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Yang Huang

Publications and source records attributed to Yang Huang.

At least 37 records · Page 2Linked to original sources
arXiv2026

Learning to Forget: Satiation-Aware Long-Sequence Transducers for Mitigating Post-Purchase Redundancy

Sequential recommendation models predominantly interpret user interactions as positive signals for preference accumulation. However, in e-commerce scenarios, a purchase action often signifies the termination of a specific intent ("Interest Exit") rather than its continuation. Existing models overlook this distinction, suffering from Action-Intent Asymmetry, which leads to severe post-purchase redundancy. In this paper, we propose the Satiation-Aware Mechanism (SAM), an end-to-end framework designed to explicitly model the lifecycle of user interests. SAM incorporates three key components: (1) A Dual-path Cross-Attention architecture that retroactively suppresses historical clicks associated with a fulfilled intent while simultaneously retrieving personalized replenishment rhythms from long-term purchase history; (2) An Adaptive Satiation Gating Unit (ASGU) that generates a time-sensitive soft mask to inhibit satisfied interests immediately after purchase and gradually "re-awaken" them as the predicted repurchase cycle approaches; and (3) A self-supervised Time-to-Next-Purchase (TTNP) auxiliary task to learn latent product lifecycles without manual annotation. Extensive offline experiments on industrial datasets and online A/B testing demonstrate that SAM significantly reduces the Post-Purchase Repeat Rate (PPRR) by over 60%.

cs.IR↗
arXiv2026

High-Fidelity One-Step Generative Visuomotor Policy via Recursive Correction, Frequency Consistency, and Contrastive Flow Matching

Generative models such as diffusion and flow matching have advanced robotic visuomotor policies by modeling multimodal action distributions, but their multi-step sampling or ODE solving introduces inference latency. Existing one-step acceleration methods often compress the whole generation process into a single large update, leading to spatial deviation, frequency distortion, and mode averaging. This paper proposes a high-fidelity one-step generative visuomotor policy framework that addresses these issues with three complementary mechanisms. Recursive Consistent Action Flow (RCAF) uses recursive correction to compensate for spatial truncation errors and align one-step predictions with refined flow trajectories. Dual-Timestep Frequency Consistency (DTFC) preserves high-frequency manipulation details through adaptive spectral consistency across flow timesteps. Contrastive Flow Matching (CFM) separates entangled action flows with a margin-based repulsive objective, reducing ambiguous actions in multimodal manipulation. Experiments on RoboTwin, RoboTwin 2.0, Adroit, DexArt, and real-world robot platforms show that the proposed method achieves competitive or superior performance compared with strong 10-step generative policy baselines while requiring only one forward pass (1 NFE), enabling low-latency visuomotor control.

cs.RO↗
arXiv2026

The Epoch of the GSE Merger: Insights from the Splash and Thick Disk Age Distributions

The epoch of the Gaia-Sausage-Enceladus (GSE) merger, the last major merger experienced by the Milky Way, is crucial for reconstructing the Galaxy's evolutionary history. This event dynamically heated the disk, scattering some stars into the halo and producing the so-called Splash. Yet the observed age distribution of the Splash is systematically older than that of the thick disk from which it is thought to originate, posing a puzzling inconsistency. In this work, we show that this apparent discrepancy can be naturally explained once stellar age uncertainties are taken into account. The GSE truncated the intrinsic age distribution of the Splash at the merger epoch, while measurement errors introduce an Eddington-like bias that systematically shifts the truncated distribution toward older ages. Importantly, the magnitude of this shift depends on both the thick-disk star formation history and the merger epoch, thereby offering a new way to constrain the timing of the merger. By combining the observed Splash age distribution with the peak of thick-disk star formation, we infer a GSE merger epoch of $10.1^{+0.2}_{-0.2}$ Gyr ago, providing one of the tightest constraints to date. This result offers new insight into the Milky Way's accretion history and opens a path toward more robust reconstructions of its early evolution.

