arXiv subjects

Jian Zhou

Publications and source records attributed to Jian Zhou.

At least 19 recordsLinked to original sources

arXiv2026

Spin and Charge Photocurrents from Electric-Magnetic Dipole Interference in Centrosymmetric Crystals

A common view regarding even-order nonlinear optical (NLO) effects, such as second-harmonic generation and the bulk photovoltaic effect, is that the breaking of inversion symmetry is necessary. However, this view relies on the assumption that light interacts with materials solely through its electric field. Here, we demonstrate that interference between the electric and magnetic components of the same light field can induce even-order NLO effects even in centrosymmetric materials. Using charge and spin photocurrents as prototypical examples, we present a unified microscopic theory showing how such electric-magnetic dipole interference (EMDI) drives NLO responses in inversion-symmetric crystals. Bilinear in both electric and magnetic fields, these responses scale linearly with light intensity. Combining symmetry analysis with first-principles calculations, we decompose the EMDI-induced photocurrents into shift-like and injection-like contributions and classify their dependence on crystal symmetry, time-reversal symmetry, and light polarization. Notably, we reveal that these EMDI-induced responses in centrosymmetric materials are weaker than conventional responses in non-centrosymmetric materials roughly by a factor of fine structure constant $α\approx [10^{-3}\text{--}10^{-2}] $. Nevertheless, they remain well within experimental reach. Our work thus unlocks NLO functionalities in centrosymmetric materials for e.g., materials characterization, photonics and optoelectronics. This is vital given that many functionally importantly materials, such as cubic Si and cubic $\mathrm{SrTiO_3}$, possess inversion symmetry.

cond-mat.mtrl-sci↗

arXiv2026

TerraVis: Towards Evaluation of World-Grounded Visual Consistency in Text-to-Image Generation via MLLM Workflows

Recent text-to-image models have made substantial progress in photorealism, aesthetics, and text-image alignment. Yet visually appealing images can still violate real-world plausibility, exhibiting malformed object structures, impossible anatomy, physically implausible interactions, or inconsistent spatial relationships. Such failures are not well captured by existing fidelity, aesthetics, preference, or alignment metrics. To address this gap, we introduce TerraVis, a framework for evaluating world-grounded visual consistency in generated images. TerraVis defines a structured taxonomy of world-consistency violations spanning object-, interaction-, and scene-level failures, and employs a multi-stage evaluation framework to identify and quantify them. Given an image, TerraVis first uses an MLLM to assess its eligibility for evaluation, then detects violations across 18 taxonomy-defined types and classifies them as minor or major to derive an overall world-consistency score. Across diverse open-source and proprietary text-to-image models on two widely used benchmarks, TerraVis achieves the strongest correlation with human judgments of world consistency among existing metrics. Our benchmark results further show that models that achieve strong performance on conventional metrics can still exhibit substantial world-consistency failures. These findings highlight world consistency as a complementary evaluation dimension and demonstrate that TerraVis enables systematic quantification, diagnosis, and comparison of such failures. Our code is publicly available at https://github.com/ShyFoo/TerraVis.

cs.CV↗

arXiv2026

Eynard-Orantin Topological Recursion, Moduli-Space Virasoro Constraints, and Semisimple CohFTs

We use Virasoro constraints to prove Eynard's intersection-number formula for Eynard-Orantin topological recursion with several simple ramification points. The key construction couples the higher Weil-Petersson partition functions associated with the individual ramification points through a quadratic differential operator determined by the Bergman kernel. We identify this operator with the quadratic part of Givental's quantized $R$-matrix, thereby obtaining a Virasoro-theoretic proof of the DOSS identification of topological recursion with Givental's formula for semisimple cohomological field theories. Furthermore, while the DOSS identification fixes the distinguished $y$-function, we keep the Bergman kernel/$R$-matrix fixed and allow $y$ to vary, showing that this amounts to shifting the evaluation point of the corresponding ancestor potential.

