arXiv subjects

Zeyu Li

Publications and source records attributed to Zeyu Li.

At least 19 recordsLinked to original sources

arXiv2026

Flexural Altermagnetism

We establish flexural deformation as a versatile route for engineering altermagnetism in two-dimensional collinear antiferromagnets. By tailoring the flexure, we selectively break different symmetries to realize altermagnetic states with distinct wave symmetries and reverse their spin splitting. A tight-binding model and first-principles calculations for monolayer MnPS3 demonstrate d- and i-wave states and a switchable transverse spin current in the d-wave state.

cond-mat.mtrl-sci↗

arXiv2026

Who Takes the Lead? Model Ability and Task-Conditioned Collaboration with Humans

As language-model agents grow more capable, they can carry more of a task without asking anyone for help. Falling reliance on a human partner is easy to read as a failure of collaboration when it may instead be a correct division of labor, and the rate of human involvement alone cannot tell the two apart. We study this systematically by placing an agent and a person under a shared objective while varying who holds the advantage: the human has a clear advantage, neither does, or the agent does. To obtain this control, we introduce a shared-control maze, in which an agent-controlled and a human-controlled avatar share the objective of reaching a common target in as few team turns as possible. The agent allocates each team turn between acting itself and handing the move to the person, and initial placement sets the human's shortest-path advantage while the task is otherwise unchanged. We evaluate GPT~5.6~Sol, Command~A+, Qwen~3.8~27B, and Command~R, first measuring solo capability with a single avatar and full-map observability, then running co-play with human participants across the three advantage conditions. Across the four models, stronger solo performance is associated with selective, route-conditioned human involvement: the models that solve the maze reliably on their own act for themselves almost without exception when the human holds no advantage, and involve the human on roughly $80\%$ of initial decisions when the human route is shorter. Command R, the model with substantially weaker solo performance, shows no such selectivity.

cs.HC↗

arXiv2026

Loopy: Low-Bit Quantization Framework for Looped Language Models

Looped language models provide a parameter-efficient way to scale iterative test-time computation by repeatedly executing a shared recurrent core. Post-training quantization (PTQ) can reduce the memory footprint and inference cost of looped language models, but errors introduced by a quantized shared core affect subsequent cores. Among PTQ methods, channel scaling and orthogonal rotations preserve the floating-point computation while producing representations with different quantization quality. We find that quantization configuration candidate rankings can change with recurrent depth, motivating configuration selection at the target deployment depth. However, evaluating every candidate over the full calibration set at this depth is costly. We therefore propose Loopy, a PTQ framework that formulates shared-core quantization through a recurrent-depth-aware objective, selecting shared low-bit representations by their final prediction loss at the target deployment depth. Channel scaling and orthogonal rotations parameterize the candidate representations. To approximately solve this selection problem efficiently, Loopy progressively allocates calibration windows to promising candidates while preserving complete target-depth execution, using only forward evaluations. Across eight settings, Loopy achieves the state-of-the-art results among different baselines. On Ouro-1.4B under W4A4, Loopy reduces LAMBADA perplexity by 36.5% relative to SpinQuant. Our code is available at https://github.com/Shameless0817/Loopy-review.git.

cs.LG↗

arXiv2026

Optimal Personalized Subspace Learning for Multi-view Tensor Observations

In this work, we model the observed multi-view tensors by decomposing the underlying signal in each view into two components: (i) the shared component that captures common dynamics across all views, and (ii) the private component that accounts for view-wise unique variations. To decouple the shared and private components, we introduce a novel Tucker personalized subspace principal component analysis (TPS-PCA) approach for tensors, which admits a one-step closed-form solution and serves as an ideal surrogate for our extended tensor-version personalized PCA (TP-PCA), adapted from the seminal work by \cite{shi2024personalized}. The theoretical analysis reveals that the proposed TPS-PCA estimators reach the minimax lower bound in terms of view-wise tensor decoupling, whereas the TP-PCA estimators only achieve a rate of average decoupling error across views, which is still slower than that of the TPS-PCA estimators. Extensive numerical experiments are conducted on synthetic and real datasets, demonstrating the wide applicability of the proposed method in fields including power management, financial analysis, and activity recognition.

