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Lei Zhang

Publications and source records attributed to Lei Zhang.

At least 19 recordsLinked to original sources
arXiv2026

ResponsibleRobotBench: Benchmarking Risk-Aware Task-Level Decision Making for Robotic Manipulation with Large Multimodal Models

Safe task-level manipulation requires a planner to recognize hazards, select appropriate mitigation actions, and satisfy their execution prerequisites without abandoning the original goal. We introduce ResponsibleRobotBench, a benchmark of 23 manipulation task configurations with separately specified goals and safety conditions. It connects hazard-category predictions, planned actions, execution feedback, and terminal outcomes at the episode level. Across evaluations of five large multimodal models, terminal safety and safe task completion can diverge: defense prompts increase terminal safety for three tested models but affect safe success in different directions. Correct hazard-category predictions also fail to ensure executable mitigation. In a cutting task under the default configuration, GPT-4o mini identifies the human hazard in all 100 episodes but never requests assistance, and every episode ends unsafe. Offering a help action and actually requesting it lead to different comparisons: outcomes differ between action menus even for a model that never requests help, and repeated requests can leave execution failures unresolved. Imitation-learning evaluations and physical robot sequences illustrate the need to coordinate mitigation with goal actions. Together, these results motivate evaluating mitigation selection, action prerequisites, and recovery alongside hazard recognition and terminal outcomes. The appendix provides task definitions, checker conditions, and prompts. Project website: https://responsible-robot-benchmark.github.io/.

cs.RO↗
arXiv2026

Shallow lunar fractures dominate the long-lasting seismic coda

Lunar seismic records are characterized by long-lasting spindle-shaped coda, which has long been considered to be caused by strong velocity contrasts in a continuous-medium lunar crust; however, simulated waveforms could not well match the observation even with unrealistically strong velocity contrasts, demonstrating its mechanism still unsolved. Here we propose a novel model of coda wave generation with random fractures in the lunar crust, where vacuum fractures prevent seismic wave propagation as insulating boundaries, causing strong persistent reverberation in terms of total reflection among fractures. Our model can easily reproduce the typical characteristics of observed Apollo data even using a limited number of simplified fractures in homogeneous matrix, with envelope correlations over 0.9, much higher than previous studies. Numerical simulations suggest that fractures in the uppermost ~1 km of the crust may dominate lunar seismic scattering. This model enables us to investigate the shallow lunar fracture system using seismic data and to suppress seismic coda for detecting critical phases reflected from deep interiors.

physics.geo-ph↗
arXiv2026

Coordinating Humanoid Loco-Manipulation through Action-Conditioned Latent Foresight

Humanoid loco-manipulation requires coordinated body motion and hand interaction over time, and a plausible-looking command sequence can still drop a held object or leave an interaction incomplete. We present action-conditioned latent foresight, a latent world-action model (WAM) that evaluates proposed command sequences before execution. An action generator proposes joint motion-hand command sequences from vision, language, and the robot state. An action-conditioned endpoint predictor estimates the visual features each sequence prefix reaches and measures progress toward a demonstration-derived subgoal, while a joint temporal prior scores temporal consistency given the robot state and preceding commands. The robot executes the first commands of the best-scoring sequence and replans from the next observation. During training, action consistency regularization reconstructs the first demonstrated command from observed and predicted feature changes, encouraging the predictor to retain action-relevant information. On ten tasks in the humanoid loco-manipulation simulation benchmark SIMPLE, our method achieves 89.7% average success, compared with 73.7% for Psi-Zero and 82.0% for DiT4DiT. On seven real-robot tasks, it achieves 85.7% under standard conditions and 65.7% under unseen object configurations, while GR00T N1.7 achieves 70.0% and 48.6%, respectively. Project page: https://ju6276.github.io/Action-Grounded-Foresight/

cs.RO↗
arXiv2026

QECert: Formal Foundations for Quantum Error-Correction Theory in Lean

Quantum error correction provides the mathematical basis for protecting quantum information against noise. Existing formalizations have established foundations for stabilizer codes and code verification. However, a systematic formal treatment of the asymptotic constructions and fundamental limitations that underpin scalable quantum error correction is still needed. We present QECert, a Lean library providing reusable formal foundations for these theoretical questions. It connects quantum error-correction criteria, stabilizer and Calderbank-Shor-Steane codes, and algebraic and homological constructions within a common framework. Asymptotically good quantum low density parity check codes provide the principal case study. We formalize constructions and asymptotic parameter theorems spanning hypergraph products, balanced products, lifted products, and quantum Tanner codes. Further developments cover Bravyi-Poulin-Terhal bounds and related locality restrictions, constant overhead magic state distillation, and energy barriers and storage errors for quantum memories. QECert establishes a reusable formal foundation for studying the capabilities, resource requirements, and fundamental limitations of quantum error correction, supporting systematic verification and further development of its theory.