math-ph↗

arXiv2026

Spectral Curves for Semisimple Conformal Frobenius Manifolds

We construct connected spectral curves for semisimple conformal Frobenius manifolds with flat unit, using the periods of the second structure equation. Eynard-Orantin topological recursion on these curves reproduces the stable ancestor correlators of Givental-Teleman reconstruction. The spectral curves are defined on simply connected domains in the complex plane. We apply the construction to the quantum cohomology of $\mathbb P^n$ for $n\ge2$ at a point, obtaining explicit spectral curve data in terms of Hecke triangle groups and hypergeometric functions. Finally, for $\mathbb P^2$ and $\mathbb P^3$, we express the complete spectral curve data, including the bidifferentials, in classical modular and quasimodular forms.

math-ph↗

arXiv2026

Automating the Design of Embodied Agent Architectures

Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules. This modularity exposes a large architectural design space, but current systems still rely on researcher intuition to choose where information is stored, how observations are processed, and how model calls are connected. Agent Architecture Search (AAS) automates such design for text-domain agents, but has not been systematically evaluated on perceptual embodied agents. We study this transfer. To make embodied architectures searchable, we introduce AgentCanvas on the executor side, representing embodied agents as editable typed graphs with simulator execution and episode-level logs. On the optimizer side, we lift AAS from LLM-call loops to tool-using coding-agent sessions, adapting ADAS and AFlow and introducing Knowledge Distill Loop (KDLoop). Across four embodied executors (MapGPT, SmartWay, ExploreEQA, and VoxPoser), search discovers improvements that persist on episodes never queried during search. KDLoop and AFlow independently discover elevation-tolerant observation processing. KDLoop's implementation also improves success rates over the baseline when transferred to real robots. These improvements are not uniform: some gains remain uncertain or reverse on held-out episodes, and an apparent gain exploits evaluator-side information. The experiments further show how rollout noise, local edit basins, and incomplete use of episode-level evidence constrain search. Together, these results establish the potential of automated architecture search for embodied agents and characterize the challenges of applying it in simulated and real-world environments. Code is available at https://github.com/jianzhou0420/AgentCanvas.

cs.RO↗

arXiv2026

Embodied Agents Take Control: Minimal-Interface Zero-Shot Agents Rival Industrial-Scale Policies in Vision-and-Language Navigation

Autonomous embodied agents must sustain a long decision-making loop of perceiving, acting, verifying, and self-correcting. In trained end-to-end policies and hand-designed workflows, the interaction structure remains externally specified. We study how much of this loop a general-purpose model can direct through a minimal interface. We term this organization agentic embodied control: the reasoning model directly steers every action, keeping reasoning and control aligned. We evaluate fourteen frontier multimodal models as provided by their vendors, with monocular RGB and primitive actions across six navigation and embodied question-answering benchmarks. On 100-episode subsets, the best agents exceed published scores and approach or surpass our human tester. Who directs the loop matters: SmartWay and O2C-Nav using the same strong models achieve substantially lower success rates on R2R-CE. The lightweight mini-swe-agent harness performs comparably to vendor harnesses. Stronger models benefit from optional rather than imposed waypoints; adding SLAM raises R2R-CE success to 88%. Physical trials expose limitations in body awareness, execution feedback, and persistent spatial understanding.

