arXiv subjects

Chen Wang

Publications and source records attributed to Chen Wang.

At least 19 recordsLinked to original sources

arXiv2026

A Control Line Multiplexing Framework for Scalable Quantum Computing

Universal fault-tolerant superconducting quantum computers are expected to require millions of physical qubits; yet extending the prevailing dedicated-control-line paradigm to this scale would exceed the wiring density and cooling-power budget of dilution refrigerators. Microwave frequency-division multiplexing (FDM) revolutionized classical communications by enabling many independent channels to share common transmission infrastructure. Translating this principle to superconducting quantum control, however, requires an all-microwave architecture and experimental validation of frequency-multiplexed gate control. Recent work in quantum error correction has demonstrated microwave-based readout, single-qubit gates and leakage suppression, whereas two-qubit gates remain predominantly driven by unipolar pulses and thus constitute the missing element of an all-microwave architecture. Here, we propose a periodically extensible hybrid multiplexing framework in which all gate operations are frequency-division multiplexed using polychromatic microwave pulses and band-pass filters, thereby reducing the required control-line count by one to two orders of magnitude. In a 1:4 demonstration, we achieve average errors of $(6.4\pm0.2)\times10^{-3}$ for parallel FDM CZ gates and $(10.6\pm0.2)\times10^{-4}$ for parallel FDM $XY(π/2)$ gates, without statistically significant multiplexing-induced degradation. Together with on-chip DC sources, this FDM-based gate-control scheme completes the last missing module in a scalable multiplexed-control architecture toward universal fault-tolerant quantum computing.

quant-ph↗

arXiv2026

Finite-Temperature Magnetism and Longitudinal Spin Transport in Compensated Half-Metal Ferrimagnets with Near-Zero Magnetization

Fully compensated ferrimagnets, characterized by nearly vanishing magnetic moments at zero temperature, perfect spin-polarized electronic structures, and ultrafast spin dynamics, represent a highly promising materials platform for next-generation spintronic devices. By minimizing stray magnetic fields and reducing switching currents, FCFs enable a viable path toward high-speed, low-power spintronic devices. In this work, we present a systematic investigation of the electronic structure, finite-temperature magnetism, and spin-dependent transport properties of the Heusler-type fully compensated half-metallic ferrimagnet Mn3Al in magnetic tunnel junctions. We demonstrate that Mn3Al is a half-metallic FCF with 100% spin polarization and fully compensated magnetization at 0 K. Combining density functional theory with Monte Carlo simulations, we characterize its finite-temperature magnetic properties and show that Mn3Al exhibits a net magnetization below 11.6 emu/cc at 300 K, confirming the robustness of its nearly compensated ferrimagnetic order at room temperature. Furthermore, we predict that the Mn3Al/MgO/Mn3Al MTJ yields a giant tunneling magnetoresistance effect at 0 K, which remains as high as 232% at 300 K. Our results establish Mn3Al and related half-metallic FCFs as excellent candidates for magnetoresistance-based spintronic applications.

cond-mat.mtrl-sci↗

arXiv2026

Ego-CEVR: Learning Cross-Embodiment Visual Representations from Human Egocentric Videos

As egocentric video becomes increasingly available for embodied learning, an important research question is how to turn this low-cost, scalable source of data into robot manipulation capabilities. Egocentric videos naturally provide rich context for high-level task understanding, while real-robot demonstrations with wrist observations and action annotations provide supervision for fine-grained action alignment. We present Ego-CEVR, a data-native framework for hierarchical visual representation learning that assigns distinct learning objectives to global and interaction representations. The global module learns object relations, task state, and temporal progress through future prediction on egocentric video. The Interaction module interprets wrist observations in global context and learns local alignment and contact cues through robot action supervision. Separate residual paths expose both representations to the action policy, preserving direct access to task context alongside fine-grained execution information. Ego-CEVR supports independent pretraining of its global video module on large-scale human egocentric videos and videos from diverse robot embodiments, without requiring robot action labels or paired wrist observations.

cs.RO↗

arXiv2026

Asking the World: Generalist Physical Reasoning through Agentic World Modeling and Probing