quant-ph↗
arXiv2026

Radial Symmetry and Nondegeneracy for Singular Liouville Systems with Small Singular Exponents

We extend the classification (Lin and Zhang, Ann. Inst. H. Poincaré Anal. Non Linéaire \textbf{27}, 2010) to finite-mass singular Liouville systems with sufficiently small positive singular exponents. For symmetric, entrywise nonnegative, irreducible and invertible coupling matrices, every solution whose mass vector lies near a fixed compact subset of the regular mass surface is radial about the singular point and is uniquely determined by its component masses up to dilation. We also establish nondegeneracy modulo dilation. To overcome the failure of the moving-plane argument for positive exponents, we use compactness and a local reduction based on integral identities. The results also hold for nonzero nonnegative exponent vectors and for small mixed-sign vectors satisfying an explicit integral inequality.

math.AP↗
arXiv2026

Quantized valley source in bilayer graphene via adiabatic quantum pumping

We propose a quantized valley source based on adiabatic quantum pumping in bilayer graphene in the presence of both quantum spin Hall and quantum valley Hall effects. By periodically modulating the gate voltages of two quantum dots coupled to a bilayer graphene nanoribbon, the helical edge states enable the generation of a pure valley current. Depending on the geometry of quantum dots, the number of emitted valley pairs either forms quantized plateaus or varies continuously with the pumping amplitude. The quantized plateau structure can be tuned by the static gate voltage and by the coupling between the quantum dots and the ribbon. Quantized valley pairs are robust against Anderson disorder applied in the quantum dots and persist for both zigzag and armchair quantum-dot boundaries. These results demonstrate that adiabatic quantum pumping provides a route toward electrically controllable quantized valley sources.

cond-mat.mes-hall↗
arXiv2026

Context-Shielded Backdoor Attack against Large Vision-Language Models

Large vision-language models (LVLMs) routinely use inference-time visual-token compression to prune hundreds of patch tokens down to a compact budget. Existing LVLM backdoors fire whenever a triggered image appears, whereas prior deployment-triggered attacks activate only when model weights are quantized or pruned. However, conventional LVLM backdoors exhibit nearly 100% attack success rate (ASR) under full-token inference, immediately exposing compromised checkpoints during pre-deployment auditing. We reveal context stripping: surrounding clean visual tokens suppress an embedded trigger at full length, whereas token pruning strips this context away and activates the backdoor. Motivated by this finding, we propose StripWake, a projector-only backdoor that remains dormant under full-token auditing yet activates across unseen compressors. Specifically, StripWake pairs clean-supervised full-token views with target-supervised randomized compressed views, and aligns early visual attention with a frozen benign teacher to preserve clean utility. On LLaVA-1.5-7B across nine unseen compressors, StripWake stays dormant at 0.4% full-token ASR yet achieves 94.0%-99.7% compressed ASR across five primary compressors without CIDEr loss.

cs.CV↗
arXiv2026

Simulation-free Unbalanced Dynamic Optimal Transport with General Growth Penalty

Inferring cellular dynamics from unpaired single-cell snapshots requires modeling both state transitions and population growth or death. Unbalanced dynamic optimal transport (UDOT) addresses this by penalizing growth along transport paths, making the choice of growth penalty a key way to encode biological priors on proliferation and apoptosis. However, existing UDOT solvers either rely on computationally expensive NeuralODE simulations or depend on analytical solutions of conditional paths, restricting their efficiency solely to quadratic penalties, i.e. Wasserstein-Fisher-Rao (WFR) geodesics. To enable an efficient UDOT solver for general growth penalties, we first show that concave growth penalties lead to degenerate solutions where growth and transport are separated. We then introduce \textbf{S}imulation-free \textbf{U}nbalanced \textbf{D}ynamic \textbf{O}ptimal transport (SUDO), a simulation-free framework for UDOT with general non-quadratic convex growth penalties. SUDO learns the conditional paths and transport costs, solves the induced semi-coupling problem, and subsequently leverages unbalanced flow matching to achieve a simulation-free solution. On WFR benchmarks, SUDO matches the accuracy of efficient, analytical solution-driven algorithms while outperforming simulation-based methods in computational speed. Beyond WFR, SUDO supports asymmetric penalties that encode proliferation-dominant priors and produce more plausible trajectories and growth estimates on synthetic and single-cell datasets.