cs.RO↗

arXiv2026

NavGPT-3: Harnessing Context in a Hierarchical Navigation Runtime

Language models trained with long-horizon agentic reinforcement learning can generalize knowledge through reasoning, express precise actions, and pursue goals over many steps, raising the ceiling on what an embodied agent can understand and decide. Physical interaction, however, remains the domain of action policies, which provide dense, low-latency control. We present NavGPT-3, a harness that connects the two models, with an OS-like runtime built above it: reasoning, acting, and monitoring run as threads with their own context, tools, and permissions, while the runtime schedules them and decides which thread controls the robot's motion, so that the robot can react to sudden real-world events through interruption and thread switching. Beneath it, our action policy NavGPT VLA, trained on 19.28M examples, allocates visual tokens using codec allocation, in proportion to scene change; its 8B model alone reaches 76.94 SR on R2R-CE and leads RxR-CE with 82.39 SR. With the complete harness, NavGPT-3 sets the state of the art on R2R-CE (81.51 SR) and, for the first time, brings an autonomous agent to human level: on RxR-CE it matches human followers in success (90.43 vs. 90.4 SR) and path fidelity (78.47 vs. 77.7 nDTW) at 1 min 22 s per episode, versus roughly 3 min for a human. We comprehensively ablate the harness design and the interaction between the two models, showing how tools and the action policy shape the path from language-model reasoning to physical control: when NavGPT VLA executes the route, the reasoning loop shortens and the system's minimum reaction time falls from 3-19 s per language-model decision to 0.5-1 s per action-policy step (1-2 Hz). These results show that designing this embodied interface is central to connecting frontier language-model intelligence with low-level physical control. We will release all models, code, and evaluation records.

cs.RO↗

arXiv2026

Nonrelativistic Optically-Induced Spin Torque in Noncollinear Magnets

Manipulating spin using contactless and non-invasive optical approaches is one of the forefront topic in the modern spintronic science and technology. Prior studies on optically-induced spin reorientation usually scales with spin-orbit coupling strength, setting a limitation on composing heavy elements. In this work, we extend such a limit into nonrelativistic regime by exploring noncollinear magnetic configuration. Using a general spin group-theoretical investigation, we show how the symmetric operators in both spin and lattice spaces control the spin torque and quantum geometry of Bloch wavefunction--including both quantum metric and Berry curvature. We describe their site-resolved spin symmetries and unravel their selection rules. Furthermore, we adopt a Kagomé lattice model and a realistic material family to showcase the local spin torque under light irradiation. Since the nonrelativistic spin splitting usually spans on the order of electron-volt, significantly larger than typical spin-orbit induced splitting, the nonrelativistic light-induced spin torque is concomitantly much stronger. This work extends the previous opto-spintronics from relativistic to nonrelativistic limit, significantly broadening its realization platform.

cond-mat.mtrl-sci↗

arXiv2026

Enhancing Quasi-Two-Dimensional Electronic Structure in Bi2O2Se via Chlorine Substitution

Bismuth oxyselenide (Bi2O2Se) has emerged as a leading semiconductor candidate for next-generation electronics and quantum phenomena research. However, its strong ionic interlayer bonding of Se layers fundamentally restricts the full potential of this otherwise promising quasi-two-dimensional (2D) material. In this work, we demonstrate chlorine substitution as an effective strategy to introduce van der Waals gaps and modify the electronic structure of the system. Through comprehensive transport and angle-resolved photoemission spectroscopy (ARPES) measurements on Bi6O6Se2Cl2 single crystal, an optimal Cl-doping compound, we show that the key electronic features, including indirect band gap, high electron mobility, and carrier effective mass, are well preserved, while the 2D character of the system is sufficiently enhanced. First-principles calculations attribute this selective tunability to the unique spatial separation between conductive Bi-O layers and dopant Se/Cl layers, which enables independent optimization of interlayer coupling and intralayer transport. Remarkably, our theoretical calculations combined with experimental observations project a broad bandgap engineering across a wide range (0.8 ~ 3.5 eV) within the Bi2O2Se system. Our findings not only expand the application potential of Bi2O2Se-based materials in optoelectronics, nanoelectronics, and fundamental research but also highlight spatial decoupling as a viable strategy for modifying detailed electronic structure of quasi-2D quantum systems.