Physical reasoning from video requires inferring latent physical properties and dynamics beyond direct observation. Direct VLM inference remains unreliable on complex physical tasks without explicit modeling and validation, while predefined tool pipelines rely on task- and domain-specific priors that limit generalization across materials, dynamics, and reasoning tasks. We introduce Asking the World (ATW), a generalist agent that constructs and interrogates task-relevant executable worlds through two adaptive stages: World Modeling calibrates a world from video, while World Probing queries, simulates, and intervenes on it to obtain question-relevant evidence. Rather than prescribing the operations in either stage, ATW determines how to model and probe according to the scene and question. We develop PolyWorld Engine, a lightweight and highly programmable Warp-based multiphysics simulator for constructing and probing worlds with rigid bodies, soft bodies, cloth, ropes, fluids, and their coupled interactions. CEM-based system identification recovers task-relevant dynamics during World Modeling. The resulting world becomes an active workspace for question-directed physical experiments rather than a predetermined downstream tool. We evaluate ATW on CLEVRER, ContPhy, and three real-world scenarios. Using Gemini-3-Flash as its base VLM, ATW achieves 80.82% overall per-question accuracy on CLEVRER, improving direct Gemini-3-Flash by 46.50 points, GPT-5.5 by 13.58 points, and PhysMind by 8.27 points. On ContPhy, it reaches 70.56% overall accuracy, surpassing Gemini-3-Flash by 28.10 points and GPT-5.5 by 3.53 points. Across the three real-world scenarios, ATW achieves 71.67% accuracy, 28.33 points above GPT-5.5. These results establish agentic world modeling and probing as an effective, execution-grounded approach to generalist physical reasoning. Project page: https://asking-the-world.github.io/

cs.CV↗

arXiv2026

Late Breaking Results: Audio-Language-Action Models with Direct Speech Conditioning for Robotic Manipulation

Vision-language-action (VLA) models enable robots to follow instructions in typed text, limiting broader deployment in natural speech. The traditional solution by prepending a speech recognition before VLA to convert speech into texts would incur additional latency and propagate errors. To address this, we present Speech-$π_{0.5}$, which maps raw speech directly to motor commands without explicit textual transcriptions. Specifically, built on $π_{0.5}$, we use a cross-modal projection to convert voice frames directly into audio tokens with significant response latency reduction. To further improve the accuracy, we adopt split LoRA adaptation with dedicated audio and task adapters, under a two-stage training. Speech-$π_{0.5}$ achieves $3\times$ lower response latency than ASR pipeline while maintaining competitive task success on LIBERO, and generalizes robustly to unseen accents where a single adapter degrades significantly.

cs.CV↗

arXiv2026

Asymptotic spectral properties and extreme eigenvalues of sample correlation matrices from nonstationary high-dimensional VAR processes

We study the limiting spectral distribution and extreme eigenvalues of the sample correlation matrix $\hat{\boldsymbol{R}}$ from a high-dimensional vector autoregressive (VAR) processes. Depending on whether roots of the characteristic polynomial of given VAR lie on (nonstationary) or outside (super-nonstationary) the unit circle, $\hat{\boldsymbol{R}}$ presents different types of asymptotic spectral properties. Under both nonstationary and super-nonstationary regimes, the empirical spectral distribution of $\hat{\boldsymbol{R}}$ degenerates in probability to a point mass at zero. The rescaled extreme eigenvalues of $\hat{\boldsymbol{R}}$ are asymptotically equivalent to the eigenvalues of a finite-dimensional symmetric random matrix determined by the characteristic roots. Moreover, under super-nonstationarity, $\hat{\boldsymbol{R}}$ degenerates in probability to a matrix with finite rank.

stat.ME↗

arXiv2026

Many Perturbations, Shared Failure Dynamics: Tracing Direction-Sensitive Perturbation Influence in Language Models

