arXiv subjects

Wei Zhu

Publications and source records attributed to Wei Zhu.

At least 19 recordsLinked to original sources

arXiv2026

LAC: Linear and Angular Compliance for Humanoid Whole-body Control

Real-world humanoid tasks involve physical interaction with objects and humans, yet current controllers either reject external forces as disturbances or restrict compliance to limited body links while ignoring angular effects. We present LAC, a general whole-body controller that simultaneously realizes commanded Linear and Angular Compliance for wrenches applied to the upper body. First, we synthesize whole-body compliant responses into a large-scale augmented dataset. Sampled force and couple events are imposed on contact frames extracted from human interaction data. At each contact link, the external force and a virtual torque from the passively yielding kinematic chain drive a virtual admittance under the commanded stiffness. Subsequently, teacher-student reinforcement learning trains a single policy to track the compliant motions under external wrenches. Finally, extensive simulation and real-world experiments demonstrate whole-body compliant responses to wrenches across the upper body, monotonic modulation over the full range of both stiffness commands, and applicability to teleoperated loco-manipulation tasks. Project website: https://lac-humanoid.github.io/

cs.RO↗

arXiv2026

Are Hot Jupiters Tidally Disrupted During Stellar Main Sequence?

Once hot Jupiters (HJs) reach their very close orbits, they are expected to experience orbital decay due to tidal interactions with their host star. However, the strength of tidal dissipation is highly uncertain, and it remains an open question whether HJs are tidally disrupted during the stellar main sequence. A previous study found that HJ hosts have a smaller Galactic total velocity dispersion than their field star counterparts, which they interpreted as evidence of tidal disruption. We revisit this study and find that, after using the more reliable vertical velocity dispersion ($\sigma_W$) as the age indicator and accounting for the heterogeneity and anisotropy of their HJ sample, the kinematic age difference between their HJ hosts and matched field stars is significantly reduced. As an independent check, we collect HJs newly discovered by TESS and find that their $\sigma_W$ is statistically similar to that of matched field stars. We also find no statistically significant $\sigma_W$ difference between the field stars and the theoretically vulnerable ultra-hot Jupiters with $P<2$ d. Our results suggest that, after accounting for systematics in the age--velocity dispersion relation, there is no statistically strong evidence from the stellar kinematics that a large fraction of hot Jupiters around Sun-like stars are tidally destroyed during the stellar main sequence.

astro-ph.EP↗

arXiv2026

Are Near Resonant Multiple-planet Systems from Kepler Young?

Recent studies have claimed that Kepler multi-planet systems hosting near-resonant planet pairs---particularly those near second-order mean-motion resonances (MMRs)---exhibit smaller stellar velocity dispersions than the general population of Kepler planet hosts. Interpreting velocity dispersion as an age indicator, these works concluded that near-resonant systems are systematically younger. We revisit this claim, but we explicitly account for contamination by thick disk stars, which are kinematically hotter and follow a different age-velocity dispersion relation (AVR) than thin disk stars. Using the kinematic criterion to separate thin and thick disk stars, we show that systems classified as having plausible second-order resonant pairs are preferentially hosted by brighter, closer stars and are therefore less contaminated by thick disk stars than the overall sample. After applying a cut to remove probable thick disk contaminants (${\rm TD/D}<0.1$), the vertical velocity dispersion of near-resonant systems becomes statistically indistinguishable from that of the overall Kepler multi-planet sample. We conclude that the apparent kinematic youth of near-resonant systems in Kepler may not be due to a genuine age difference, but rather arises from observational selection effects linked to host star properties and planet detectability. We also comment on the kinematic ages of ultra-short-period planets (USPs).

astro-ph.EP↗

arXiv2026

Bernhard-1: An Eccentric Binary Periodically Obscured by its Misaligned Circumbinary Disk

Bernhard-1 is a proposed KH 15D-like circumbinary disk occultation (CBO) system, but its binary nature and disk geometry have not previously been confirmed. We present new optical and near-infrared spectroscopy together with multi-band photometric monitoring of the system. The radial velocities confirm that Bernhard-1 hosts a highly eccentric binary with $e = 0.80 \pm 0.09$, confirming that the periodic photometric variability arises from occultation by a misaligned circumbinary disk. Joint modeling of the spectra and phase-dependent spectral energy distributions yields pre-main-sequence components with masses of $\sim 1.1\,M_\odot$ and $\sim 0.8\,M_\odot$. Combining stellar isochrones with the measured lithium abundance yields a system age of $\sim$ 10 Myr. Together with the spatial, astrometric, and metallicity properties of Bernhard-1, this suggests that Bernhard-1 is probably a member of the open cluster Dolidze 42. By combining the RV orbit with a semi-transparent occultation-screen model, we infer a disk--binary mutual inclination of roughly $50^\circ$ or $130^\circ$, with the degeneracy arising from the unknown disk rotation direction. This geometric method can be applied to any CBO system once radial velocity monitoring yields an orbital solution. The new light curves deviate from earlier model predictions, consistent with ongoing disk precession, while the phase-dependent H$\alpha$ profiles indicate pulsed accretion near periastron. Bernhard-1 therefore joins KH 15D and Bernhard-2 as a rare spectroscopically confirmed CBO system.