cond-mat.mtrl-sci↗

arXiv2026

Quantifying TMD factorization breaking at tree level

Rogers and Mulders identified color entanglement as a mechanism that breaks transverse-momentum-dependent (TMD) factorization in nearly back-to-back dijet production. Using the double-Sivers asymmetry in their model, we quantify the contribution of color entanglement in the intermediate region $Λ_{\rm QCD}\ll q_T\ll P_T$, where $q_T$ is the transverse-momentum imbalance of the dijet and $P_T$ is the hard transverse momentum. We compare the collinear twist-three result with the TMD-factorization prediction, expressing both in terms of the Qiu--Sterman functions. In the absence of color entanglement, the factorized prediction would involve the Sivers functions of semi-inclusive deep-inelastic scattering (SIDIS). Diagrams without crossed attachments reproduce the hard coefficient obtained from the perturbative tails of these Sivers functions. Crossed diagrams, in which each coherent gluon attaches to the other hadron's quark line, contribute an extra term equal to $4/(N_c^2-1)$ times this coefficient.

hep-ph↗

arXiv2026

Nucleon Tomography with 0-jettiness

We propose a novel strategy to systematically isolate the nucleon's intrinsic non-perturbative three-dimensional structure by employing 0-jettiness to suppress initial-state radiation in transverse-momentum-dependent observables. Applying this method to transverse single spin asymmetries (SSAs) in $W^\pm$ and $Z^0$ boson production at RHIC, we demonstrate a substantial enhancement of the asymmetry signal (e.g., by $115\%$ for $W^-$ SSA at $q_\perp=5$ GeV). We show that this enhancement yields a substantial net gain in experimental sensitivity -- even after accounting for the statistical cost of the veto -- facilitating a more definitive test of the predicted Sivers function sign change. We further explore its applicability to spin-dependent measurements at the Electron-Ion Collider. Our analysis is formulated within a joint resummation framework that systematically resums large logarithms associated with both the veto scale and the gauge boson's transverse momentum.

hep-ph↗

arXiv2026

From Automated Simulation to Autonomous Discovery: A Hierarchical Framework for Agentic Computational Materials Science

The convergence of large language models, materials-specific foundation models, and agentic artificial intelligence is reshaping the paradigm of computational materials discovery. While high-throughput computation, automated workflows, and data-driven modeling have greatly expanded the scale of materials exploration, the core scientific decision-making loop remains largely human-directed. Agentic AI introduces the possibility of systems that can autonomously reason about materials objectives, execute simulations, and refine strategies. However, the rapid emergence of such systems has created a critical need for a unified and operational framework to define, evaluate, and guide scientific autonomy in computational materials discovery. In this Perspective, we propose the Computational Materials Agent Autonomy Level (CMA-AL) framework, a hierarchical taxonomy defining six levels of autonomous agency in computational materials science: scripted excecutor, LLM-assisted operator, adaptive explorer, experiment-ready modeler, agentic digital twin, and self-extending intelligence. We further map emerging agentic systems onto the framework and identify key scientific and technological challenges toward higher autonomy. CMA-AL provides a common language for characterizing agentic computational materials discovery, evaluating the maturity of emerging systems, and guiding their evolution toward increasingly autonomous materials discovery.

cond-mat.mtrl-sci↗

arXiv2026

Non-global logarithms and fiducial transverse-momentum-dependent observables in deep-inelastic scattering

Extracting the intrinsic transverse-momentum structure of quarks from deep-inelastic scattering data requires separating nonperturbative effects from perturbative radiation, which broadens the measured transverse-momentum distributions. We address this problem in electron--proton scattering, $ep\to eX$, by introducing the fiducial imbalance $\boldsymbol{q}_T$, defined as the vector sum of the scattered electron's transverse momentum and all hadronic transverse momenta within a specified rapidity window. This construction reduces radiative recoil without requiring jet reconstruction or an explicit jet veto. We account for non-global logarithms (NGLs) arising from correlated soft emissions across the acceptance boundary. The azimuthally averaged NGL contribution is resummed to all orders at leading-logarithmic accuracy in the large-$N_c$ limit using Banfi--Marchesini--Smye evolution, while the leading NGL correction to the first azimuthal harmonic is included at $\mathcal{O}(α_s^2)$. For the EIC and EicC kinematics considered, widening the rapidity window increases the cross section at low $q_T$, a region particularly sensitive to nonperturbative transverse dynamics, and reduces the dilution of spin asymmetries by azimuthally symmetric soft recoil. Both the Sivers single-spin asymmetry $A_{UT}$ and the worm-gear double-spin asymmetry $A_{LT}$ increase in magnitude, while the $\boldsymbol{q}_T$ integrated cross section remains unchanged. The unpolarized $\cosϕ$ moment generated by soft gluon radiation is sensitive to NGL effects, with its ratio definition reducing common normalization uncertainties. The absence of jet reconstruction makes this observable particularly relevant at the lower collision energies of EicC, providing a way to study nonperturbative spin--momentum correlations by varying the fiducial acceptance.

