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Kai Xu

Publications and source records attributed to Kai Xu.

At least 19 recordsLinked to original sources
arXiv2026

Cycle WAM: Coupling Action and Video through Cycle Consistency

Recent advances in video modeling have driven substantial progress in world action models (WAMs). Existing approaches exploit pretrained video priors by decoding actions from latent representations or inferring them from generated videos. The underlying link is causal: robot actions produce changes in the scene, and video observations of these changes provide evidence for inferring the actions that produced them. However, existing training formulations do not by themselves ensure that actions and their predicted visual consequences are consistent, potentially limiting the use of pretrained capabilities. We treat video and action as paired modalities linked by this causal relationship and learn their correspondence through a unified training framework designed to better exploit pretrained video priors. Cycle WAM first augments video-action co-training with bidirectional conversion to reinforce cross-modal correspondence and recover forward and inverse dynamics. It then introduces a differentiable cycle-reconstruction objective to further couple the two mappings. The resulting model supports both forward and inverse dynamics, with the potential to improve performance through more effective use of pretrained video priors. Cycle WAM achieves state-of-the-art performance among comparable methods on LIBERO-Pro and substantially improves DreamZero on DROID. Comparisons with UWM characterize its inverse-dynamics performance and show sharper visual detail in the displayed forward-dynamics predictions.

cs.CV↗
arXiv2026

Nanoscale origin of thermal anisotropy in monoclinic \texorpdfstring{$β$-Ga$_2$O$_3$}{beta-Ga2O3}

Monoclinic $β$-Ga$_2$O$_3$ is a promising ultra-wide-bandgap semiconductor for power electronics, but its low and anisotropic thermal conductivity limits heat removal, making it a compelling model system for nanoscale thermal transport studies. Here, we demonstrate a pronounced nanoscale enhancement of thermal anisotropy and uncover the underlying phonon mechanism by combining complementary thermal and acoustic spectroscopy techniques with first-principles calculations. Anisotropic thermoreflectance thermometry determines the full thermal-conductivity tensor from angular measurements within the (100), (010), and (001) crystallographic planes. Angle-resolved Brillouin light scattering reveals that low-frequency acoustic phonons exhibit only weak velocity anisotropy, insufficient to account for the measured thermal anisotropy. Frequency-resolved ab initio Green--Kubo calculations show that the bulk anisotropy pattern emerges only when the accumulated conductivity includes phonon modes above 4 THz, whereas the sub 3 THz acoustic contributions are nearly isotropic. Extreme-ultraviolet transient thermal grating spectroscopy reveals that reducing the grating period to 28 nm strongly enhances the in-plane thermal anisotropy, most markedly in the (001) plane, where it reaches nearly twice its macroscopic value. Reconstructed and calculated mean-free-path (MFP) accumulation functions link this enhancement to the stronger directional contrast of short-MFP phonons. Together, these results identify high-frequency, short-MFP phonons as the dominant origin of thermal anisotropy in $β$-Ga$_2$O$_3$, while low-frequency phonons attenuate the directional contrast at macroscopic scales.

cond-mat.mtrl-sci↗
arXiv2026

Controlled-Z Gates via Floquet Coupling to Two Auxiliary States in Transmon Circuits

High-fidelity entangling gates are essential for quantum information processing. In transmon circuits, higher excited states provide additional pathways for engineering interactions within the computational subspace. Here we demonstrate controlled-Z (CZ) gates that exploit two noncomputational states, $\ket{02}$ and $\ket{20}$, through selective Floquet coupling. The nontrivial parametric modulation can activate both auxiliary transitions from $\ket{11}$ while suppressing the leading exchange between $\ket{01}$ and $\ket{10}$. Based on this approach, we develop two gate protocols: a nonadiabatic round trip through a bright auxiliary superposition and adiabatic evolution along a zero-energy dark state, both achieving average gate fidelities above $99.99\%$ in numerical simulations. Control of the modulation phase further extends the bright-state protocol to conventional and unconventional geometric CZ gates. Effective-model simulations show that unconventional control improves tolerance to residual $ZZ$ coupling and static qubit-frequency offsets relative to conventional control over the parameter ranges examined. Simulations including neighboring spectator qubits show that the original nonadiabatic bright-state and adiabatic dark-state CZ protocols retain high gate fidelities. This framework offers new routes toward the experimental realization of CZ gates and their geometric implementations.