cs.LG↗
arXiv2026

Hierarchy of Electromagnetic Gauge Field Quantum Geometries

The conventional Bloch quantum geometric tensor (QGT) describes electric-dipole geometry for spatially uniform perturbations and vertical Bloch transitions. We develop a broader framework linking quantum geometry to local and nonlocal electromagnetic response in gauge-field space, placing this tensor within a hierarchy of electric and magnetic multipole quantum geometries. The intrinsic antisymmetric adiabatic response in every electromagnetic channel is a Berry curvature in the space of its conjugate fields. The field-dependent projector uniquely fixes the QGT for that channel, pairing the response curvature with a quantum metric. This construction extends to spatially nonuniform perturbations, whose finite momentum transfer induces nonvertical Bloch transitions. We explicitly construct the current--current QGT connecting Bloch states at $\mathbf{k}\pm\mathbf{q}/2$, providing a unified metric and curvature description of nonlocal current response. In the doubled Brillouin zone, tangential derivatives generate conventional momentum-space QGT multipoles, whereas normal derivatives connect finite-momentum geometry to uniform electric- and magnetic-multipole-pair QGTs. We derive this connection through second order in $\mathbf{q}$. The finite-momentum curvature governs the intrinsic nonlocal Hall response, while an inverse-frequency moment of symmetric optical absorption probes the index-symmetric metric sector.

cond-mat.mes-hall↗
arXiv2026

Morse theory and topological degree for resonant mean field equations on Riemann surfaces with boundary

In this paper, we establish Morse theory for the Neumann mean field equation on a compact Riemann surface at $ρ=4πm$, $m\in\mathbb N$. To address the loss of compactness associated with bubbling, we use Bahri's theory of critical points at infinity. Assume that the reduced functionals are Morse and that the leading scale coefficient does not vanish at their critical points. We classify the critical points at infinity arising from interior, boundary, and mixed concentration, compute their Morse indices and local relative homology, and establish deformation lemmas at resonance. We prove that, for $m\geq 2$, every sufficiently negative sublevel is homotopy equivalent to the boundary-weighted barycenter space of order $m-1$. The Euler--Poincaré identity gives the Leray--Schauder degree at resonance without assuming nondegeneracy of the solutions. If all solutions are nondegenerate, we further obtain strong Morse inequalities with explicit Betti numbers over $\mathbb Z_2$. As a consequence, we obtain existence and multiplicity results, as well as criteria for solutions with prescribed Morse index.

math.AP↗
arXiv2026

Rotation Equivariant Mamba for Vision Tasks

Rotation equivariance is an important structural prior for a broad range of vision tasks, particularly in domains such as remote sensing and medical imaging where objects have no canonical orientation, yet it remains notably absent from current Mamba-based vision architectures. Despite the success of Mamba in natural language processing and its growing adoption in computer vision, existing visual Mamba models fail to account for rotational symmetry in their design. This omission renders them sensitive to image rotations, thereby constraining their robustness and cross-task generalization. To address this limitation, we incorporate rotation symmetry, an important geometric prior in many visual domains, into Mamba-based architectures. Specifically, we introduce EQ-VMamba, the first rotation equivariant visual Mamba architecture for vision tasks. The core components of EQ-VMamba include a carefully designed rotation equivariant cross-scan strategy and group Mamba blocks. Moreover, we provide a rigorous theoretical analysis of the intrinsic equivariance error, demonstrating that the proposed architecture enforces end-to-end rotation equivariance under the p4 rotation group. Extensive experiments across multiple benchmarks---including high-level image classification, mid-level semantic segmentation, and low-level image super-resolution---demonstrate that EQ-VMamba consistently improves rotation robustness and achieves superior or competitive performance compared to non-equivariant baselines on most benchmarks, while requiring 40-70% fewer parameters. These results indicate that embedding rotation equivariance not only effectively bolsters the robustness of visual Mamba models against rotation transformations, but also improves accuracy on data exhibiting rotational symmetry, with substantially improved parameter efficiency. Code is available at https://github.com/zhongchenzhao/EQ-VMamba.