hep-ph↗

arXiv2026

NavHarness: Towards Lifelong Embodied Navigation

Frontier models can now perform well on individual embodied navigation tasks through multi-round multimodal reasoning with simple tools. Across successive tasks, however, an agent must also rely on an evolving map and earlier search records, both of which may be incomplete or conflict with new observations. We present NavHarness, a training-free embodied harness towards lifelong navigation that makes memory processing part of the navigation loop. During navigation, its multi-round agentic session draws on maps, task records, and house knowledge, checking them against observations and recording corrections to guide its actions. NavHarness preserves this experience across fresh conversations for new tasks or recovery attempts, while outcome verification and run-end summaries support its later reuse. On GOAT-Bench, NavHarness improves s-SR over context-only independent sessions by 18.6 points with Astra and 22.6 with Opus 5. Using SLAM-estimated poses, NavHarness with GPT-6 Astra achieves state-of-the-art task success of 83.7 s-SR with 36.9 e-SR on GOAT-Bench and 85.9 s-SR on IR2R-CE. To understand these gains, we examine how experience is carried between sessions and find that structured recovery handovers outperform length-matched summaries. In extended deployments across houses, consolidation improves navigation beyond retaining maps and task records, with case studies showing how agents use earlier experience to interpret new goals, investigate unresolved questions, and resume failed searches. We suggest that progress towards lifelong navigation depends on how successive reasoning sessions build on prior experience, alongside improvements in single-task capability.

cs.RO↗

arXiv2026

PulseInfer: I/O-Centric Sparse KV Cache Offloading for Efficient Long-Context LLM Decoding

Long-context LLM serving is increasingly bottlenecked by decode, where large KV caches limit batch size and underutilize GPUs. Sparse KV cache offloading expands effective capacity by storing most historical KV blocks in CPU DRAM and recalling only selected blocks on demand. However, we find that existing offloading systems shift the bottleneck to CPU-GPU recall I/O: recall volume varies widely across layers, decode steps and requests, while headwise sparse selection fragments recalls into many small PCIe transfers. This paper presents PulseInfer, an I/O-centric sparse KV cache offloading system. PulseInfer hides variable recall latency with interruptible layer-wise scheduling, adapts offloading decisions with IO-Adaptive Offloading Admission, and coalesces fragmented transfers using SoloHead sparse selection and a gather-scatter I/O engine. Implemented on SGLang, PulseInfer improves decode throughput by up to 4.7x over SGLang and 2.6x over the best existing offloading baseline, while reducing TPOT by up to 76% and preserving near-lossless accuracy.

cs.LG↗

arXiv2026

KITE: KV-Invariant Transformer Expansion for Efficient Agentic LLM Scaling

Scaling a language model is not only a question of final quality: the architectural choice determines how much computation is spent during training, prompt processing, and autoregressive decoding to achieve certain model quality. An ideal model architecture should lower all above computation costs to facilitate scaling to a larger model, while ensure the larger model indeed outperforms smaller baselines. We introduce KV-Invariant Transformer Expansion (KITE), a scaling paradigm that achieves this goal. It trains the model from a smaller size to a larger size (i.e., saving training costs via upcycling), while places newly added parameters in regions that do not affect attention KV. Consequently, during inference, prefilling KV only relies on the smaller part of the model, so the inference costs are saved. As a concrete instantiation, we present Step Scale Transformer (SST), a two-tower decoder in which one tower produces KV and the other reads them. At comparable cumulative training compute, SST, a 67B MoE model with 2.15B active body parameters per decode token, achieves lower training loss than 47B and 63B MoE Transformers with 1.48B and 2.02B active body parameters, respectively, while reducing estimated inference cost by 6.7% and 31.6%.