quant-ph↗
arXiv2026

Register Anything Model for Generalizable and Robust Point Cloud Registration

We present Register Anything Model (RAM), a unified model for generalizable 3D point cloud registration that robustly estimates 6-DoF transformations across diverse domains spanning sensors and environments. Existing registration methods are typically tailored to a single domain, with carefully tuned architectures and hyperparameters, and thus degrade markedly when applied to novel domains. We bridge this gap by training a single model on diverse data from indoor, outdoor, and object domains. A central challenge is the severe geometric discrepancy across domains, i.e., differences in scene scale, sensor noise, point density, and structural patterns, which hinders processing with a unified point-based model. To address this, we introduce a geometry-aware normalization strategy that jointly voxelizes and normalizes point clouds into a standardized geometric space, adaptively based on their geometric properties to preserve salient local structures while standardizing resolution. Built on this normalization, RAM follows a hierarchical coarse-to-fine paradigm, and employs an efficient geometric transformer that models local geometric consistency instead of globally over all points. This design mitigates discretization noise introduced by voxelization, and improves computational efficiency. Extensive experiments on 12 benchmarks demonstrate state-of-the-art performance in terms of inlier ratio and registration recall. Notably, our method outperforms the counterparts trained on each separate domain, showing superior generalization capability.

cs.CV↗
arXiv2026

Reconstructing BPS String Charge Cone in 6d N=(1,0) Supergravity

The tensor branch of six-dimensional $\mathcal N=(1,0)$ supergravity is characterized by a spectrum of BPS strings charged under the two-form gauge fields. Their charges span a cone in the tensor charge lattice, whose extremal rays are generated by primitive BPS string charges that become tensionless at the boundaries of the tensor moduli space. We develop a systematic procedure, based entirely on effective field theory data, to reconstruct the primitive BPS string generators and thereby determine the complete closed BPS string cone of any 6d $(1,0)$ supergravity theory. The key ingredients are the universal {\it H}-string, which allows us to isolate a fixed sector determined by classified LST blocks and gauge anomaly data from the remaining gauge-neutral E-string sector, and a BPS refinement of the Completeness Conjecture that ensures population of the relevant charge rays. We further show that this reconstruction provides new constraints on the low-energy theory and can rule out candidate EFTs that pass all previously known local lattice and anomaly consistency conditions. Separately, for theories admitting geometric tensor bases, we explain how the same reconstruction determines the complete Mori cone of the corresponding Kähler surface.

hep-th↗
arXiv2026

FoLD: Force-Informed Learning for Dexterous Articulated Object Manipulation

Transferring human demonstrations to dexterous robots remains challenging because differences in hand morphology and contact dynamics often cause retargeted motions to fail at producing the intended object behavior. We present \textbf{FoLD}, a framework for learning dexterous manipulation of articulated objects through explicit force guidance. FoLD compute compensatory force fields from human demonstrations together with the robot's current interaction state, yielding a force prior that promotes the demonstrated object motion. This force prior informs a residual policy that adapts retargeted hand motions to the contact requirements of the task. We evaluate FoLD on a public benchmark for articulated object manipulation, where it consistently outperforms state-of-the-art baselines across tasks and embodiments. We further validate FoLD on real dexterous robot platforms, demonstrating successful transfer of human manipulation skills to robot execution. Here is the link of our project page: https://gghgghgghgg.github.io/FoLD-project-page/.

cs.RO↗
arXiv2026

Mechanical trisqueezing in circuit quantum acoustodynamics with SNAIL devices

Continuous-variable quantum information processing with mechanical oscillators requires a uni- versal gate set, including a genuine non-Gaussian evolution. While circuit quantum acoustodynamics (cQAD) experiments have realized bilinear and quartic (four-wave mixing) operations by coupling mechanical modes to transmon qubits, the intrinsic Kerr nonlinearity of these devices precludes access to odd-order effective Hamiltonians such as trisqueezing. Here we propose a cQAD architec- ture in which a mechanical mode, hosted in a high-overtone bulk acoustic-wave resonator (HBAR), is coupled to a superconducting nonlinear asymmetric inductive element (SNAIL). By combining the cubic nonlinearity of the SNAIL with parametric flux driving, we engineer a three-wave mixing interaction that generates trisqueezed mechanical states, producing the characteristic triangular, star-shaped Wigner function with negative regions indicative of non-Gaussianity. We derive the effective trisqueezing Hamiltonian from the full circuit model, identify the drive frequencies and flux modulation required to isolate the desired interaction from competing terms, and quantify the achievable coupling strengths for parameters compatible with current fabrication capabilities. Our results establish a hardware-efficient route to non-Gaussian resource generation in mechanical quantum systems, extending the toolbox of cQAD toward universal continuous-variable quantum computation and enhanced quantum-limited sensing.

