arXiv subjects

Hong Li

Publications and source records attributed to Hong Li.

At least 37 records · Page 2Linked to original sources

arXiv2026

Two-fluid effects on the nonlinear dynamics of RFP relaxation

This study investigates the role of two-fluid effects during magnetic relaxation in reversed-field pinch (RFP) plasmas. Within the multiple-helicity (MH) regime, two-fluid simulations produce distinct sawtooth oscillations, in contrast to the sawtooth-free state obtained in single-fluid simulations. Analysis of the magnetic field aligned projection of Faraday's law reveals that, tearing modes collectively generate a dynamo electric field that sustains the magnetic relaxation, a process analogous to the flux-pumping in tokamaks. Despite the reduced linear tearing-mode growth rates, stronger two-fluid effects produce more pronounced sawtooth activity over the parameter range considered. Modal energy analysis shows that Hall-mediated nonlinear energy redistribution disrupts the coherent tearing-mode dynamics required to sustain steady flux-pumping, thereby facilitating intermittent reconnection. This transition is interpreted as a Hall-mediated dynamical bifurcation between steady flux-pumping and quasi-periodic sawtooth relaxation.

physics.plasm-ph↗

arXiv2026

Collisionless Resonances Set the Size of a Weyl Exceptional Ring

Eliminating continuum degrees of freedom converts conservative dynamics into a dispersive, non-Hermitian response whose topology need not represent the causally selected kinetic dynamics. In collisionless magnetized plasma, retarded Landau and cyclotron resonances damp two Weyl modes differently and replace a cold Weyl point by an exceptional ring. Axial roots and root-normalized polarization mixing predict its off-axis radius, $R_{\rm EP}^{\rm pred}=|ΔΓ|/(2|v_{\rm mix}|)$, before any double root is computed. Across five nonrelativistic scans spanning a 310-fold range of differential damping, $R_{\rm EP}^{\rm pred}$ agrees with the nonlinear double-root radius $R_{\rm EP}^{\rm full}$ within $0.12\%$. The selected retarded kinetic-response bundle is a continuous graph over the electric root line bundle and has the same first Chern class under stated conditions. The cold interface mode continues to a localized Keldysh pole with one oriented crossing in the tested line-gap window, consistent with the inherited charge.

physics.plasm-ph↗

arXiv2026

The effects on CMB power spectra and bispectra from the polarization rotation and its correlations with temperature and E-polarization

The Chern-Simons term, through which the cosmic Axion-like field couples to the electromagnetic field, has the effect to rotate CMB polarization directions and to break the CPT symmetry. This rotation will change the CMB power spectra, no matter isotropic or anisotropic the rotation angle is. In this paper we revisit this issue by further considering the correlations between the (anisotropic) rotation angle $α$ and the CMB temperature and (unrotated) $E$ polarization fields. These correlations could be generated in the Axion-like models with nonzero potential under the adiabatic initial condition. We first investigate how these correlations contribute further modifications to the CMB power spectra, then calculate the CMB bispectra for the temperature and rotated polarization fields. These bispectra would vanish if the $Tα$ and $Eα$ correlations are absent. So, they are useful in searching for CPT violation and the $Tα$ and $Eα$ correlations arisen in the Axion-like models.

astro-ph.CO↗

arXiv2026

Study the nature of dynamical dark energy by measuring the CMB polarization rotation angle

Recent results from the Dark Energy Spectroscopic Instrument (DESI) support the dynamical dark energy. Intriguingly, the data favor a transition of the dark energy equation of state across $w=-1$, a hallmark of the Quintom scenario. In this paper, we consider a different approach to the dynamical nature of dark energy by investigating its interaction with ordinary matters, specifically the Chern-Simons (CS) interaction with photons. In cosmology, this interaction rotates the polarized plane of the cosmic microwave background (CMB) photons, which induces non-zero polarized TB and EB power spectra. We forecast this measurement with the Ali CMB Polarization Telescope (AliCPT) experiment. We take the best-fit value of the isotropic rotation angle from Planck data as our fiducial input. We project that 11 module-year (modyr) of observations will yield an improved detection sensitivity with a significance $\sim 5σ$, given a calibration precision of $0.1^\circ$ in the polarization angle. We also forecast AliCPT's sensitivity to the amplitude of a scale invariant spectrum of the anisotropic polarization rotation field. With $50$~modyr of observations, the large-aperture configuration is expected to reach $σ_{A_{\mathrm{CB}}}\sim10^{-2}$, offering a sixfold improvement over the small-aperture design and enabling competitive tests of spatial fluctuations in the dark energy field.

