arXiv subjects

Shiyu Zhou

Publications and source records attributed to Shiyu Zhou.

At least 19 recordsLinked to original sources

arXiv2026

SleepWalking: Privileged Representation Shaping for End-to-End Blind Locomotion in Legged Robots

Partially observable locomotion requires a policy to act when task-relevant properties of the robot--environment state are not fully specified by instantaneous observations. Existing approaches often address this challenge by explicitly estimating missing physical variables or processing extended observation histories through structured architectures. We take a different view: partial observability is fundamentally an information-retention problem. The decisive question is not how task-relevant information enters the network, but whether the policy's internal state retains it. Guided by this perspective, we propose SleepWalking for Robot Locomotion (SWAQ), a one-stage end-to-end framework that uses next-step privileged physical reconstruction to shape what a recurrent history representation retains during policy learning, while the deployed actor uses only a direct history-to-action pathway. Under aligned training settings, SWAQ achieves a 15.0\% higher peak mean terrain level than DWAQ, the strongest non-exteroceptive baseline, while using 44.4\% fewer inference MACs per control step. Layerwise probes further show that information associated with the reconstructed physical variables remains linearly decodable through the policy head up to the layer preceding the action output. Complementary theoretical analysis relates privileged-variable recoverability to the achievable-return gap between history-based and privileged-information policy classes. These results suggest that semantic objectives can structure learning without requiring a corresponding architectural decomposition of the deployed controller.

cs.RO↗

arXiv2026

MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning

Predicting the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small molecules remains a major challenge in drug discovery. Here, we present MEGA-CL, a foundation graph neural network framework for universal molecular ADMET prediction. MEGA-CL integrates self-supervised contrastive learning with a multi-head external attention mechanism and an enhanced message-passing architecture, enabling simultaneous modeling of local chemical substructures and global inter-graph relationships while mitigating over-smoothing effects commonly observed in deep graph networks. Across 13 benchmark datasets and 21 downstream ADMET tasks, MEGA-CL consistently outperforms state-of-the-art baseline models. In particular, the framework demonstrates robust performance on challenging regression tasks, including clearance (CL) and steady-state volume of distribution (VDss), while maintaining strong generalization ability in independent external validation. Clinically relevant predictive accuracy was achieved, with more than 75% of predictions falling within a 3-fold error range. In an external evaluation on 18 novel compounds derived from recently approved FDA drugs, over 50% of human liver microsome clearance (HLMC) predictions were within a 2-fold error range. To further assess its practical applicability, MEGA-CL was prospectively evaluated on three preclinical drug candidates using in vitro hepatic microsomal metabolism assays and CYP450 inhibition assays guided by model predictions. The predicted HLMC values for all candidates were within 2.5-fold of the experimentally measured values, and 73.3% of CYP450 inhibition endpoints (11/15) were correctly classified. These results demonstrate the potential of MEGA-CL as a generalizable framework for accelerating in silico ADMET evaluation and early-stage drug candidate optimization.

cs.LG↗

arXiv2026

MissingBench-Verified: Probing Vision-Language Models' Inability to Detect Missing Object Parts

Vision Language Models (VLMs) are well known for hallucinating non-existent objects in images. Objects with missing parts present a unique challenge for VLMs, stemming from both real-world knowledge bias and the scarcity of such images in training data. We present MissingBench-Verified, a benchmark designed to evaluate a specific and practically relevant scenario: when vision-language models fail to recognize that an essential component of an object has been removed. Across ten leading models, we observe consistent and significant failure rates that persist even when external tool evidence explicitly contradicts the model's visual perception. We further ask whether granting models access to image processing tools (e.g., cropping, contrast adjustment) enables autonomous inspection to resolve these failures. We find that existing mitigation strategies, including tool-assisted verification, autonomous visual reasoning, longer reasoning durations, and fine-tuning on an easier dataset, provide negligible improvement, indicating that this failure mode cannot be addressed through current prompting or post-hoc correction techniques. Our findings highlight a fundamental limitation of current VLM for inspection and monitoring tasks and underscore the need for architectural or training-level interventions that enable models to override internal expectations when confronted with contradictory evidence.

cs.CV↗

arXiv2026

Evolution of Quadrupole Wakefield Driven by Transversely Asymmetric Electron Beams in Hollow Plasma Channels