Why can a task-preserving rewrite turn a correct answer into a failure while a larger internal change leaves the answer intact? We develop a direction-sensitive, readout-relative account of semantic fragility: heterogeneous language changes can share geometric and readout relationships without sharing an input-independent failure direction. Clean decision reserve, representation transformation, and response alignment provide complementary descriptions of this process. An exact longitudinal-transverse decomposition identifies directional information discarded by scalar transmission. A compact reserve-and-transmission model predicts failures across held-out families and items in Qwen3-32B and Gemma 4 31B, with useful but parameterization-dependent transfer to ReClor. Matched interventions show that orientation carries causal information beyond scalar geometry. Among naturally failed rewrites, attention-component tests on disjoint original items and subsequent answer-order controls favor the natural paired direction over tested random and same-answer-letter donor directions. Yet failures rarely persist across option orders, and current-order corrections use recomputed clean references. Shared predictive regularity thus coexists with context-dependent directional effects. Numerical path integration reduces, but does not eliminate, finite-patch approximation error within a fixed context; a larger discrepancy from the natural rewrite effect remains. A complementary fixed-CoT cue study extends this account to influence unacknowledged in recorded reasoning: readout associations transfer across domains, although the positive-mean causal criterion fails and signed-effect prediction remains post hoc. Together, these results support shared relations among reserve, transformation, and response while distinguishing local causal sensitivity from a complete explanation of natural failure.

cs.CL↗

arXiv2026

Side-Chain Engineering Controls Molecular Packing and Vertical Electron Transport in Discrete n-Type NDI-T2 Oligomers

High-efficiency vertical charge transport in multilayer organic optoelectronic devices highly depends on molecular structure and arrangement. Using two T2-(NDI-T2)2 conjugated oligomers with ethylhexyl (EH) and octyldodecyl (OD) side chains as a model system, their effects on electron transport are investigated using, among other techniques, atomic force microscopy (AFM), conductive atomic force microscopy (C-AFM), grazing-incidence wide-angle X-ray scattering (GIWAXS), steady-state optical spectroscopy, and time-dependent density functional theory (TDDFT) calculations. The oligomers of both materials exhibit relatively disordered structures in the as-cast state, whereas annealing enhances structural order and leads to side-chain-dependent changes in molecular stacking. EH-based films show a smaller annealing-induced photoluminescence shift than OD-based films, suggesting less pronounced stacking and conformational rearrangements, consistent with TDDFT-supported exciton analysis. The current-voltage characteristics are analyzed using the space-charge-limited current (SCLC) model. After annealing, both materials exhibit improved vertical electron mobility. The EH-based films reach a maximum mobility of 1.2 x 10^-4 cm^2 V^-1 s^-1. The combined results suggest that the shorter EH side chains modify the out-of-plane packing environment and facilitate vertical electron transport. These results establish a structure-property relationship between side-chain structure, molecular order, exciton coupling and vertical mobility, providing a molecular design basis for optimising organic semiconductors through side-chain engineering.

cond-mat.mtrl-sci↗

arXiv2026

MEgoVista: Multi-view Ego-aware Motion Estimation for Metric 4D Hands and Head in the Wild

Learning manipulation from human video requires high-fidelity hand-motion reconstruction in metric units. Today's metric hand labels come from studio rigs and instrumented headsets, and both are confined in the same two ways: neither leaves a prepared setting, and neither is checked against an independent reference. Unconstrained head-worn recording promises the opposite trade-off, scaling with the number of people wearing a device. We therefore introduce MEgoVista, an offline pipeline that turns a single unprepared MEgo View recording into metric two-hand and head motion in one gravity-aligned world frame. Three properties set it apart from existing egocentric reconstruction systems: first, it reconstructs in settings studio volumes and tabletop rigs cannot reach, settling hand ownership at detection so bystander hands stay out of the wearer's trajectory; second, it takes its metric gauge from calibrated stereo rather than a monocular prior, installing scale at initialisation so policies receive physical units, not arbitrary coordinates; third, both outputs are scored inside a motion-capture volume against independent Chingmu optical capture, under a protocol that audits its own reference and charges what a method declines to predict. MEgoVista is offered as a measured route from egocentric video to metric hand supervision, one that widens where such labels can be gathered. To bridge the gap between lab validation and industrial-scale data annotation, we further introduce MEgoDeploy, a production-grade GPU stack that fully preserves reconstruction fidelity while achieving a 14.05x end-to-end throughput speedup, scaling the generation of metric 4D pseudo-labels for embodied foundation models.