astro-ph.GA↗
arXiv2026

Reversible non-equilibrium phase transformation in amorphous germanium

First principles molecular dynamics simulations of germanium reveal a reversible liquid-glass transition below the equilibrium melting point with a wide hysteresis loop. Direct calculation of the liquid and amorphous state free energies, enabled by absolute entropy calculations, verify that the transition is first order in character between two metastable phases. These results lend credence to models of explosive recrystallization from amorphous Ge thin films, through a metastable liquid state and finally reaching the low temperature crystalline structure.

cond-mat.mtrl-sci↗
arXiv2026

A sharp product bound for non-trivial cross-intersecting families

Two families $\mathcal{A}, \mathcal{B} \subset \binom{[n]}{k}$ are cross-intersecting if $A \cap B \ne \emptyset$ for all $A \in \mathcal{A}$ and $B \in \mathcal{B}$, and non-trivial if neither $\m A$ nor $\m B$ is a star. Pyber proved that any two cross-intersecting families $\mathcal{A}, \mathcal{B} \subset \binom{[n]}{k}$ satisfy $|\mathcal{A}||\mathcal{B}| \le \binom{n-1}{k-1}^2$, and the maximum is attained by two full stars. Frankl, as well as Frankl and Wang, conjectured that the sharp bound, when both families are required to be non-trivial, is $h(n,k)^2$, where $h(n,k) = \binom{n-1}{k-1} - \binom{n-k-1}{k-1} + 1$, the size of the Hilton--Milner family. The cases $k=3$, and the range $k\ge8$ and $n\ge 4k$, were established earlier by Frankl and by Frankl and Wang, respectively. In this paper, we prove their conjecture in the full range. We show that every non-trivial cross-intersecting pair $\mathcal{A}, \mathcal{B} \subset \binom{[n]}{k}$ with $n \ge 2k$ and $k \ge 3$ satisfies $|\mathcal{A}||\mathcal{B}| \le h(n,k)^2$. Moreover, we characterize all extremal pairs. Whereas the corresponding sum problem admits asymmetric and unbalanced extremizers, the product extremum forces a balanced, symmetric-or-dual structure: the two families are isomorphic when $n>2k$ and complement-dual when $n=2k$. Independently and contemporaneously with the present work, Frankl and Wang obtained the same bound for $k\ge8$ and $n\ge2k+1$ by a different method. Our proof combines a diversity technique with several new properties of an extended shift operation. Moreover, we show that the problem behaves differently for different uniformities, exhibiting new extremal configurations. In particular, we disprove a related conjecture proposed by Frankl and Wang.

math.CO↗
arXiv2026

Structure and properties of large cross-intersecting families

The study of intersecting families, initiated by Erdős, Ko, and Rado, is a central topic in extremal combinatorics. A classical stability result of Hilton and Milner determines the largest non-trivial intersecting family, and in subsequent works researchers developed structural stability results via the notion of diversity. In this paper, we study cross-intersecting families. We establish a structural theorem for large cross-intersecting pairs, extending Kupavskii's theorem from intersecting families to the cross-intersecting setting. Our result characterizes extremal cross-intersecting pairs in terms of their diversity parts and maximal cross-intersecting extensions. As corollaries, we obtain cross-intersecting analogues of several classical theorems, including those of Han--Kohayakawa and Huang--Peng. A key ingredient in the proof is a new shifting method, called the $S_{U,V}^{Q}$-shift, which not only preserves global intersection properties but also maintains certain local substructures after shifting. We expect this method to be useful elsewhere, and it is already one of the key tools in establishing a product analogue of the Hilton--Milner theorem.

math.CO↗
arXiv2026

Post-perihelion Coma Composition of the Interstellar Comet 3I/ATLAS from Optical Spectroscopy