astro-ph.SR↗

arXiv2026

Covert Semantic Transmission in ISAC: Dual-Functional Waveform Design and Rectified Flow-Assisted Recovery

Semantic integrated sensing and communication (ISAC) is envisioned as a promising paradigm for efficient and intelligent connectivity in future wireless networks. However, the open wireless channel exposes the dual-functional waveform to detection, which challenges the joint guarantee of covertness, sensing fidelity, and semantic accuracy. To address the challenge, we propose CoSMIC, a novel covertness-oriented semantic ISAC framework, where the sensing output is embedded into a dual-functional ISAC waveform through semantic modulation. Specifically, a semantic rotation coding scheme is established to map semantic latents onto the pairwise rotation and scaling of Gaussian reference sequences, which satisfies a derived closed-form covertness constraint by a differentiable budget projection. Moreover, the radar performance is analyzed to confirm an invariant matched-filter mainlobe response and a bounded output signal-to-interference-plus-noise ratio (SINR) under the semantic embedding. Subsequently, a reliability-guided rectified flow (RFlow) refiner is designed to effectively reconstruct high-fidelity semantic representations from coarse observations. Simulation results demonstrate that CoSMIC improves the semantic reconstruction quality by 18% over diffusion-based baseline schemes with substantially reduced inference latency under strict covertness constraints, which validates the applicability to practical ISAC scenarios. The source code and video demonstrations are available at https://github.com/LanceAnlan/CoSMIC-covertness-oriented-semantic-ISAC-framework.

eess.SP↗

arXiv2026

Enhancing Relation Modeling with Social Attributes for Social Media Popularity Prediction

Recent studies highlight the critical role of retrieval-augmented mechanisms in social media popularity prediction (SMPP). Although such frameworks have improved SMPP performance by leveraging historical posts, existing methods still suffer from the low retrieval accuracy due to the oversight of relative relationships among UGC instances. To address this limitation, we propose a novel Relation-Enhanced Retrieval-Augmented framework (RE-Rag) that models UGC similarity as a continuous relation jointly driven by semantic content and social attributes. Specifically, RE-Rag employs a Semantic-Attribute Retriever (SAR) to obtain instances aligned in both semantic and social-attribute distributions. Subsequently, we design a Relation-Guided Predictor (RGP): first, cross-attention encodes multimodal features of retrieved instances; then, a relative relation graph is introduced to guide attention weight allocation, forming a Relation-Guided Transformer (RGTs) that dynamically modulate attention weights based on relative attribute relations to capture the interplay between semantics and various social attributes. The refined features are fused with the target instance for popularity prediction. Experiments on three public benchmarks show that RE-Rag consistently outperforms state-of-the-art methods in both prediction accuracy and retrieval efficiency.

cs.MM↗

arXiv2026

Conformal Nature of Quantum Phase Transitions via Fuzzy Three-Sphere Regularization

Conformal field theory (CFT) offers a modern viewpoint for understanding phase transitions. However, directly accessing the conformal algebra and microscopically uncovering the emergent conformal symmetry, especially in higher dimensions, remains a significant challenge. Motivated by recent advances in revealing CFT features via the fuzzy two-sphere, here we generalize this approach to higher dimensions and aim to expose the conformality at the (3+1)-D quantum critical point. We demonstrate this framework by investigating quantum phase transitions belonging to the Ising and Yang-Lee universality classes in a (3+1)-D quantum model (equivalent to a classical four-dimensional system), realized via Landau level projection on the fuzzy three-sphere. By computing the energy spectra at criticality, we explicitly verify the state-operator correspondence, a hallmark of conformal invariance. Together with prior advances, this work establishes a new pathway for the microscopic study of emergent conformality in higher-dimensional phase transitions.