cs.CV↗

arXiv2026

Point-Level Refinable Semantic Mapping for Spatial Understanding

Open-vocabulary 3D semantic maps have emerged as a key spatial memory representation for long-horizon robot autonomy, enabling robots to reason about objects, their spatial relationships, and past observations across time. However, existing object-centric semantic maps are primarily designed for static environments and rely on an append-only point integration strategy, where newly observed points are accumulated but previously integrated points are rarely reevaluated. Consequently, perception errors accumulate over time, and outdated points persist after objects move or disappear. Although recent dynamic mapping methods update object-level representations in changing scenes, they do not explicitly revise noisy or obsolete points within each object. We propose an online refinable semantic mapping framework that continuously refines object representations through probabilistic point-level confidence updating. Each object point maintains a confidence score that is updated online using multi-view temporal evidence: consistent observations reinforce reliable points, while inconsistent segmentation masks, depth measurements, and visibility cues suppress spurious and outdated points. Combined with object tracking and re-identification to preserve instance identities under motion and relocation, our framework enables object-centric semantic maps to be continuously refined rather than monotonically accumulated. Experiments demonstrate improved mapping accuracy over the previous method by 3.46% on Replica and 2.55% on ScanNet, show robustness in dynamic environments and under perception noise, and provide an effective spatial memory for object navigation and embodied question-answering with success rates of 82.4% and 76.7%.

cs.RO↗

arXiv2026

SCOPE-RL: Stable and Quantitative Control of Policy Entropy in RL Post-Training

Reinforcement learning (RL) is a key paradigm for post-training large language models (LLMs), but the widely used Group Relative Policy Optimization (GRPO) often suffers from entropy collapse: exploration quickly disappears, policies converge prematurely, and sample diversity declines, ultimately harming training effectiveness. Existing remedies, including entropy bonuses and clip-based methods, rarely keep entropy within a stable exploration regime and often introduce oscillatory entropy or reward degradation. In this work, we identify a previously overlooked asymmetry in entropy dynamics: under high-temperature sampling, positive and negative samples have opposite effects on policy entropy. Specifically, high-temperature positive samples promote entropy growth, whereas negative samples suppress it. We provide a qualitative theoretical explanation for this phenomenon: when entropy decreases during policy updates, its derivative with respect to temperature is strictly positive under positive sample updates, indicating that high-temperature positive samples can counteract entropy decay, thereby slowing entropy collapse and potentially reversing it. Motivated by this insight, we propose SCOPE-RL, a stable entropy control framework through a regularization term constructed from temperature-adaptive positive samples. Extensive experiments show that SCOPE-RL consistently outperforms strong RL baselines on both Pass@1 and Pass@k. Our results provide evidence that escaping entropy collapse can improve reasoning performance, while our quantitative empirical analysis further reveals a non-monotonic entropy-performance relationship, with an optimal level of exploration for RL post-training in reasoning LLMs. Our code is available at https://github.com/597358816/SCOPE-RL.

cs.LG↗

arXiv2026

Anisotropy and tracer-profile assumptions in aperture-based strong-lens dynamics: Conditional inference from supernova distance ratios and unresolved stellar kinematics