quant-ph↗
arXiv2026

TempFit: Panoptic Symbol Spotting via Template Fitting

In this paper, we introduce TempFit, a novel approach for the problem of Panoptic Symbol Spotting on CAD drawings. Existing methods treat symbol spotting as a per-primitive classification or mask-prediction task, with no explicit constraint enforcing that the primitives assigned to one instance form a spatially connected group. As a result, predictions may erroneously merge primitives from distant symbols into a single instance or fragment a coherent symbol across multiple predictions. Motivated by the professional "retrieve-and-transform" CAD design workflow, we reformulate this task as a Template Fitting problem, where each foreground instance is detected by retrieving and aligning a canonical template from a library, enforcing structural completeness by construction. Building on this formulation, we present an autoregressive transformer that unifies template retrieval and pose prediction within a single end-to-end pipeline. The framework treats the template library as an explicit geometric memory, parsing background regions in parallel via learnable background tokens and synthesizing foreground instances autoregressively while attending to template-specific geometry at each step. Experiments on FloorplanCAD and our newly collected IndustryCAD dataset, a more challenging benchmark with complex industrial symbols, heavy occlusion, and wide scale variation, demonstrate state-of-the-art performance with particularly large gains on IndustryCAD. Project page: https://tempfit-project.github.io/.

cs.GR↗
arXiv2026

Experimental Realization of a C-Shunt Flux-Qubit Coupler for Controlled-Z Gates between Transmons

Fast entangling gates and low-interaction idling require distinct operating conditions in superconducting quantum processors. We experimentally realize a transmon-C-shunt-transmon (TCT) device in which a flux-tunable, three-junction capacitively shunted flux-qubit coupler connects two transmon data qubits. The C-shunt coupler provides a flux-tunable, positively anharmonic spectrum for tailoring the interactions between the data qubits. Conditional Ramsey and swap measurements identify an idle region with near-zero residual ZZ interaction strength and suppressed transverse exchange. At this bias, simultaneous single-qubit interleaved randomized benchmarking yields X-gate fldelities above 99.7%, consistent with individual operation. A 45 ns nonadiabatic controlled-Z (CZ) gate achieves an interleaved randomized benchmarking fldelity estimate of 99.23(15)%. Numerical simulations identify coupler energy relaxation as an important limitation on CZ-gate fldelity and indicate that extending the coupler lifetime can improve gate performance. These results demonstrate the experimental feasibility of C-shunt flux-qubit couplers for combining low-interaction idling with fast entangling operations between transmon qubits.

quant-ph↗
arXiv2026

Learning native eddy-resolving ocean dynamics outperforms coarse emulation and downscaling

Eddy-resolving ocean simulations are essential for representing mesoscale dynamics that regulate ocean circulation, energy transport, and climate variability. However, they are costly to generate, store, and share. Artificial intelligence (AI) may help, but it remains unclear whether high-resolution ocean fields can be faithfully represented by evolving ocean states at coarse resolution followed by spatial downscaling, or whether dynamical evolution needs to be learned directly from native high-resolution simulations. We show that directly learning dynamical evolution on a 0.1$^\circ$ grid provides a more faithful representation of mesoscale variability than evolving ocean states on a 1$^\circ$ grid followed by downscaling reconstruction. We developed a full-depth 1$^\circ$ emulator and a native 0.1$^\circ$ sea surface temperature (SST) emulator, both of which remained stable over long-term free-running simulations without significant drift, providing reliable baselines for this comparison. Direct learning at 0.1$^\circ$ preserves mesoscale energy, spectral characteristics, and frontal gradients, whereas the coarse-resolution evolution followed by downscaling approach recovers large-scale patterns but fails to reproduce key mesoscale signatures. We show that the learned mesoscale statistics remain consistent across diverse hardware platforms, supporting the reproducibility and portability of AI-based ocean simulations. These results suggest that downscaling of low-resolution large-scale states alone is insufficient to represent ocean mesoscale variability, supporting the need to learn dynamical evolution directly in a high-resolution state space. These findings highlight the continued importance of high-resolution physics ocean models as a foundation for AI-based ocean simulation, providing dynamically resolved training data for physically faithful high-resolution emulators.