stat.ME↗

arXiv2026

Conditional Tensor Diffusion: Distributional Counterfactual Learning and Inference

Causal inference guides operational and managerial decisions but remains challenging in high-dimensional panel or tensor settings, where decisions may depend on the joint conditional distribution of missing control outcomes. We develop \emph{Counterfactual Tucker Diffusion} (\CFTDiff), which integrates the treatment mask and latent Tucker structure into conditional diffusion to recover this distribution given observed control outcomes through efficient nonlinear score learning in a low-dimensional core. The masked Tucker score preserves dependence across tensor modes while reducing the dimension of nonlinear score learning from the product of mode dimensions to the much smaller product of Tucker ranks. We establish high-probability error bounds for conditional score estimation that depend on the Tucker ranks, largest mode dimension, and the factor-strength-adjusted number of missing outcomes, and show how these bounds translate into recovery guaranties for the conditional distribution of the missing control outcomes. Across missing rates, simulations show more accurate point recovery than common causal panel and matrix/tensor completion methods; comparisons with nested diffusion specifications further demonstrate the gains from masked conditioning and Tucker dimension reduction. In Norway's iFlex experiment, \CFTDiff recovers missing outcomes more accurately than competing methods; when applied to causal analysis, its estimated conditional distributions yield counterfactual prediction intervals and target-attainment probabilities, allowing pricing interventions to be evaluated by demand-reduction magnitude and reliability.

stat.ML↗

arXiv2026

Energy and Angular-Momentum Redistribution in Hydrogen Migdal Ionization

MARVEL's first direct observation of Migdal ionization in neutron scattering marks an experimental milestone and opens a new avenue for probing electronic response to nuclear recoil. Hydrogen, both the simplest atom and a constituent of its molecular target, provides a controlled benchmark. Within the nonrelativistic sudden approximation, we compute its energy-differential and integrated ionization probabilities with the full recoil phase. At the recoil parameter $β\equiv v_N/(αc)=1.04$, the full-to-dipole spectral ratio rises from $0.43$ when the emitted-electron energy is $1\%$ of the hydrogen binding energy to $5.0$ when it is $2.3$ times that energy. Near $β=10$, partial waves with orbital angular momentum $\ell\ge3$ carry more than $90\%$ of the calculated continuum probability. An independent bound-state-closure evaluation verifies the absolute normalization: at eight recoil values, its ionization probabilities agree with the continuum-integrated results to relative discrepancies below $4.4\times10^{-7}$. These results extend the hydrogen dipole response to the fast-neutron regime and establish energy and angular-momentum redistribution as linked consequences of resolving the recoil phase across an atom.

physics.atom-ph↗

arXiv2026

CMD: An Integrated CGRA Framework with Cluster-Based Distributed Memory Design

Coarse-Grained Reconfigurable Arrays (CGRAs) are a promising solution for achieving high energy efficiency and reconfigurability across various application domains, but their performance is often crippled by rigid memory architectures that limit the number and location of tiles that can access data memory. This creates a significant bottleneck for kernels with intensive memory accesses. To address this, we propose CMD, an integrated CGRA framework featuring cluster-based distributed memory design with a co-designed compilation toolchain. The compiler includes a novel memory-aware mapper and a design space exploration (DSE) mechanism that identifies the optimal memory architecture design for specific kernels. Experimental results show that our post-DSE CMD CGRAs achieve an average speedup of $1.39\times$ over a conventional CGRA while simultaneously reducing the total area to an average of $0.912\times$ of the conventional CGRA.

cs.AR↗

arXiv2026

QUACK: Questioning, Understanding, and Auditing Communicated Knowledge in Multimodal Social Deduction Agents