Plasma wakefield acceleration in hollow plasma channels has emerged as a promising approach for positron acceleration, since an electron beam can drive wakes with a transversely uniform accelerating field and no intrinsic defocusing force for positrons. Recently, it was proposed that a transversely asymmetric electron beam can excite quadrupole-dominated wakefield in a hollow channel, enabling the formation of accelerating and focusing fields suitable for positrons. However, the self-consistent evolution and stability of such asymmetric drivers, which are crucial for sustaining a usable wake over long distances, remain insufficiently understood. In this work, we investigate the evolution modes of wakefield driven by asymmetric electron beams in hollow plasma channels using fully three-dimensional particle-in-cell simulations. We identify two distinct unstable scenarios: a reversal of quadrupole field polarity and continuous penetration of the driver into the plasma wall. By analyzing the transverse dynamics of the driver and the restoring forces provided by the channel ions, we establish simple physical criteria that ensure stable propagation. These results clarify the fundamental constraints governing asymmetric-driver evolution and provide practical guidance for realizing long-lived, quasi-steady wakes in hollow plasma channels.

physics.plasm-ph↗

arXiv2026

World Models for Policy Refinement in StarCraft II

Large Language Models (LLMs) have recently shown strong reasoning capabilities, motivating their use in complex decision-making environments. StarCraft II (SC2), with its massive state-action space and partial observability, is a challenging testbed. However, existing LLM-based SC2 agents primarily focus on improving the policy itself, leaving the integration of a learnable, action-conditioned dynamics model into the decision loop largely unexplored. In this work, we propose StarWM and StarWM-Agent, and conduct the first systematic study of the learnability and decision utility of player-view, action-conditioned textual world models for SC2. StarWM predicts short-horizon future observations under partial observability. StarWM-Agent integrates StarWM into a lightweight Generate-Simulate-Refine loop for foresight-driven policy refinement. Extensive experiments show that StarWM substantially outperforms zero-shot baselines across multiple dimensions, while StarWM-Agent achieves consistent win-rate gains of 30%, 15%, and 30% against the SC2 built-in AI at Hard (LV5), Harder (LV6), and VeryHard (LV7), respectively. Additional analyses show that our method is complementary to existing history-summarization SC2-agent approaches, with their combination achieving the strongest online performance in our evaluation.

cs.AI↗

arXiv2026

Topological textures and emergent altermagnetic signatures in ultrathin BiFeO3

Magnetoelectric multiferroics, materials with intrinsically coupled electric polarization and magnetic order, promise ultralow-power switching, nonvolatile memory, and energy-efficient signal transduction. Yet practical deployment demands ultrathin films down to the atomic limit, where both orders typically degrade. Maintaining both order parameters at the thinnest scales in complex oxides remains a tremendous challenge, as uncompensated bound charge drives nanoscale depolarization in most ferroelectrics, while off-stoichiometry, reduced anisotropy, and charge transfer can produce magnetic dead layers in ultrathin oxides at substrate interfaces. Here, we realize a multiferroic phase of BiFeO3 that not only sustains both order parameters at room temperature with no dead layer but also exhibits signatures of emergent altermagnetism in the four-unit-cell, ultrathin limit. First-principles calculations, spin symmetry analysis, atomic-resolution imaging, and angle-resolved magnetic imaging reveal that short-circuit electrostatic boundary conditions, together with epitaxial strain, drive a continuous second-order, thickness-driven phase transition that enables the formation of multiferroic topological textures. Moreover, the imposed boundary conditions stabilize a d-wave altermagnetic time-reversal symmetry breaking, with corresponding signatures observed in magnetic circular dichroism. Collectively, these results establish a pathway to stabilize unconventional multiferroicity at device-relevant thicknesses, reframing scaling limits for oxide electronics.

cond-mat.mtrl-sci↗

arXiv2025

Speech-Aware Long Context Pruning and Integration for Contextualized Automatic Speech Recognition