cs.CV↗
arXiv2026

Uniform estimates for complex Monge-Ampère equations II: Hermitian manifolds

We establish uniform estimates for degenerate complex Monge--Ampère equations on compact Hermitian manifolds under weak Orlicz integrability assumptions on the densities. Our estimates apply uniformly to Hermitian metrics and to semipositive uniformly non-collapsing reference forms. For arbitrary big reference forms, we obtain uniform relative $L^\infty$-estimates with respect to the extremal function with minimal singularities, and establish continuity of the resulting solutions on the regular locus. As an application, we prove the existence of solutions with minimal singularities to degenerate complex Monge--Ampère equations associated with big forms. The proofs combine local Orlicz estimates with pluripotential-theoretic envelope techniques and uniform control of the Monge--Ampère masses, overcoming the lack of invariance of the total Monge--Ampère mass in the Hermitian setting.

math.CV↗
arXiv2026

Re4: Scientific Computing Agent with Rewriting, Resolution, Review and Revision

Large language models (LLMs) have demonstrated abilities to perform complex tasks in multiple domains, including mathematical and scientific reasoning. In this work, we construct a novel agent framework for solving representative problems in scientific computing. The proposed agent incorporates a "rewriting-resolution-review-revision" logical chain via three reasoning LLMs (the Consultant, Reviewer, and Programmer) in a collaborative and interactive manner. The Consultant module enables knowledge transfer to link problems to professional domain insights, thereby rewriting problem descriptions through text augmentation. The Programmer module generates and executes well-structured code to solve problems. The Reviewer module enables self-debugging and self-refinement through interactive feedback with code runtime outputs. Through the end-to-end review mechanism, the Programmer's executable code undergoes iterative revision. We comprehensively evaluate the framework on typical partial differential equations (PDEs), engineering-oriented flow past a cylinder, ill-conditioned linear systems, and data-driven physical analysis problems. Compared with a single-model approach, this collaborative framework significantly improves the bug-free code generation rate and reduces non-physical solutions, establishing a reliable framework for autonomous code generation from natural language descriptions. The review mechanism improved the average execution success (bug-free code and non-NaN solutions) rate of DeepSeek R1 from 59% to 82%, while ChatGPT 4.1-mini improved from 66% to 87% and Gemini-2.5 improved from 60% to 84%. Automatic code generation and review provide a promising workflow for scientific computing; analysis of solver-development trajectories further reveals recurring implementation difficulties in boundary treatment, array indexing, and the preservation of numerical constraints.

cs.AI↗
arXiv2026

Memorize When Needed: Decoupled Memory Control for Spatially Consistent Long-Horizon Video Generation

Spatially consistent long-horizon video generation aims to maintain temporal and spatial consistency along predefined camera trajectories. Existing methods mostly entangle memory modeling with video generation, leading to inconsistent content during scene revisits and diminished generative capacity when exploring novel regions, even when trained on extensive annotated data. To address these limitations, we propose a decoupled framework that separates memory conditioning from generation. Our approach significantly reduces training costs while simultaneously enhancing spatial consistency and preserving the generative capacity for novel scene exploration. Specifically, we employ a lightweight, independent memory branch to learn precise spatial consistency from historical observations. We first introduce a hybrid memory representation to capture complementary temporal and spatial cues from generated frames, then leverage a per-frame cross-attention mechanism to ensure each frame is conditioned exclusively on the most spatially relevant historical information, which is injected into the generative model to ensure spatial consistency. When generating new scenes, a camera-aware gating mechanism is proposed to mediate the interaction between memory and generation modules, enabling memory conditioning only when meaningful historical references exist. Compared with the existing method, our method is highly data-efficient, yet the experiments demonstrate that our approach achieves state-of-the-art performance in terms of both visual quality and spatial consistency. Project page can be found at https://iguoyanjun.github.io/memorize-when-needed-project-page/.

cs.CV↗
arXiv2026

SWE-Bench ProMax: Benchmarking Agents on Large-Scale Multilingual Code Refactoring