Social deduction games have become a popular testbed for probing reasoning, deception, coordination, and belief modeling in Large Language Model (LLM) agents. However, most environments are scored only by game outcomes such as win rates and largely remain to text-only interaction, making it difficult to tell whether an agent's language is actually grounded in what it perceived and did, or to identify the failure modes underlying its behavior. To address this gap, we introduce QUACK, an open-source environment and evaluation framework for auditing the grounding of agent language in multimodal social reasoning. QUACK evaluates agents at three levels: game outcomes, behavioral trajectories, and utterance-level consistency. Its core Statement Verification Pipeline reconstructs each agent's ground-truth trajectory from engine logs and checks every discussion claim against it, automatically flagging spatial hallucination, unsupported accusation, deception collapse, and language-action inconsistency. Evaluating three frontier VLMs in both homogeneous and cross-model adversarial settings, we find that even the strongest agent hallucinates 15.1% of its verifiable spatial claims and 11.5% of accusations are strictly unsupported. We release the full engine, evaluation framework, toolkit, and logs in https://github.com/AAAAA-Academia-Attractions/QUACK.

cs.CL↗

arXiv2026

CARE: Privacy-Compliant Agentic Reasoning with Evidence Discordance

Large language model (LLM) systems are increasingly used to support high-stakes decision-making, but they typically perform worse when the available evidence is internally inconsistent. Such a scenario exists in real-world healthcare settings, with patient-reported symptoms contradicting medical signs. To study this problem, we introduce MIMIC-DOS, a dataset for short-horizon organ dysfunction worsening prediction in the intensive care unit (ICU) setting. We derive this dataset from the widely recognized MIMIC-IV, a publicly available electronic health record dataset, and construct it exclusively from cases in which discordance between signs and symptoms exists. This setting poses a substantial challenge for existing LLM-based approaches, with single-pass LLMs and agentic pipelines often struggling to reconcile such conflicting signals. To address this problem, we propose CARE: a multi-stage privacy-compliant agentic reasoning framework in which a proprietary LLM provides guidance by generating structured categories and transitions without accessing sensitive patient data, while a local LLM uses these categories and transitions to support evidence acquisition and final decision-making. Empirically, under controlled retrospective evaluation on MIMIC-DOS, CARE achieves the best overall performance across key metrics among the evaluated LLMs and agentic workflows, showing that it can more robustly handle conflicting clinical evidence while preserving privacy.

cs.CL↗

arXiv2026

Not All Attention Heads Contribute to Critical Visual Token Selection: Head-Aware Pruning Matters More

Vision-Language Models (VLMs) have exhibited impressive performance across diverse visual scenarios. However, this success comes at the cost of explosive growth in visual tokens, which imposes substantial memory and computational overhead during inference, ultimately increasing latency. To improve VLM inference efficiency, a typical class of visual token pruning methods estimates token importance by aggregating attention scores across all heads in the pruning layer of the Large Language Model (LLM) backbone and prunes tokens based on aggregated scores. However, in this paper, we reveal a compelling phenomenon: the capability to pinpoint critical visual tokens is concentrated within a small fraction of heads. Aggregation exclusively on these heads can improve task performance. Inspired by this observation, we propose ProViP, a training-free progressive visual token pruning framework. ProViP first removes redundant visual tokens based on the embedding similarity of input tokens before reasoning of the LLM backbone, and then further prunes tokens during reasoning via head-aware pruning. Experiments demonstrate that ProViP delivers outstanding task performance and inference efficiency. For instance, when applied to LLaVA-1.5-7B, ProViP retains 95.9% of the original performance and achieves 1.62x inference speedup under an 88.9% pruning ratio.

cs.CV↗

arXiv2026

Phase calibration of quantum oscillations in the magnetostrictive coefficient using the topological antiferromagnet YbMnBi$_2$

The Berry phase accumulated along a cyclotron orbit encodes important information about electronic band topology and is commonly inferred from the phase of quantum oscillations. Measurements of the ac magnetostrictive coefficient have recently emerged as a sensitive thermodynamic probe of quantum oscillations, but the phase offset has not been experimentally calibrated. Here, using the topological antiferromagnet YbMnBi$_2$, we calibrate this offset by directly comparing quantum oscillations in magnetization with those in the ac magnetostrictive coefficient. Measurements of both responses on the same single crystal reveal a single fundamental frequency of approximately 160 T in fields up to 14 T, enabling a direct phase comparison free from ambiguities associated with multiple frequencies. We observe an approximately $π/2$ relative phase shift between the two oscillatory responses, consistent with the Maxwell relation linking the magnetostrictive coefficient to the stress derivative of magnetization. Our results establish the appropriate phase needed to extract cyclotron-orbit phase information from quantum oscillations in the ac magnetostrictive coefficient.