Automatic speech recognition (ASR) systems have achieved remarkable performance in common conditions but often struggle to leverage long-context information in contextualized scenarios that require domain-specific knowledge, such as conference presentations. This challenge arises primarily due to constrained model context windows and the sparsity of relevant information within extensive contextual noise. To solve this, we propose the SAP$^{2}$ method, a novel framework that dynamically prunes and integrates relevant contextual keywords in two stages. Specifically, each stage leverages our proposed Speech-Driven Attention-based Pooling mechanism, enabling efficient compression of context embeddings while preserving speech-salient information. Experimental results demonstrate state-of-the-art performance of SAP$^{2}$ on the SlideSpeech and LibriSpeech datasets, achieving word error rates (WER) of 7.71% and 1.12%, respectively. On SlideSpeech, our method notably reduces biased keyword error rates (B-WER) by 41.1% compared to non-contextual baselines. SAP$^{2}$ also exhibits robust scalability, consistently maintaining performance under extensive contextual input conditions on both datasets.

cs.CL↗

arXiv2025

Universal quantum phase classification on quantum computers from machine learning

The classification of quantum phases of matter remains a fundamental challenge in condensed matter physics. We present a novel framework that combines shadow tomography with modern time-series machine learning models to enable efficient and practical quantum phase classification. Our approach leverages the definition of quantum phases based on connectivity through finite-depth local unitary circuits, generating abundant training data by applying Haar random evolution to representative quantum states for a given phase. In this way, the training data can be efficiently obtained from a quantum simulator. Additionally, we demonstrate that advanced time-series models can be used to process the training data and achieve universal quantum phase classification that does not rely on local order parameters. To validate the universality and versatility of our method, we test the model against one-dimensional quantum spin chains such as the Ising model and the axial next-nearest-neighbor Ising (ANNNI) model, showing excellent agreement with known phase boundaries.

quant-ph↗

arXiv2025

Low-energy domain wall racetracks with multiferroic topologies

Conventional racetrack memories move information by pushing magnetic domain walls or other spin textures with spin-polarized currents, but the accompanying Joule heating inflates their energy budget and can hamper scaling. Here we present a voltage-controlled, magnetoelectric racetrack in which transverse electric fields translate coupled ferroelectric-antiferromagnetic walls along BiFeO3 nanostrips at room temperature. Because no charge traverses the track, the switching dissipates orders of magnitude less energy than the most efficient spin-torque devices with more favourable scaling, making the scheme significantly more attractive at the nanoscale. We further uncover noncollinear topological magnetoelectric textures that emerge at domain walls in BiFeO3, where the nature of these topologies influences their stability upon translation. Among these are polar bi-merons and polar vertices magnetoelectrically coupled with magnetic cycloid disclinations and previously unobserved, topological magnetic cycloid twist topologies. We observe domain wall velocities of at least kilometres per second - matching or surpassing the fastest ferrimagnetic and antiferromagnetic racetracks and approaching the acoustic-phonon limit of BiFeO3 - while preserving these topologies over tens of micrometres. The resulting high velocity, low-energy racetrack delivers nanosecond access times without the thermal overhead of current-driven schemes, charting a path toward dense, ultralow-power racetrack devices which rely on spin texture translation.

cond-mat.mtrl-sci↗

arXiv2025

Standardized test of many-body coherence in gate-based quantum platforms

Quantum coherence is a crucial resource in achieving quantum advantage over classical information processing, and more generally developing new quantum technologies. While its effects are observable in current quantum platforms, there are no standardized tools for systematically measuring and quantifying multi-qubit coherence across different gate-based quantum hardware. In this work, we propose a method to define a many-body quantum coherence length scale using anyon interference effects in a spin-chain setup, which effectively mirrors the problem of a quantum particle on a ring, with or without flux through it. We propose using the maximum length of the ring for which the presence or absence of flux can be clearly discerned, as a simple measure of the many-body quantum coherence grade (Q-grade) in a given quantum hardware. We demonstrate how this approach can be implemented on gate-based quantum platforms to estimate and compare the quantum coherence of current devices, such as those from Google, IBM, IonQ, IQM, and Quantinuum that we considered here. This work aims to contribute to the creation of a live Web interface where the latest developments and advancements can be demonstrated, and progress in quantum coherence resources tracked over time. Establishing such a standardized quantum test would enable monitoring the growth of quantum coherence in gate-based quantum platforms, in a spirit similar to Moore's law.