We present multi-epoch optical spectroscopy of the interstellar comet 3I/ATLAS obtained between December 2025 and January 2026 (heliocentric distances 1.8-3.3 au), yielding post-perihelion production rates and mixing ratios for CN, C$_3$, C$_2$, CH, and gaseous metals (Fe I and Ni I). Our results show that the coma is less depleted in C$_2$ after perihelion than before, indicative of subsurface activation or compositional heterogeneity. The outgassing profiles reveal a pronounced perihelion asymmetry: CN and metal production rates decline more gradually outbound than inbound, consistent with the reported behavior of H$_2$O and implying a change in the comet's activity pattern across perihelion. Despite being metal-rich relative to its H$_2$O content, 3I follows the metal-CO correlation observed in comets of diverse origins, suggesting that gaseous metal release is more closely linked to a CO-bearing volatile reservoir than to H$_2$O, potentially in the form of metal carbonyls. In addition, the [O I] $\lambda6300$ emission shows a significant residual after subtracting the expected contributions from H$_2$O, CO$_2$, and CO, which may reflect systematic uncertainties in the photodissociation yields of those molecules or a contribution from additional oxygen-bearing parents.

astro-ph.EP↗
arXiv2026

Scalar absorption beyond geometric optics in Klein-Gordon-separable Johannsen black hole spacetimes

Johannsen metric is a natural and significant generalization of the Kerr metric, representing the most general stationary, axisymmetric spacetime that preserves the Carter constant of motion. The theoretical status furnishes a powerful, systematic framework for strong-field tests of the no-hair theorem and for investigations of deviations from Kerr black-hole geometries. We formulate massless scalar plane-wave absorption in a Klein-Gordon-separable subclass of Johannsen spacetimes. In the asymptotically flat Johannsen metric, we impose Klein-Gordon separability, derive the separated angular and radial equations, and build a partial wave framework for the leading deformation sectors $A_1(r)$, $A_2(r)$, and $A_5(r)$. The resulting description separates deformations that change the radial size function $X(r)$ from those that enter only the radial kinetic term. The former modify the low-frequency area law, the high-frequency null-capture cross section, and the finite-frequency absorption spectra, whereas a pure $A_5$ deformation leaves the leading null-capture observable unchanged while remaining detectable in wave propagation. We further examine off-axis incidence, co-/counter-rotating contributions, and superradiant modes, where changes in $X(r_+)$ shift the horizon angular velocity and hence the superradiant threshold. Our results identify finite-frequency absorption as a wave-optics diagnostic that can probe radial propagation sectors inaccessible to both the area law and null geodesic capture observables, offering a new tool for strong-field tests of black hole geometry.

gr-qc↗
arXiv2026

From Head to Tail: Asymmetric Knowledge Transfer in Long-tail Recommendation with Generative Semantic IDs

Long-tail recommendation in real-world e-commerce platforms remains challenging due to severe data imbalance. Existing methods often struggle to combine content-based multimodal features with collaborative signals. Many of these methods also ignore an important asymmetry in knowledge transfer between head and tail IDs: noisy signals from tail IDs can hurt representation learning for head IDs. This paper presents AKT-Rec, a framework for Asymmetric Knowledge Transfer in long-tail Recommendation that uses LLM-generated semantic IDs. AKT-Rec uses Multimodal LLMs (MLLMs) with supervised fine-tuning to align content representations with collaborative information for both items and users, producing semantic representations. It then discretizes these representations into semantic IDs with a Residual-Quantized VAE (RQ-VAE), which yields semantic clusters of similar entities. AKT-Rec has two main components: (1) Cluster-Guided Adaptive Embedding, which decomposes each ID representation into a cluster-level embedding that captures shared semantics and an individual embedding. Through an asymmetric contrastive objective and an activity-aware gating mechanism, this module directs knowledge transfer from head to tail IDs. (2) Hierarchical Feature Aggregation, which builds parallel feature views and adaptively fuses them to optimize predictions for samples with varying activity levels. Extensive experiments on a large-scale industrial dataset and online A/B testing on the Alibaba Tmall platform demonstrate the effectiveness of AKT-Rec. AKT-Rec improves offline performance by 0.35% in AUC and 1.53% in GAUC, outperforming several competitive baselines. In online A/B testing, AKT-Rec achieves a 2.76% increase in CTR and a 3.47% increase in GMV, validating its utility in real-world production environments.