cond-mat.str-el↗

arXiv2026

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation

Discrete denoising diffusion models (DDMs) have recently emerged as a compelling alternative to autoregressive (AR) modeling for discrete data, offering parallel generation and iterative global refinement capabilities. Unlike continuous diffusion, where the state space is fixed, DDMs are fundamentally shaped by how the discrete state space is constructed: the tokenization scheme, the vocabulary topology, and domain-specific structural alphabets. This work introduces a unified conceptual framework that views discrete diffusion models through the construction of the underlying discrete state space. Within this framework, existing formulations, including transition-matrix, masking/absorbing-state, and score/ratio-based approaches, emerge as different instantiations of a common design space. The framework further exposes common design trade-offs across training objectives, inference algorithms, scaling behavior, systems optimization, and evaluation protocols, suggesting several promising directions for future research.

cs.LG↗

arXiv2026

Quantum Anomalous Hall Effect in $d^{10}$ Oxide Monolayers

Quantum anomalous Hall effect (QAHE) arises from the interplay between magnetic order and spin-orbit coupling, which opens up a topologically nontrivial band gap to host chiral edge states in the absence of magnetic field. So far, magnetic order of QAHE usually originates from partially filled transition-metal $d$ orbitals or correlation-driven moiré bands. Here, we propose an experimentally accessible family of two-dimensional oxides, M$_2$DO$_6$ (M = Zn, Cd; D = Se, Te), that can realize QAHE from the half-filled O-$2p$ orbital induced spontaneous ferromagnetism. In M$_2$DO$_6$ monolayers, spin-polarized Dirac points appear at K/K$^{\prime}$ valleys and along $Γ$-K/$Γ$-K$^{\prime}$ lines. $C_3$ rotational symmetry then generates eight symmetry-related crossings in the first Brillouin zone. Upon gap opening by spin-orbit coupling, each massive Dirac point contributes half Chern number, resulting in a high-Chern-number QAHE phase with $\mathcal{C}=4$. We establish cation deintercalation as a general strategy to activate O-$2p$ ferromagnetism in oxides. Our finding provides a route to realize QAHE from O-$2p$ ferromagnetism and offers design principles applicable to oxygen-based magnetic topology platforms beyond conventional $d$-electron systems.

cond-mat.mes-hall↗

arXiv2026

PICopilot: An LLM-based Agentic Framework for Assisting Photonic Integrated Circuit Design via Script Generation

The rapid development of photonic integrated circuits (PICs) is shifting the design flow from traditional graphical user interface (GUI)-based methods to script-based methods for higher flexibility, portability, and maintainability. However, script-based design introduces new challenges, requiring designers to possess additional proficiency in tool application programming interfaces (APIs) and programming. It also demands greater effort and time because it is inherently less intuitive and more complex than GUI-based methods. As PICs grow in scale and complexity, the productivity gap between design needs and manual scripting capabilities continues to widen. To address this gap, we introduce PICopilot, the first large language model (LLM)-based agentic framework that assists in PIC design via automated design script generation from natural language instructions. PICopilot leverages a multi-agent architecture with a feedback mechanism and a specifically designed retrieval-augmented generation (RAG) pipeline, achieving a high success rate and reliability. Experimental results on a benchmark of diverse PIC scripting tasks demonstrate that PICopilot successfully completes all 48 tasks and outperforms other LLM-based approaches without incurring substantial extra latency or cost, even solving 21 more tasks than the advanced GPT-5 model with a general RAG pipeline.

cs.ET↗

arXiv2026

Three-Loop Five-Point CK-Dual Amplitudes and UV Structure in N=4 SYM and N=8 SUGRA

We construct the complete full-color three-loop five-point integrand of N=4 super-Yang--Mills theory in a representation that manifestly satisfies color--kinematics duality. Its double copy gives the corresponding N=8 supergravity integrand. For four-dimensional external states, we evaluate the ultraviolet poles of both amplitudes in the critical dimension, Dc=6. Extending the external-state dependence to D dimensions is subtle. We consider a candidate replacement of the four-dimensional prefactors by expressions built from D-dimensional tree amplitudes. It reproduces the open-string prediction for the SYM pole with generic D-dimensional external states, whereas the corresponding gravity expression differs from the string-inspired one by an evanescent term.