quant-ph↗

arXiv2025

Noise-strength-adapted approximate quantum codes inspired by machine learning

We demonstrate that machine learning provides a powerful tool for discovering new approximate quantum error-correcting (AQEC) codes beyond conventional algebraic frameworks. Building upon direct observations through hybrid quantum-classical learning, we discover two new 4-qubit amplitude damping codes with an innovative noise-strength-adaptive (NSA) feature where the codeword varies with noise strength. They are NSA self-complementary and NSA pair-complementary codes. We show that they can both outperform conventional codes for amplitude damping (AD) noise. The 4-qubit self-complementary NSA code outperforms the standard LNCY AD code in fidelity and Knill-Laflamme condition violation. The pair-complementary code, which has no known non-NSA analog, achieves even better performance with higher-order loss suppression and better fidelity. We further generalize both approaches to families of NSA AD codes for arbitrary system size, as well as an NSA variant of the 0-2-4 binomial code for single-photon loss. Our results demonstrate that adaptation to noise strength can systematically lead to significant improvements in error correction capability, and also showcase how machine learning can help discover new valuable code formalisms that may not emerge from traditional design approaches.

quant-ph↗

arXiv2024

Entanglement area law and Lieb-Schultz-Mattis theorem in long-range interacting systems, and symmetry-enforced long-range entanglement

We establish multiple interrelated, fundamental results in quantum many-body systems that can have long-range interactions. For a sufficiently long quantum spin chain, we first show that if the multi-spin interactions in the Hamiltonian decay fast enough as their ranges increase and the Hamiltonian is gapped, then the ground states satisfy the entanglement area law, even if there is a ground state degeneracy due to a spontaneously broken discrete symmetry. This area law also holds for certain excited states. Second, if such a long-range interacting Hamiltonian has an anomalous symmetry, then the Lieb-Schultz-Mattis theorem applies, i.e., the Hamiltonian cannot have a unique gapped symmetric ground state. If the Hamiltonian contains only 2-spin interactions, these results hold when the interactions decay faster than $1/r^2$, with $r$ the distance between the two interacting spins. Third, we show that pure states with an anomalous symmetry, which may not be a ground state of any natural Hamiltonian, must be long-range entangled. The symmetries we consider include on-site internal symmetries combined with lattice translation symmetries, and they can also extend to purely internal but non-on-site symmetries. Moreover, these internal symmetries can be discrete or continuous. We explore the applications of these results through various examples.

cond-mat.str-el↗

arXiv2024

Non-volatile spin transport in a single domain multiferroic

Antiferromagnets have attracted significant attention in the field of magnonics, as promising candidates for ultralow-energy carriers for information transfer for future computing. The role of crystalline orientation distribution on magnon transport has received very little attention. In multiferroics such as BiFeO$_3$ the coupling between antiferromagnetic and polar order imposes yet another boundary condition on spin transport. Thus, understanding the fundamentals of spin transport in such systems requires a single domain, a single crystal. We show that through Lanthanum(La) substitution, a single ferroelectric domain can be engineered with a stable, single-variant spin cycloid, controllable by an electric field. The spin transport in such a single domain displays a strong anisotropy, arising from the underlying spin cycloid lattice. Our work shows a pathway to understand the fundamental origins of spin transport in such a single domain multiferroic.

cond-mat.mtrl-sci↗

arXiv2024

Probing anyonic statistics via Mach-Zehnder interferometry in quantum computers

We introduce a synthetic Mach-Zehnder interferometer for digitized quantum computing devices to probe fractional exchange statistics of anyonic excitations that appear in quantum spin liquids. Employing an IonQ quantum computer, we apply this scheme to the toric ladder, a quasi-one-dimensional reduction of the toric code. We observe interference patterns resulting from the movement of `electric' excitations in the presence and absence of `magnetic' ones. We model the noise in IonQ via depolarizing Lindbladian dynamics, and find quantitative agreement with the measurements obtained from the quantum device. The synthetic Mach-Zehnder interferometer can thus also serve as an effective means to probe the coherence length and time scales of multi-qubit noisy quantum devices.

quant-ph↗

arXiv2024

Designed spin-texture-lattice to control anisotropic magnon transport in antiferromagnets