cs.IR↗
arXiv2026

An Old, Low-mass, Metal-poor Hypervelocity Star Candidate Consistent with a Galactic Center Origin

We report the discovery of DESI-HVS1, a hypervelocity star (HVS) candidate identified from DESI DR1 spectroscopy and Gaia DR3 astrometry. DESI-HVS1 is an old, low-mass, metal-poor F-type star with a mass of $0.8\,M_\odot$, an age of $\sim14.1$~Gyr, and $\mathrm{[Fe/H]}=-1.6$. It is located at a heliocentric distance of $3.77^{+0.39}_{-0.36}$~kpc and has a Galactocentric total velocity of $523^{+46}_{-47}\,\mathrm{km\,s^{-1}}$, marginally exceeding the local escape speed, corresponding to an unbound probability of $P_{\rm ub} \sim 50\%$. Backward orbit integrations show that DESI-HVS1 had a closest approach to the Galactic center (GC) of $0.40^{+0.23}_{-0.11}\,\mathrm{kpc}$, with a velocity of $682^{+22}_{-35}\,\mathrm{km\,s^{-1}}$ and a flight time of $12.89^{+0.92}_{-0.74}\,\mathrm{Myr}$. The reconstructed orbit exhibits a clear perigalactic turning point and only a single crossing of the Galactic midplane ($P_{\rm cross} > 0.95$). These properties suggest that DESI-HVS1 is most naturally explained by the Hills mechanism, although alternative scenarios cannot be entirely ruled out. Its discovery provides the first strong evidence for an old, low-mass HVS candidate consistent with a GC origin, indicating that the apparent dominance of young, massive GC-origin HVSs is likely a consequence of observational selection effects.

astro-ph.GA↗
arXiv2026

Composition-Weighted Symbolic Regression for General-Purpose Property Prediction

We introduce a composition-weighted symbolic regression framework for interpretable prediction of materials properties directly from chemical composition. The method jointly learns analytical functional forms and task-dependent elemental weightings without predefined descriptors. By incorporating max/min operators, it naturally enforces constraints such as non-negative band gaps and bounded classification probabilities, unifying regression and classification tasks. Efficient search is achieved through a hybrid Monte Carlo tree search--genetic programming algorithm with gradient-based refinement and parallel computation. Benchmarks on MatBench tasks show competitive accuracy relative to state-of-the-art black-box models while yielding explicit analytical expressions. Applied to III--V semiconductor alloys, the model produces smooth composition-dependent trends and learned elemental weights with chemically meaningful periodic behavior. This framework provides a scalable and interpretable route for materials discovery and property screening.

cond-mat.mtrl-sci↗
arXiv2026

OpAgent: Operator Agent for Web Navigation

To fulfill user instructions, autonomous web agents must contend with the inherent complexity and volatile nature of real-world websites. Conventional paradigms predominantly rely on Supervised Fine-Tuning (SFT) or Offline Reinforcement Learning (RL) using static datasets. However, these methods suffer from severe distributional shifts, as offline trajectories fail to capture the stochastic state transitions and real-time feedback of unconstrained wide web environments. In this paper, we propose a robust Online Reinforcement Learning WebAgent, designed to optimize its policy through direct, iterative interactions with unconstrained wide websites. Our approach comprises three core innovations: 1) Hierarchical Multi-Task Fine-tuning: We curate a comprehensive mixture of datasets categorized by functional primitives -- Planning, Acting, and Grounding -- establishing a Vision-Language Model (VLM) with strong instruction-following capabilities for Web GUI tasks. 2) Online Agentic RL in the Wild: We develop an online interaction environment and fine-tune the VLM using a specialized RL pipeline. We introduce a Hybrid Reward Mechanism that combines a ground-truth-agnostic WebJudge for holistic outcome assessment with a Rule-based Decision Tree (RDT) for progress reward. This system effectively mitigates the credit assignment challenge in long-horizon navigation. Notably, our RL-enhanced model achieves a 38.1\% success rate (pass@5) on WebArena, outperforming all existing monolithic baselines. 3) Operator Agent: We introduce a modular agentic framework, namely \textbf{OpAgent}, orchestrating a Planner, Grounder, Reflector, and Summarizer. This synergy enables robust error recovery and self-correction, elevating the agent's performance to a new State-of-the-Art (SOTA) success rate of \textbf{71.6\%}.