cond-mat.str-el↗

arXiv2026

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields

Compressible physical fields are widely present in the real physical world, but current artificial intelligence lacks an understanding mechanism for the non-differentiable features in compressible physical fields. Addressing the limitations of existing deep learning architectures in handling global non-differentiable features, we propose the Inverse Low-Dimensional Manifold reconstruction framework (ILDM). This framework couples the Non-differentiable Approximation Function (NAF) for capturing non-differentiable features in compressible flows with the Smooth Fluid Reconstruction (SFR) module tailored for smooth fluid regions. Extensive evaluations across 1D and 2D benchmarks, including Riemann problems and double Mach reflection, demonstrate that ILDM significantly outperforms cPINN and R-adaptive DeepONet. Specifically, ILDM achieves superior localization of non-differentiable interfaces and maintains robust super-resolution performance even with low-resolution inputs, establishing a physically consistent and scalable paradigm for data-driven fluid dynamics.

physics.comp-ph↗

arXiv2026

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference

We introduce CausticFlow, a machine learning framework that combines neural controlled differential equations with normalizing flows to infer binary microlensing parameters. This architecture naturally handles irregularly sampled time series and data gaps while flexibly capturing strongly correlated and multimodal posterior distributions. Trained on simulated KMTNet-like light curves, CausticFlow generates posterior samples in a fraction of a second, with maximum-a-posteriori estimates achieving typical precisions of $\sim17\%$ for the mass ratio $q$ and $\sim3\%$ for the projected separation $s$. When used as a proposal distribution for downstream local optimization, the framework improves these precisions to $<5\%$ and $<1\%$, respectively, and recovers model $\chi^2$ for $\sim80\%$ of simulated events. We test the generalizability of the framework on 10 real binary lensing events characterized by higher-order effects, varied cadences, and real-world noise. Despite these mismatches between simulation and reality, CausticFlow successfully recovers the model parameters, light-curve morphology, and lensing geometry for 7 of the 10 events after simple local refinement, achieving precision levels comparable to those found for simulated data in 10 CPU minutes per event. These results demonstrate that CausticFlow acts as a fast and robust proposal engine, bridging the gap between the rapid influx of data and the need for systematic modeling in large-scale microlensing surveys such as Roman, CSST, and ET.

astro-ph.IM↗

arXiv2026

Learning Lax Pairs: Revisiting the Classical Paradigm

A Lax pair $(L,P)$ is sometimes thought of as a structural certificate, in that the spatial operator $L$ carries the spectral data of an integrable system, and its isospectral evolution under $\partial_t L = [L,P]$ encodes the nonlinear dynamics. Yet, experience shows that the correspondence between equations and Lax pairs is much more nuanced than this picture suggests. Equations can admit Lax pairs that fail to encode the expected integrable structure. This paper probes that anomalous corner of the Lax pair landscape through five case studies (the Euler top, the free Schr\"odinger equation, the inviscid Burgers equation, the shallow water system, and the Korteweg--de Vries equation), each illustrating a different way the link to integrability can be distorted. The approach combines analytical calculations with the Sparse Identification of Lax Operators (SILO) framework, which proved useful throughout, in some cases confirming the textbook pair and in others surfacing alternatives worth understanding on their own terms. The recurring lesson across the five cases is that compatibility underdetermines the Lax representation, so that anomalous pairs are regular features of the landscape rather than pathologies. Notably, we show that a spectrally degenerate Korteweg--de Vries Lax pair, classified as fake by standard criteria, still generates the full conservation hierarchy through its operator algebra, which shows that a blunt dichotomy between true and fake Lax pairs can be too reductive.

nlin.SI↗

arXiv2026

Generalization Guarantees for Multi-Input Neural Operator Learning in Sobolev Spaces

We develop approximation and generalization error estimates for multi-input neural operators, with the output error measured in Sobolev norms. In contrast to standard operator-learning settings with a single input function, our framework allows multiple input functions defined on possibly different domains, with different dimensions and Sobolev regularities. The derived rates explicitly quantify the contribution of each input space to the final error bound. In particular, in the balanced regime, the approximation and generalization rates are governed by the interaction between the input dimensions, regularities, and Sobolev orders, while the dependence on the model complexity retains a $\log\log/\log$-type structure. Our analysis provides a general theoretical framework for multi-input operator learning, including Sobolev training, and is applicable to operator learning problems arising from partial differential equations and scientific computing.

cs.LG↗

arXiv2026

LLMZero: Discovering Adaptive Training Strategies for RL Post-Training via LLM Agents