physics.ao-ph↗
arXiv2026

A superconducting qutrit link beyond the qubit limit

Superconducting microwave links have enabled deterministic state transfer and remote entanglement between qubits, but deterministic links have so far operated with an effectively two-dimensional transmitted Hilbert space. Here we demonstrate a superconducting qutrit link between two independently packaged nodes connected by a microwave channel. Each node combines a transmon qutrit, a transmission resonator, and a tunable Purcell-filter interface, allowing the two remote microwave-photon interfaces to be matched in both frequency and bandwidth. We implement two transition-selective photon-mediated operations that transfer the $|e\rangle$ and $|f\rangle$ qutrit components in distinct temporal modes of the same channel. We tomographically characterize arbitrary qutrit-state transfer, obtaining a mean transferred-state fidelity of 83.68% and a qutrit process fidelity of 77.12%, exceeding both the classical qutrit-transfer benchmark and the best possible average fidelity of an effective qubit channel used to transmit an arbitrary qutrit. Using partial-transfer operations, we reconstruct a remote two-qutrit state with negativity 0.730, a tomography-inferred dense-coding capacity of 2.273 bits, and a tomography-inferred Collins-Gisin-Linden-Massar-Popescu (CGLMP) parameter $I_3=2.332$, all beyond the corresponding qubit or local bounds. These results demonstrate a superconducting microwave link that uses the native three-level structure of transmons as a genuine high-dimensional communication resource.

quant-ph↗
arXiv2026

Turbo Harness: Instance-Adaptive Harness Optimization

Automating the search for effective harnesses is an important step toward enabling agents to recursively self-improve. Existing harness optimizations typically produce a single global harness that is applied uniformly across task instances. However, a harness that works well on average may not be optimal for every instance. We introduce Turbo Harness, a framework that can adapt a globally optimized harness to each instance by reusing information generated during the original optimization process. Specifically, Turbo Harness recycles artifacts produced during a completed global harness optimization run, and summarizes them into a structured playbook. We train a harness editor to leverage this prior optimization experience to generate instance-specific patches to the global harness. At inference time, the editor uses the instance and the playbook to construct a tailored harness in which the execution model operates. Through numerical experiments, we show that Turbo Harness consistently outperforms existing harness optimization baselines across seven benchmarks spanning interactive agent tasks, software engineering, and long-horizon terminal tasks.

cs.AI↗
arXiv2026

Is Agent Code Less Maintainable Than Human Code?

Maintainability is a core dimension of software engineering, shaping how code is written, reviewed, and developed over time. While coding agents have demonstrated strong performance on single-issue tasks, it remains unclear how maintainable their code is when future agents build on top of it, potentially leading to compounding downstream effects. We investigate how agent code compares to human code in these maintenance settings, presenting CodeThread, a framework to construct controlled experiments from repository-level coding benchmarks. Applying CodeThread to four frontier coding agents and four benchmarks, we find that agents are less effective at resolving tasks when building on agent code compared to human code, with task resolve rate drops of up to 13.1%. Regression analysis reveals that many traditional software engineering maintainability metrics do not explain this difference. Instead, the clearest signals are subtler behavioral differences in agent code, such as changes to input validation and error handling, along with differences in downstream code size and task difficulty. These findings highlight the need to evaluate these systems not only by immediate task resolution but also by code maintainability, and point to potential sources of downstream errors introduced by agent code.

cs.SE↗
arXiv2026

WeaveData: A Multimodal Data Analysis System with Self-Critiquing and Self-Evolving LLM Plans

Multimodal data analysis, which answers questions over relational tables, text, and images, has attracted growing attention in the data management community. Large language models (LLMs) enable such analysis in natural language by generating analysis plans over relational and semantic operators. However, LLM-generated plans are error-prone: a plan may silently compute something other than what was asked, fail during execution, or return a result that misses the question. This paper presents WeaveData, a multimodal data analysis system with self-critiquing and self-evolving LLM plans. First, WeaveData generates a typed logical plan for each question and critiques it step by step before execution, and it checks the executed result against the question afterwards. Second, WeaveData evolves a plan that fails or misses the question: it diagnoses the failure with the actual data, reuses the results that remain valid, and accumulates planning experience for later questions. Third, WeaveData grounds planning in a metadata knowledge graph of all modalities, clarifies ambiguous questions with the user, and backs every model judgment with evidence in an interactive notebook. We demonstrate WeaveData on two public multimodal datasets.

cs.DB↗
arXiv2026

InterMASH: A Unified Geometric Representation for Grasp Synthesis

Grasp synthesis aims to generate stable and physically plausible hand--object interactions, and has become a fundamental problem in both human hand modeling and robotic manipulation. However, a unified representation across human and robotic hands is still lacking, mainly due to differences in hand morphology and surface modeling. Prior methods typically rely on either contact maps or dense implicit descriptors to represent interaction, but these representations are often incomplete or computationally expensive and redundant. We propose InterMASH, a unified geometric representation that establishes cross-embodiment correspondence using sphere-fixed anchors. At each anchor, low-degree spherical harmonics compactly encode local hand geometry, object geometry, and contact, forming an explicit and interpretable token sequence. Building on this natively tokenized structure, we introduce a conditional Diffusion Transformer that operates directly in the proposed InterMASH representation space and jointly generates hand geometry and contact, improving consistency and physical plausibility. Our method achieves competitive performance with state-of-the-art methods on key physical feasibility metrics in a large-scale ShadowHand benchmark, supports joint training across multiple hands, and shows that cross-embodiment fine-tuning with human grasp data can improve robotic grasp success and diversity. Project page is available at https://inter-mash.github.io/.

