through practical applications.

cond-mat.mtrl-sci↗

arXiv2026

CoEmpaTeam: Enhancing Cognitive Empathy using LLM-based Avatars and Dynamic Role Play in Virtual Reality

Cognitive empathy, the ability to understand others' perspectives, is essential for effective communication, reducing biases, and constructive negotiation. However, this skill is declining in a performance-driven society, which prioritizes efficiency over perspective-taking. Here, the training of cognitive empathy is challenging because it is a subtle, hard-to-perceive soft skill. To address this, we developed CoEmpaTeam, a VR-based system that enables users to train their cognitive empathy by using LLM-driven avatars with different personalities. Through dynamic role play, users actively engage in perspective-taking, experiencing situations through another person's eyes. CoEmpaTeam deploys three avatars who significantly differ in their personality, validated by a technical evaluation and an online experiment (n=90). Next, we evaluated the system through a lab experiment with 32 participants who performed three sessions across two weeks, followed by a one-week diary study. Our results showed a significant increase in cognitive empathy, which, according to participants, transferred into their real lives.

cs.HC↗

arXiv2026

AttentiveLearn: Personalized Post-Lecture Support for Gaze-Aware Immersive Learning

Immersive learning environments such as virtual classrooms in Virtual Reality (VR) offer learners unique learning experiences, yet providing effective learner support remains a challenge. While prior HCI research has explored in-lecture support for immersive learning, little research has been conducted to provide post-lecture support, despite being critical for sustained motivation, engagement, and learning outcomes. To address this, we present AttentiveLearn, a learning ecosystem that generates personalized quizzes on a mobile learning assistant based on learners' attention distribution inferred using eye-tracking in VR lectures. We evaluated the system in a four-week field study with 36 university students attending lectures on Bayesian data analysis. AttentiveLearn improved learners' reported motivation and engagement, without conclusive evidence of learning gains. Meanwhile, anecdotal evidence suggested improvements in attention for certain participants over time. Based on our findings of the field study, we provide empirical insights and design implications for personalized post-lecture support for immersive learning systems.

cs.HC↗

arXiv2026

Decoupling effects of the resistive-switching behavior on the polarization reversal in ultrathin ferroelectric Hf0.5Zr0.5O2 films

HfO2-based ferroelectric films have attracted considerable attention as their nanoscale ferroelectricity and compatibility with cmos technology, fulfilling demands of emerging memory technologies. However, as films scale down, resistive-switching behavior becomes increasingly pronounced, intricately intertwining with the polarization-switching process and affecting ferroelectric switching factors often overlooked yet crucial for device performance optimization. By characterizing resistive-switching behavior and oxygen vacancy motion using tailored electric pulse schemes, we decouple the resistive-switching behavior from the overall switching process in ultrathin ferroelectric HZO films, which would otherwise erroneously inflate polarization values and increase coercive fields. Building on this, we elucidate endurance degradation mechanisms from dual perspectives of resistive switching and defect migration. Furthermore, we demonstrate the mitigated resistive switching activity by designing HfO2-based devices with symmetric oxide electrodes, achieving reduced coercive fields and improved cycling performances. This work provides crucial insights into the origins of inflated polarizations and reliability challenges in HfO2-based devices while offering a viable strategy to enhance ferroelectric properties for advanced memory applications.

cond-mat.mtrl-sci↗

arXiv2026

A Pragmatic VLA Foundation Model

Offering great potential in robotic manipulation, a capable Vision-Language-Action (VLA) foundation model is expected to faithfully generalize across tasks and platforms while ensuring cost efficiency (e.g., data and GPU hours required for adaptation). To this end, we develop LingBot-VLA with around 20,000 hours of real-world data from 9 popular dual-arm robot configurations. Through a systematic assessment on 4 robotic platforms, each completing 100 tasks with 130 post-training episodes per task, our model achieves clear superiority over competitors, showcasing its strong performance and broad generalizability. We have also built an efficient codebase, which delivers a throughput of 261 samples per second with an 8-GPU training setup, representing a 1.5~2.8$\times$ (depending on the relied VLM base model) speedup over existing VLA-oriented codebases. The above features ensure that our model is well-suited for real-world deployment. To advance the field of robot learning, we provide open access to the code, base model, and benchmark data, with a focus on enabling more challenging tasks and promoting sound evaluation standards.

cs.RO↗