RL post-training strategies are dataset-dependent and reveal a recurring empirical pattern: capacity parameters accumulate monotonically across stages, while regularization parameters predominantly oscillate in response to shifting training dynamics. This distinction highlights a potential flaw in fixed training schedules: by forcing all parameters along rigid paths, they fail to capture the dynamic exploration-exploitation tradeoffs that regularization must track. We uncover this through LLMZero, an agentic system that optimizes training trajectories via tree search by diagnosing pathologies at each checkpoint and proposing coordinated multi-parameter transitions. Across four diverse GRPO tasks, LLMZero discovers strategies that improve over the base model by 9% to 140% and over grid search by 6% to 15% (relative), consistently outperforming random search and a skill-based agent under a matched compute budget. The capacity--regularization asymmetry is consistent across all four tasks, offering a candidate design heuristic for multi-stage training.

cs.LG↗

arXiv2026

ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL

Agentic reinforcement learning (RL) enables LLM agents to improve continuously from environment rewards, yet the resulting policies do not systematically accumulate reusable strategies that generalize across tasks. Modular skills can provide such reusable strategies, yet existing skill-augmented RL methods decouple skill creation from policy optimization, risking adopting skills that conflict with the evolving policy. Inspired by Anthropic's Skill Creator, we introduce ReSkill, an RL-in-the-loop skill creation framework that reconciles skill evolution with policy learning. ReSkill exploits the group-wise structure of GRPO to naturally embed three mechanisms with only marginal additional overhead: (1) an assertion-driven skill creator that diagnoses failures from past experience and proposes conditional, trigger-based skill revisions; (2) within-group rollout sampling that enables controlled comparison of skill versions, capturing which version best supports the policy's ongoing learning; and (3) Thompson Sampling with adaptive discounting to balance exploration and exploitation in skill version selection as the policy evolves. Across several domains, ReSkill consistently outperforms existing memory and skill-based RL methods, with the largest gains on unseen tasks. Analysis of the skill lifecycle shows skills being automatically created, tested, refined, and pruned as the policy improves, demonstrating reconciled skill-policy co-evolution.

cs.AI↗

arXiv2026

Looking for Condensed Gluons: A Cross-Scale Journey from the Deep Structure of Protons to High-Energy Cosmic Rays -- A Mini-Review

Quark-gluon dynamics within protons and high-energy radiation phenomena in the universe are typically regarded as two entirely distinct fields. This paper aims to demonstrate that gluon condensation (GC) may serve as a direct bridge between these two fields. We review three key aspects of GC research: first, the Zhu-Shen-Ruan (ZSR) equation, as a nonlinear evolution equation based on structural symmetry, exhibits self-consistent connections with the DGLAP, BFKL and GLR-MQ-ZRS equations, providing a theoretical foundation for the generation of GC; second, the chaotic solutions and the shadowing-antishadowing synergy inherent in this equation can drive gluons to aggregate near the critical momentum, thereby forming a novel type of high-density, strongly interacting matter; third, these changes in microstructure manifest themselves as a broken-power-law feature in high-energy cosmic gamma-ray spectra, thereby offering new insights into the hadronic scenarios underlying certain astrophysical sources. Consequently, GC not only concerns the novel behaviour of quantum chromodynamics under extreme conditions but may also serve as a vital window for probing the deep structure of protons using cosmic-ray signals. With the advancement of higher-precision gamma-ray observations, hadron collision experiments and related theoretical research, the physical picture of GC and its observational criteria are expected to undergo more rigorous testing. Should this picture be confirmed, certain features in the high-energy gamma-ray spectrum will need to be re-examined within the deeper context of hadronic dynamics; simultaneously, GC may also provide a new entry point for research into pion condensation in nuclear physics and even condensed matter physics. Consequently, the significance of the search for GC extends beyond the model itself, reaching into multiple fields of natural science.

hep-ph↗

arXiv2026

Crosstalk-free Chiral Anomaly Bulk States in Photonic Crystals

Ultracompact cladding-free waveguide arrays with zero inter-channel spacing and negligible crosstalk open a new avenue for high-density integrated photonic circuits. However, existing cladding-free waveguide arrays typically rely on conventional trivial bulk modes, making them highly susceptible to scattering losses at sharp bends or in the presence of obstacles and defects. To overcome this limitation, we theoretically propose and experimentally demonstrate a robust, crosstalk-free, and cladding-free photonic waveguide array based on chiral anomaly bulk states (CABSs) in photonic crystals. By interfacing distinct Dirac photonic crystals that host Dirac cones at different high-symmetry points ({\Gamma} and K) in the Brillouin zone and carefully engineering the boundary conditions, the boundary-induced CABSs in adjacent channels become effectively decoupled due to a large momentum separation, thereby eliminating inter-channel crosstalk. More importantly, we experimentally demonstrate that these crosstalk-free CABSs are robust to perturbations, including metallic obstacles, air defects, and sharp bends. We further extend the CABS-based waveguide array to two dimensions and demonstrate a cladding-free triangular resonator and a crosstalk-free waveguide crossing, both of which are previously unattainable. Our work establishes a new design paradigm for cladding-free, crosstalk-free, and ultracompact topological photonic devices, paving the way for robust, highly integrated photonic circuits.