cs.AI↗
arXiv2026

RV and TTV Measurements of Two Transiting Long-Period Giants around TOI-4600

TOI-4600b and c, originally identified by the Transiting Exoplanet Survey Satellite (TESS) and reported by I. Mireles et al. (2023), are a rare pair of transiting long-period giant planets ($\rm P_b=82.7$ days, $\rm P_c=482.8$ days) orbiting an early K dwarf. In this work, we refine the orbital parameters of the TOI-4600 system by combining new TESS photometry, ground-based transit follow-up, and radial velocity (RV) observations from MAROON-X. We obtain improved constraints on planetary masses and eccentricities, and update other parameters, such as the stellar age. For TOI-4600b, we measure a mass of $M_p = 74.7^{+4.7}_{-4.4}\,M_{\oplus}$ and an eccentricity of $e=0.153^{+0.020}_{-0.018}$, and $M_p = 212.53^{+13.26}_{-13.03}\,M_{\oplus}$ and $e=0.219^{+0.015}_{-0.018}$ for TOI-4600c. We find significant transit timing variations (TTV) in both planets, with semi-amplitudes of approximately $1$\,hr. We derive Transit Spectroscopy Metric values of 16.87 for TOI-4600b and 10.09 for TOI-4600c, indicating that both planets are promising JWST targets for studying the atmospheres of temperate and cold Jupiters, a relatively poorly characterized sample thus far. These updated parameters and TTV ephemerides are important for planning and interpreting future photometric, spectroscopic, and dynamical studies of the TOI-4600 system.

astro-ph.EP↗
arXiv2026

Convex hypersurface theory in contact topology

We lay the foundations of convex hypersurface theory in contact topology, extending the work of Giroux in dimension three. Specifically, we prove that any closed hypersurface in a contact manifold can be $C^0$-approximated by a convex one. We also prove that a $C^0$-generic family of mutually disjoint closed hypersurfaces parametrized by $t\in[0,1]$ is convex except at finitely many times $t_1,\dots,t_N$, and that crossing each $t_i$ corresponds to a bypass attachment. As an application, we prove the existence of compatible (relative) open book decompositions for contact manifolds.

math.SG↗
arXiv2026

Contrastive Heliophysical Image Pretraining for Solar Dynamics Observatory Records

Deep learning has revolutionized solar image analysis, yet most approaches train task-specific encoders from scratch or rely on natural-image pretraining that ignores the unique characteristics of Solar Dynamics Observatory (SDO) data. We introduce SolarCHIP, a family of contrastively pretrained visual backbones tailored to multi-instrument SDO observations. SolarCHIP addresses three key challenges in solar imaging: multimodal sensing across AIA and HMI instruments, weak inter-class separability due to slow temporal evolution, and strong intra-class variability with sparse activity signals. Our pretraining framework employs a multi-granularity contrastive objective that jointly aligns (1) global class tokens across co-temporal AIA-HMI pairs to enhance temporal discrimination, (2) local patch tokens at fixed spatial indices to enforce position-consistent, modality-invariant features, and (3) intra-sample patches across different spatial locations to preserve fine-grained spatial structure. We train both CNN- and Vision Transformer-based autoencoders and demonstrate their effectiveness on two downstream tasks: cross-modal translation between HMI and AIA passbands via ControlNet, and full-disk flare classification. Experimental results show that SolarCHIP achieves state-of-the-art performance across both tasks, with particularly strong gains in low-resource settings where labeled data is limited. Ablation studies confirm that each contrastive component contributes essential discriminative capacity at different granularities. By publicly releasing pretrained weights and training code, we provide the heliophysics community with a practical, plug-and-play feature extractor that reduces computational requirements, improves label efficiency, and establishes a reusable foundation for diverse solar imaging applications.