cs.LG↗

arXiv2026

DeepSeek Elastic Compute (DSec): A Sandbox Infrastructure for Effective Agentic Training at Scale

Large-scale agentic training and evaluation with large language models (LLMs) rely on isolated, stateful execution environments in which models inspect repositories, invoke tools, execute commands, and interact with task-specific services. These workloads create sandboxes in large bursts, span heterogeneous functionality and isolation requirements, retain state across long interactions, and draw from large image corpora with limited reuse. Supporting them therefore requires an elastic execution platform rather than a single sandbox runtime. This report presents DeepSeek Elastic Compute (DSec), a production sandbox platform that exposes FnCall, container, microVM, and full-VM sandbox backends through a unified SDK. DSec coordinates placement and lifecycle management across the cluster, composes environments from independently versioned layers, combines memory sharing, reclamation, and CPU scheduling for high-density execution, and loads image data on demand from Fire-Flyer File System (3FS), a cluster-wide distributed filesystem. DSec is co-designed with the reinforcement learning (RL) framework, decouples stateful rollout execution from preemptible GPU training, coordinates sandbox lifecycle with training to preserve rollout state while reclaiming idle resources, and mitigates agent misbehavior such as reward hacking. A single production-scale unit of DSec spans around 160 nodes, serving about 3 million sandboxes per day; in production, it supports over 380,000 concurrent sandboxes and sustains over 5,000 sandbox creations per second. Our evaluation and deployment experience show that these mechanisms reduce environment setup and image-distribution overhead, improve memory efficiency, and preserve latency-sensitive performance under high-density overcommit.

cs.DC↗

arXiv2026

Anatomically Faithful Artifact Suppression in SENSE Accelerated Brain MRI

Background: Four-fold accelerated sensitivity encoding (SENSE4) can shorten brain MRI acquisition time but may amplify noise and result in residual aliasing artifacts after conventional reconstruction. Purpose: To evaluate whether an image-domain refinement framework can improve the quality of SENSE4 brain MRI while preserving anatomical information for quantitative measurements. Methods: In this prospective paired study, 80 participants underwent fully sampled and four-fold accelerated SENSE T1-weighted MRI. We developed an Anatomy-aware Residual Attention Network (ART-Net) to refine accelerated reconstructions through generalized self-attention and correlation-based residual artifact regularization. Participant-level splitting yielded training, validation, and independent test cohort (45/5/30 participants). The independent test cohort underwent quantitative, segmentation-based, and blinded radiologist assessments of image quality and anatomical preservation. Results: ART-Net demonstrated highly competitive reconstruction performance, achieving the highest peak signal-to-noise ratio (31.03 +/- 2.88 dB) and structural similarity index (0.963 +/- 0.022) among evaluated methods. It also demonstrated improved anatomical fidelity, with numerically highest Dice coefficients for medial temporal structures relevant to atrophy assessment (0.8824 +/- 0.0827) and whole-brain regions (0.8857 +/- 0.0885). Moreover, ART-Net improved gradient fidelity, regional contrast preservation, and radiologist-rated structural quality. Conclusion: ART-Net improved agreement between SENSE4 and fully sampled T1-weighted images in a single-center, held-out test cohort while maintaining segmentation-derived anatomical measurements. These findings suggest that ART-Net may support accelerated brain MRI by improving image fidelity and enabling reliable downstream anatomical analysis.

eess.IV↗