As AI coding agents take on increasingly complex, long-horizon software engineering tasks, existing benchmarks are rapidly saturating and their evaluation quality has come under serious scrutiny: a recent audit found that nearly 60% of unsolved SWE-bench Verified instances contain flawed tests -- either overly narrow tests that reject correct solutions or overly broad tests that check unstated requirements -- and that frontier models can verbatim reproduce gold patches from training data. Code refactoring, which requires coordinated, behavior-preserving changes across many files, offers a substantially harder and more realistic test of agent capability, yet remains underserved by current benchmarks. We introduce SWE-Bench ProMax, an expert-curated, multilingual code refactoring benchmark of 170 instances drawn from real commits across seven programming languages (Python, Java, TypeScript, Go, C, C++, and Rust). Every instance undergoes rigorous, multi-stage curation that directly addresses the quality problems identified in prior benchmarks: issue descriptions are rewritten from scratch to provide precise, unambiguous specifications, and test suites are manually reviewed to remove overly narrow and overly broad tests. Tasks with insufficient complexity or limited cross-file scope are filtered out, yielding a benchmark of challenging, large-scale refactoring tasks that average 11.5 modified files and 261.6 lines of code per instance, substantially exceeding the scale of existing benchmarks. Experiments with frontier models under two agent scaffolds show that the best model achieves only 41.2% resolve rate, confirming that SWE-Bench ProMax presents a meaningful and unsaturated challenge for current AI coding agents. Our benchmark is available at https://huggingface.co/datasets/swe-bench-promax/SWE-Bench-ProMax.

cs.CL↗
arXiv2026

A $B_{1g}$ phonon unlocks $d$-wave superconductivity in the repulsive Hubbard model

Using numerically exact finite-temperature quantum Monte Carlo, we demonstrate that a $B_{1g}$ phonon coupled to the doped $t$-$t'$ repulsive Hubbard model produces genuine $d$-wave superconductivity at a substantial transition temperature, in excess of either interaction alone. The observed trends suggest that stronger Hubbard repulsion may cooperate with the phonon to further raise the pairing scale. The enhancement persists at stronger repulsion within the accessible window, limited by the fermion sign problem, and follows a common trend across phonon frequencies $Ω$ when organized by a retardation scale $2πT/Ω$. We also find a well-defined superconducting dome in doping, echoing a characteristic feature of correlated superconductors. Our results identify the $B_{1g}$ phonon as a plausible microscopic ingredient for robust $d$-wave superconductivity in the Hubbard model and suggest a concrete, physically motivated route toward materials with higher superconducting transition temperatures.

cond-mat.supr-con↗
arXiv2026

S$^2$-RL: Sample-Set Dual Reinforcement Learning for Generative Semantic Segmentation Dataset Distillation

While dataset distillation has witnessed significant advances in image classification, its extension to dense prediction tasks such as semantic segmentation is plagued by two core bottlenecks: bi-level optimization-based approaches suffer from prohibitive computational costs stemming from pixel-wise gradient unrolling, whereas proxy-based generative methods merely infuse visual textures into fixed semantic masks derived from real data, thus failing to condense rich semantic knowledge into a compact set of informative, novel scene generations. To address both bottlenecks simultaneously, we propose a novel Sample-Set Reinforcement Learning (S$^2$-RL) framework for generative semantic segmentation dataset distillation (SSDD). Specifically, S$^2$-RL formulates SSDD as a diffusion model based text-to-image generation paradigm, enabling flexible generation of images with informative, novel semantic distributions via a concise text prompt. Furthermore, we fine-tune a diffusion policy via Group Relative Policy Optimization (GRPO), leveraging a sample-set dual reward paradigm: for individual samples, it enforces semantic alignment with input text prompts and intra-class feature diversity; for sample sets, it maximizes inter-sample semantic distribution diversity by solving a contextual multi-armed bandit problem. These design choices enable S$^2$-RL to distill large-scale semantic segmentation datasets into a compact set of generated samples that encapsulate diverse, informative semantic knowledge without the need of pixel-wise gradient unrolling. Extensive experiments on the ADE20K and COCO datasets demonstrate that S$^2$-RL achieves substantial and consistent improvements over state-of-the-art baselines, establishing a new benchmark for SSDD.

cs.CV↗