cs.CV↗
arXiv2026

StarCLR: Contrastive Learning Representation for Astronomical Light Curves

With the rapid development of time-domain surveys, the availability of massive light curve data offers new opportunities for studying stellar evolution and variable star classification, while simultaneously posing challenges for feature extraction and modeling. We present StarCLR, a contrastive pretraining framework for large-scale light curves. By constructing positive pairs from partially overlapping sub-sequences, StarCLR encourages the model to learn temporal representations. We pretrain StarCLR on the TESS dataset and fine-tune it for variable star classification on three surveys with distinct observational characteristics, namely TESS (18 types), ZTF (11 types), and Gaia (24 types). StarCLR achieves macro-F1 scores of 84.35%, 87.82%, and 92.73%, and micro-F1 scores of 94.46%, 92.83%, and 99.49%, respectively. Compared with LSTM and Transformer trained from scratch, StarCLR performs better on TESS and ZTF, with the largest gains on sparsely sampled ZTF light curves, demonstrating promising generalization. For Gaia, which involves a broader class space, the evaluation is not directly comparable, and performance is likely influenced by astrophysical features, resulting in a more limited contribution from the pretrained backbone. Systematic ablations on embedding design, pooling strategy, and pretraining settings further indicate that the pretrained representations provide performance gains by capturing informative temporal characteristics of light curves. Looking ahead, with standardized datasets and more diverse labeling schemes, the generalization ability of StarCLR can be further enhanced.

astro-ph.SR↗
arXiv2026

Crystal Fractional Graph Neural Network for Energy Prediction of High-Entropy Alloys

High-entropy alloys (HEAs) have attracted growing attention for their exceptional mechanical and thermal properties arising from complex atomic configurations. In this paper, we propose crystal fractional graph neural network for predicting the energy of high-entropy alloys by explicitly integrating both local atomic environments and global compositional information. The model consists of three components: a crystal graph neural network, which employs graph attention network layers to learn local interactions among 16 on-site atoms within the crystal lattice; fractional neural network, a fully connected network that embeds the global fraction of constituent elements; and feature fusion neural network, which fuses the outputs of the two submodels to predict the total crystal energy. We train the model on a dataset of 1,049 crystal structures and validate it on 198 quaternary structures, optimizing all hyperparameters via Optuna. Our results show that our model achieves an RMSE comparable to first-principles calculations and maintains high accuracy even for low-energy configurations. However, the model exhibits limitations in handling large crystal cells, which we aim to address in future work to extend its applicability to more complex systems.

physics.comp-ph↗
arXiv2026

Filter Design for Estimating the Stellar Metallicity of Metal-poor Stars from Gaia XP Spectra

The estimation of stellar atmospheric parameters for large-scale samples, particularly metal-poor stars, is a cornerstone of Galactic archaeology. In this work, we optimized a photometric filter design tailored to measuring stellar metallicities for very metal-poor stars with [Fe/H]$< -1$.The optimal configurations consist of a central wavelength $λ_{\rm c}$ = 3960 Angstrom with a bandwidth $Δλ$ = 80 Angstrom for giant stars, and $λ_{\rm c} $= 3920 Angstrom with $Δλ$ = 80 Angstrom for dwarf stars. By applying these optimized filters to synthetic photometry derived from Gaia XP spectra, we inferred metallicities for both populations. Both internal and external validations demonstrate high precision across a wide metallicity range: 0.18-0.19 dex for $-2 \le \rm [Fe/H] \le -1$, 0.23-0.33 dex for $-3 \le \rm [Fe/H] \le -2$, and approximately 0.39 dex for the most metal-poor regime, successfully extending down to $\rm [Fe/H] \approx -4$ for giant stars, $\rm [Fe/H] \approx -3.3$ for dwarf stars. Finally, we present a catalog of approximately 14.5 million metal-poor stars with robust $\rm [Fe/H]$ measurements, along with more than ten thousand red giant ultra metal-poor candidates with $\rm [Fe/H] < -4.0$, providing a valuable resource for exploring the early formation and chemical evolution of the Milky Way.

astro-ph.SR↗