physics.optics↗

arXiv2026

Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training

Post-training has become essential for adapting large language models (LLMs) to complex downstream behaviors, including instruction following, preference alignment, and multi-step reasoning. Reinforcement learning with verifiable rewards (RLVR) has recently emerged as a particularly effective post-training paradigm for improving reasoning capabilities, with critic-free algorithms such as GRPO and GSPO enabling scalable optimization. However, RLVR post-training with full fine-tuning (FFT) requires substantial GPU memory and incurs high training costs. Although parameter-efficient fine-tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), effectively reduce computational costs, they often suffer from a noticeable performance gap compared to full fine-tuning in post-training for complex reasoning tasks. In this paper, we propose Hybrid-LoRA, an efficient hybrid post-training framework that selectively applies full fine-tuning to a small subset of modules less suited to low-rank adaptation, while adapting the remaining components with LoRA. We introduce a novel Hybrid-LoRA Score to rank candidate modules according to their sensitivity to low-rank adaptation under a fixed parameter budget. Experiments show that Hybrid-LoRA closely matches full fine-tuning performance under a 10% full fine-tuning module budget, with the remaining candidate modules adapted by LoRA, consistently outperforming four state-of-the-art PEFT post-training baselines, achieving improvements of up to 5.65% and on average 4.36% over the best baseline.

cs.LG↗

arXiv2026

Cascade of fractional quantum Hall states in 2D system

The observation of the fractional quantum Hall (FQH) effect in 2D electron gases ushered in investigations of topological phases driven by strong electron correlations. Their remarkable features include fractionalized elementary excitations, gapless boundary states, and non-trivial quantum entanglement patterns. Thanks to persistent efforts in the building of new platforms and making higher-quality samples, a diverse plethora of FQH states have been unveiled in experiments. We report a systematic study of ultrahigh-quality GaAs/AlGaAs quantum wells with mobility up to 3.7*10^7 cm^2/V/s using quantum transport measurements in nuclear adiabatic demagnetization and dilution refrigerators down to 1 mK. In addition to many FQH states that have already been identified in previous work, new longitudinal resistance dips are observed at filling factors 17/33 and 15/31. The application of an in-plane magnetic field causes disparate variations of the FQH states. The theoretical foundation of these states is discussed in the framework of composite fermion theory. While most fractions can be explained as non-interacting composite fermions forming integer quantum Hall states, a few states correspond to FQH states of composite fermions that arise from residual interaction between them. We summarize the observed fractions in the range of 0 < {\nu} < 2 and propose a pattern to account for their experimental appearance that provides an intuitive picture about the relative strengths of different FQH states.

cond-mat.mes-hall↗

arXiv2026

Noise-Started One-Step Real-World Super-Resolution via LR-Conditioned SplitMeanFlow and GAN Refinement

Pre-trained text-to-image (T2I) diffusion models have shown strong potential for real-world image super-resolution (Real-ISR), owing to their noise-started generation process that enables realistic texture synthesis and captures the one-to-many nature of super-resolution. However, diffusion-based Real-ISR methods still face a fundamental efficiency-quality trade-off. Multi-step methods generate high-quality results by iteratively denoising random Gaussian noise under LR conditioning, but suffer from slow sampling. Recent one-step methods greatly improve efficiency, yet they typically replace noise-started generation with direct LR-to-HR restoration, which weakens stochasticity and limits realistic detail synthesis. To address this issue, we propose SMFSR, a noise-started one-step Real-ISR framework via LR-conditioned SplitMeanFlow and GAN refinement. SMFSR preserves the random-noise starting point of diffusion models and learns a direct noise-to-HR mapping conditioned on the LR image. To this end, Interval Splitting Consistency distills the multi-step generative trajectory into a single average-velocity prediction, enabling efficient one-step generation. To compensate for the reduced opportunity for progressive refinement, we further introduce a GAN refinement stage, where a DINOv3-based discriminator enhances realistic texture synthesis and variational score distillation aligns the generated outputs with the natural image distribution under a frozen diffusion teacher. Extensive experiments demonstrate that SMFSR achieves state-of-the-art perceptual quality among one-step diffusion-based Real-ISR methods while retaining fast single-step inference.

cs.CV↗