hep-th↗

arXiv2026

Structured Scaling of AI Discovery Across Diverse Scientific Domains

Scientific discovery often requires many cycles of proposing, testing, and refining candidate solutions. Language models can increasingly participate in these loops, but simply generating more attempts does not ensure progress: parallel searches may duplicate one another and iterative refinement may become trapped in poor directions. The central challenge is therefore not only to scale AI-driven discovery, but to structure that scaling so that evaluation signals compound over time. Here we introduce SimpleTES (Simple Test-time Evaluation-driven Scaling), a framework that focuses on the structured scaling of AI discovery loops, organizing evaluator queries across independent trajectories, iterative refinement, local candidate selection, and the selective reuse of evaluated histories. Drawing on structural features of scientific communities, SimpleTES uses a single open-source GPT-OSS model to establish new state-of-the-art solutions across 28 open-ended problems in diverse scientific domains ranging from quantum physics and astronomy to biology, AI, and mathematics. These include a 24.5% reduction in quantum circuit compilation overhead, up to 23% lower propulsive cost for deep-space trajectories, a 2.17x faster lasso-path solver, an 8.5% lower-error whole-brain neural-activity predictor, the fastest reported TriMul kernel, and new mathematical constructions beyond prior human or AI records. We further post-train the model for long-horizon discovery by assigning each attempt the final outcome of the trajectory it helped produce. This improves performance on both training and held-out mathematics problems, further advancing the frontier. Together, these results establish structured scaling as a general mechanism for advancing AI scientific discovery.

cs.LG↗

arXiv2026

VersaQ-3D: Architecture Support for Visual Geometry Grounded Transformers via Versatile Quantization

3D reconstruction and view synthesis are fundamental to AR/VR, robotics, and digital twins. The Visual Geometry Grounded Transformer (VGGT) enables strong feed-forward 3D reconstruction while its billion-parameter scale limits on-device deployment. LLM-oriented quantization methods fail on VGGT due to saturated activation channels that resist low-bit quantization and diverse 3D semantics that impede calibration. VGGT further poses hardware challenges from multi-precision architecture support and long-sequence global attention with excessive memory demands. We propose VersaQ-3D, an algorithm-architecture co-design framework for efficient VGGT inference. At the algorithm level, we present the first calibration-free, input-agnostic quantization method for VGGT, leveraging transform coding to suppress outliers and preserve structural weight features, enabling robust low-bit inference down to 4 bits. At the architecture level, we design a reconfigurable accelerator with a hierarchical multi-precision compute unit (BF16/INT8/INT4) that executes both linear and non-linear operators within a shared systolic datapath, reducing end-to-end latency by 77%. A two-stage recomputation-based tiling strategy further cuts runtime by 7% by alleviating on-chip memory pressure for long-sequence attention. Evaluations across various datasets show that VersaQ-3D incurs negligible accuracy loss at W4A8 and consistently achieves leading accuracy at W4A4 over prior quantization methods across diverse scenes. The co-designed accelerator delivers 5.4$\times$-22.0$\times$ speedup over edge GPUs and 2.2$\times$-3.0$\times$ over prior quantization-based accelerators under iso-PE-area comparison, enabling instant and energy-efficient feed-forward 3D reconstruction on edge devices.

cs.AR↗

arXiv2026

AI for Pattern Hunter: Application in Wilson Loop of 2D Lattice Yang-Mills Theory

We employ the Transformer to learn patterns in two-dimensional lattice Yang-Mills theory. Specifically, we represent both Wilson loops and their expectation values as tokenized sequences. Taking the shape of Wilson loops as input, the model successfully predicts expectation values with high accuracy, indicating a meaningful connection between loop geometry and physical results. Our study differs from prior machine learning applications in lattice QCD by emphasizing analytical structures rather than numerical computations. We explore model performance under varying hyperparameters, training data sizes, and sequence lengths. This work serves as a first step toward extending such methods to higher dimensions and inspiring rigorous analytical derivations.

hep-th↗