cs.RO↗
arXiv2026

Pulse-Level Compilation of Measurement-Free Recovery in Transmon Circuits

Quantum error correction commonly relies on syndrome measurement, decoding, and conditional feedback. We numerically show that the conditional spectrum of an interacting transmon circuit can compile a local recovery rule into a fixed open-loop control cycle. In a four-bit repetition-code ring, the resulting input-independent control slows the decay of logical coherence under Pauli-\(X\) noise and stabilizes logical-one population under data relaxation relative to uncorrected references. Its local, bounded-degree architecture provides a hardware-native framework for extending compiled recovery control to larger quantum networks.

quant-ph↗
arXiv2026

CGGT: Curve-Grounded Geometry Transformer for 3D Parametric Curve Reconstruction

Recovering editable 3D parametric curves from 2D images is a fundamental challenge in computer graphics, bridging pixel-based perception and vector-based CAD modeling. Existing NeRF- and 3DGS-based methods often rely on dense calibrated views, precomputed 2D edge maps, and costly per-scene optimization, limiting their applicability to casually captured real-world inputs. We propose CGGT, a Curve-Grounded Geometry Transformer that directly grounds 3D-consistent 2D curve instances in the image space from sparse, unposed multi-view images. CGGT combines a geometry-aware transformer encoder for multi-view feature learning with a curve-aware masked-attention decoder for cross-view instance association. In a single forward pass, it predicts camera parameters, dense depth maps, and instance-level 2D curve masks, which are then lifted into 3D and refined through a fast parametric optimization stage to recover compact, editable 3D curve primitives. To support structured curve learning, we introduce Wireframe-100K, a large-scale dataset comprising 100,000 CAD models with diverse topologies, realistic multi-view renderings, and accurate parametric curve annotations. Extensive experiments show that our framework achieves substantial improvements in both reconstruction accuracy and efficiency, particularly under challenging sparse-view settings and in separating persistent 3D structural edges from view-dependent image edges caused by silhouettes, textures, and appearance variations. Despite being trained solely on synthetic data, CGGT generalizes well to real-world images, demonstrating its potential for practical CAD-style wireframe reconstruction from unconstrained visual inputs.

cs.CV↗
arXiv2026

Event-triggered Implicit Perturbation for Zeroth-Order Fine-Tuning of Spiking Transformers

Zeroth-order (ZO) optimization estimates gradients using only forward-pass evaluations, making it suitable for fine-tuning non-differentiable, event-driven spiking neural networks (SNNs). However, its deployment on in-memory computing (IMC) accelerators is constrained by the repeated read-modify-write (RMW) operations arising from explicit weight perturbation and the prohibitive hardware footprint of random number generators (RNGs) for statistically independent per-weight perturbations. To address these challenges, we propose an implicit-perturbation ZO (IPZO) architecture in which perturbation sums computed by an event-triggered perturbation generation unit (PGU) are combined with the weighted sums produced by the IMC array, eliminating perturbation-induced RMW operations while preserving weight-stationary execution of IMC. By exploiting spike sparsity, the PGU generates and accumulates perturbation contributions only for spike-activated weight rows, reducing the required row dimension of the RNG array. An address-driven XOR recombination scheme (PGU-XOR) is further introduced to mitigate the spatial correlations caused by direct RNG reuse (PGU-Reuse). The results show that (1) PGU-XOR matches software RNGs in accuracy on Spikingformer/CIFAR-10 (76.41% vs. 76.53%) and perplexity (PPL) on SpikeGPT/WikiText-2 (54.20 vs. 53.23), whereas PGU-Reuse degrades accuracy by 9.56 percentage points and increases PPL by 11.8; (2) implemented in a TSMC 16-nm CMOS technology, PGU-XOR incurs 40.3%-46.0% area and 15.2%-48.9% energy overhead per matrix-vector multiplication relative to PGU-Reuse, yet its faster convergence reduces the total perturbation energy to 0.51x that of PGU-Reuse at iso-accuracy; (3) IPZO reduces the perturbation energy to 0.46x-0.83x that of conventional explicit weight perturbation for a batch size of B=64 and T=4 time steps, with the advantage growing as BT decreases.

cs.AR↗