Spin waves in magnetic materials are promising information carriers for future computing technologies due to their ultra-low energy dissipation and long coherence length. Antiferromagnets are strong candidate materials due, in part, to their stability to external fields and larger group velocities. Multiferroic aniferromagnets, such as BiFeO$_3$ (BFO), have an additional degree of freedom stemming from magnetoelectric coupling, allowing for control of the magnetic structure, and thus spin waves, with electric field. Unfortunately, spin-wave propagation in BFO is not well understood due to the complexity of the magnetic structure. In this work, we explore long-range spin transport within an epitaxially engineered, electrically tunable, one-dimensional (1D) magnonic crystal. We discover a striking anisotropy in the spin transport parallel and perpendicular to the 1D crystal axis. Multiscale theory and simulation suggests that this preferential magnon conduction emerges from a combination of a population imbalance in its dispersion, as well as anisotropic structural scattering. This work provides a pathway to electrically-reconfigurable magnonic crystals in antiferromagnets.

cond-mat.mtrl-sci↗

arXiv2023

A Scalable, High-Efficiency, Low-Energy-Spread, Laser Wakefield Accelerator using a Tri-plateau Plasma Channel

The emergence of multi-petawatt laser facilities is expected to push forward the maximum energy gain that can be achieved in a single stage of a LWFA to tens of GeV, which begs the question - is it likely to impact particle physics by providing a truly compact particle collider? Colliders have very stringent requirements on beam energy, acceleration efficiency and beam quality. In this article, we propose a LWFA scheme that can for the first time simultaneously achieve hitherto unrealized acceleration efficiency from the laser to the electron beam of >20% and a sub-one percent energy spread using a stepwise plasma structure and a nonlinearly chirped laser pulse. Three-dimensional high-fidelity simulations show that the nonlinear chirp can effectively mitigate the laser waveform distortion and lengthen the acceleration distance. This combined with an inter-stage rephasing process in the stepwise plasma can triple the beam energy gain compared to that in a uniform plasma for a fixed laser energy thereby dramatically increasing the efficiency. A dynamic beam loading effect can almost perfectly cancel the energy chirp that arises during the acceleration, leading to the sub-percent energy spread. This scheme is highly scalable and can be applied to peta-watt LWFA scenarios. Scaling laws are obtained that suggest electron beams with energy gain of >100 GeV, charge of 2 nC, and with an energy spread <1% can be realized with a high laser pulse to particle beam energy transfer efficiency in a LWFA driven by a peta-watt laser, which could be the basis for a proof of concept of one arm of a future electron-positron collider.

physics.acc-ph↗

arXiv2023

Persistent anisotropy of the spin cycloid in BiFeO3 through ferroelectric switching

A key challenge in antiferromagnetic spintronics is the control of spin configuration on nanometer scales applicable to solid-state technologies. Bismuth ferrite (BiFeO3) is a multiferroic material that exhibits both ferroelectricity and canted antiferromagnetism at room temperature, making it a unique candidate in the development of electric-field controllable magnetic devices. The magnetic moments in BiFeO3 are arranged into a spin cycloid, resulting in unique magnetic properties which are tied to the ferroelectric order. Previous understanding of this coupling has relied on average, mesoscale measurements to infer behavior. Using nitrogen vacancy-based diamond magnetometry, we show that the spin cycloid can be deterministically controlled with an electric field. The energy landscape of the cycloid is shaped by both the ferroelectric degree of freedom and strain-induced anisotropy, restricting the magnetization changes to specific ferroelectric switching events. This study provides understanding of the antiferromagnetic texture in BiFeO3 and paves new avenues for designing magnetic textures and spintronic devices.

cond-mat.mtrl-sci↗

arXiv2023

Probing fractional statistics in quantum simulators of spin liquid Hamiltonians

Recent advances in programmable quantum devices brought to the fore the intriguing possibility of using them to realise and investigate topological quantum spin liquid phases. This new and exciting direction brings about important research questions on how to probe and determine the presence of such exotic, highly entangled phases. One of the most promising tools is investigating the behaviour of the topological excitations, and in particular their fractional statistics. In this work we put forward a generic route to achieve this, and we illustrate it in the specific case of $\mathbb{Z}_2$ topological spin liquids implemented with the aid of combinatorial gauge symmetry. We design a convenient architecture to study signatures of fractional statistics via quasiparticle interferometry, and we assess its robustness to diagonal and off-diagonal disorder, as well as to dephasing -- effects that are generally pervasive in noisy quantum programmable devices. A useful counterpart of our scheme is that it provides a clear test of the `quantumness' of these devices, since the signatures that we are looking for crucially hinge on quantum coherence and quantum interference effects in the system.

quant-ph↗