Single-aperture stellar kinematics jointly constrain the mass distribution, tracer light profile, and velocity anisotropy. Their coupling affects the interpretation of an inferred anisotropy. We quantify how tracer-profile assumptions and data treatment affect common-anisotropy comparisons and inferred mass-density slopes in a compiled galaxy-scale lens sample. We analyse 107 lenses using distinct \pantheon\ supernova counterparts, spatial flatness, distance duality, and the full supernova covariance. We compare the individual-slope power-law tracer with Hernquist and Sérsic profiles using measured effective radii. Numerical Jeans solutions, likelihood comparisons, posterior distributions, and controlled recovery experiments quantify the effects of tracer representation. The power-law fixed-minus-free Bayesian information criterion differences are 6.74 for isotropy and 41.51 for fixed $β=0.18$, compared with $-3.89$ and $-2.26$ for Hernquist and $-4.67$ and $-4.30$ for Sérsic $n=4$. The preference also changes with Sérsic index. Removing 12 systems with linearised distance-ratio uncertainty above 20\% reduces the power-law isotropy comparison to 2.51. In isotropic Sérsic $n=4$ and Hernquist simulations, power-law fits yield $Δ\mathrm{BIC}_0>6$ in 60.0\% and 50.0\% of realisations, respectively, compared with 0.5\% and 1.0\% for matched-tracer fits.} {The preference for a common free anisotropy parameter depends on the tracer representation and sample composition. Controlled recovery identifies tracer mismatch as one mechanism that can generate this preference within the adopted aperture models. Physical interpretation requires joint treatment of photometry, distance uncertainties, and kinematic weighting.

astro-ph.CO↗

arXiv2026

AnyNav: Visual Neuro-Symbolic Friction Learning for Off-road Navigation

Off-road navigation is critical for a wide range of field robotics applications from planetary exploration to disaster response. However, it remains a longstanding challenge due to unstructured environments and the inherently complex terrain-vehicle interactions. Traditional physics-based methods struggle to accurately capture the nonlinear dynamics underlying these interactions, while purely data-driven approaches often overfit to specific motion patterns, vehicle geometries, or platforms, limiting their generalization in diverse, real-world scenarios. To address these limitations, we introduce AnyNav, a vision-based friction estimation and navigation framework grounded in neuro-symbolic principles. Our approach integrates neural networks for visual perception with symbolic physical models for reasoning about terrain-vehicle dynamics. To enable self-supervised learning in real-world settings, we adopt the imperative learning paradigm, employing bilevel optimization to train the friction network through physics-based optimization. This explicit incorporation of physical reasoning substantially enhances generalization across terrains, vehicle types, and operational conditions. Leveraging the predicted friction coefficients, we further develop a physics-informed navigation system capable of generating physically feasible, time-efficient paths together with corresponding speed profiles. We demonstrate that AnyNav seamlessly transfers from simulation to real-world robotic platforms, exhibiting strong robustness across different four-wheeled vehicles and diverse off-road environments.

cs.RO↗

arXiv2026

Beyond Static Leaderboards: Capability-Focused Evaluation with Structural Diversity for Time Series Foundation Models

Static public benchmarks for time series foundation models (TSFMs) typically have fixed and narrow test distributions. Their repeated reuse encourages benchmark-specific tuning, making reported performance reflect adaptation to the fixed test set rather than genuine forecasting generalization. In addition, a single aggregate score cannot fully characterize model capabilities across distinct temporal structures and input relationships. We propose CaFE (Capability-Focused Evaluation), a refreshable evaluation extension grounded in original benchmark data that enriches static benchmarks with structural diversity and enriches single-score black-box evaluation with multi-dimensional, capability-oriented assessment. CaFE first profiles the original benchmark and defines eight Structural Features (SFs) that characterize its temporal structure. For each SF, it resamples the original data according to its structural support, removes edge-of-distribution samples, and augments the retained samples by varying the SF's salience, producing samples with different structural profiles. After filtering out out-of-distribution samples while preserving diversity, CaFE yields a refreshed evaluation set. TSFMs are then evaluated on this set using both conventional forecast-error metrics and intervention-response metrics, giving each model a per-feature capability profile alongside endpoint accuracy. Experiments on GIFT-Eval and FEV-Mini20 show that CaFE yields stable evaluation results and reveals per-feature strengths and weaknesses that a single aggregate score hides. Moreover, on genuine data filtered by structural condition, models selected by CaFE outperform those chosen by the aggregate leaderboard. CaFE therefore makes model selection conditional on the structural response of interest.

cs.AI↗

arXiv2026

GPT-6 Astra Is a Native Visuo-Tactile Policy for Contact-Rich Manipulation

Recent advances in multimodal models have enabled GPT-6 Astra to perform robotic manipulation through in-context learning. Yet contact-rich control and tactile perception are often regarded as strengths of specialist policies, leaving open whether Astra can directly understand and use tactile observations. We provide, to our knowledge, the first demonstration that Astra directly interprets vision-based tactile images and uses their contact cues for manipulation. Tactile RGB images enter through its existing visual interface, enabling few-shot closed-loop control on nineteen simulated UniVTAC and OpenViTac tasks with 210 sampled scene seeds per setting. With visuo-tactile input, Astra achieves mean success rates of 93.8% and 81.8%, exceeding the strongest reported specialist baselines in our comparison by 22.9 and 13.1 percentage points, respectively. Vision-only performance also leads these baselines, reaching 76.3% and 80.6%. Trajectory analysis reveals active probing, cautious grasping, contact-guided alignment, and recovery, alongside difficulties with tangential loading and corrective execution. Our findings challenge the exclusivity of specialist policies in contact-rich manipulation and show that fine-grained vision can partially compensate for missing touch. They also identify vision-based tactile sensing as an interface for scaling contact understanding with foundation models and reveal signs of benchmark saturation. These results motivate joint visual-tactile pretraining, harder contact-rich evaluations, and physical-robot validation.

cs.RO↗

arXiv2026

HAVANA: A Hierarchical Video-Action Model for Vision-Language Navigation

Vision-Language Navigation (VLN) requires long-horizon instruction-consistent planning and short-horizon local planning in current geometry. Video-action models (VAMs) offer a promising way to learn predictive representations for navigation. However, existing VAMs jointly predict visual futures and local trajectories in a single process, even though they operate at different paces: future prediction remains stable across multiple updates while local trajectories must react to fresh observations. To bridge this gap, we introduce HAVANA, a dual-system VAM that decouples predictive reasoning from reactive local planning. A slow system produces a compact language-conditioned latent plan from observation history, while a fast system combines the latest plan with the current observation to generate local trajectories. Their asynchronous execution enables rapid adaptation without repeated future prediction, and no future video is generated during inference. Experiments on OmniNavBench demonstrate state-of-the-art navigation performance over prior baselines, while real-world deployments further show robust generalization to unseen environments across different robot embodiments. Our code and checkpoints will be available at https://github.com/Anastasiawd/HAVANA.

cs.CV↗

arXiv2026

Learning neural controllers for nonlinear systems from data

This article addresses the problem of designing neural feedback controllers for unknown nonlinear systems. We propose an indirect data-driven framework that uses offline data to identify the system dynamics, upon which a neural feedback controller and a neural Lyapunov function are jointly synthesized. Input constraints are enforced by integrating a hard-saturation structure into the controller architecture. Robust synthesis conditions are derived to account for data perturbations during identification. Formal stability is certified by combining SMT verification with local Lyapunov analysis near the equilibrium. Numerical examples validate the effectiveness of the proposed framework.

eess.SY↗

arXiv2026

The formation of merging black holes via stable mass transfer in massive binary stars

The vast majority of massive binary systems in the universe is evidently unsuited to produce merging binary black holes. However, several narrow evolutionary paths of isolated massive binaries towards this goal have recently been identified. Due to the high degree of simplification and assumptions applied in previous modelling of these paths, conclusions remained vague so far. For one of these paths, the stable mass transfer channel, we now construct detailed binary evolution models which include internal differential rotation as well as mass and angular momentum transfer between the stars, all the way from the zero-age main sequence to the formation of the black holes, only skipping the rapid late burning stages. This allows us to follow the mass and chemical structure evolution of the mass accreting component, which turns out to have a key influence on the phase of reverse mass transfer, that allows the obtained black hole spins and mass ratios to naturally fall into the regime observed for the gravitational-wave source in the 10--25$M_\odot$ primary black hole mass range. As for this channel, also a large number of progenitor binaries are known, we conclude that it likely contributes to the observed population of gravitational wave sources.

astro-ph.SR↗