astro-ph.CO↗

arXiv2026

GeoStereo: A Unified Stereo Geometry Estimation Framework for Disparity and Surface Normal

Stereo matching and surface normal estimation are fundamental tasks in 3D vision. However, existing feed-forward stereo methods still struggle to produce reliable predictions in challenging regions, mainly due to the lack of strong geometric priors. In this paper, we propose $\textbf{GeoStereo}$, a unified stereo geometry estimation framework that leverages powerful diffusion priors to jointly predict disparity and surface normals. Specifically, GeoStereo couples a feed-forward stereo matching pipeline with a diffusion-based normal estimation branch. To enable effective interaction between the two tasks, we introduce a disparity to normal initialization strategy and construct a warp to left-view condition for the diffusion process. This coupled design allows the diffusion branch to provide strong structural priors that enhance disparity estimation in ill-posed regions, while the feed-forward branch offers reliable geometric guidance for accurate normal prediction. Extensive experiments show that GeoStereo performs reliably in challenging scenarios, including low-light environments, highly reflective surfaces, and transparent objects. Under zero-shot settings, it achieves Rank-1 disparity estimation on multiple benchmarks, including KITTI and NYUv2, and delivers the best normal estimation accuracy on many real indoor benchmarks, such as iBims-1 and ScanNet. Project page: https://qz-wei.github.io/GeoStereo.github.io/

cs.CV↗

arXiv2026

PartDiffuser: Part-wise 3D Mesh Generation via Discrete Diffusion

Existing autoregressive (AR) methods for generating artist-designed meshes struggle to balance global structural consistency with high-fidelity local details, and are susceptible to error accumulation. To address this, we propose PartDiffuser, a novel semi-autoregressive diffusion framework for point-cloud-to-mesh generation. The method first performs semantic segmentation on the mesh and then operates in a "part-wise" manner: it employs autoregression between parts to ensure global topology, while utilizing a parallel discrete diffusion process within each semantic part to precisely reconstruct high-frequency geometric features. PartDiffuser is based on the DiT architecture and introduces a part-aware cross-attention mechanism, using point clouds as hierarchical geometric conditioning to dynamically control the generation process, thereby effectively decoupling the global and local generation tasks. Experiments demonstrate that this method significantly outperforms state-of-the-art (SOTA) models in generating 3D meshes with rich detail, exhibiting exceptional detail representation suitable for real-world applications.

cs.CV↗

arXiv2026

Mosaic: A Fleet of User Embedding Specialists for Recommendation at Meta

User representation is one of the highest-leverage modeling problems in industrial recommendation systems: a single advancement in how users are encoded can propagate across retrieval, ranking, and integrity tasks at platform scale. Prior industrial user representation work builds either a single user model that emits one or more embedding vectors or a shared backbone with task-specific adaptation. In this paper, we present Mosaic, a foundational user modeling platform that employs a fleet of specialists to learn user embeddings. The fleet comprises four architecturally diverse model families - memorization-driven, dense-heavy, sequential-based, and CoTrain models - each focusing on a distinct facet of user behavior. We developed MRM (Multi-task Relations Mining) and CRL (Cosine Redundancy Loss) techniques to maximize the marginal information contribution of each new specialist. We also introduce CoEval and User Tower Zero-Out, new logging-free embedding evaluation framework that improves development velocity while preserving downstream-aligned accuracy. Our hybrid CPU/GPU, online-and-offline serving stack allows each specialist to choose the adequate serving strategy to meet the freshness, latency, and computational requirements. Mosaic delivers consistent and significant offline NE improvements in addition to online gains.

cs.IR↗

arXiv2026

RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation

Graph-based retrieval at billion-node scale requires jointly solving three tightly coupled problems -- graph construction, representation learning, and real-time serving -- yet existing work addresses each in isolation. We present RankGraph-2, a framework deployed at Meta that co-designs all three lifecycle stages for similarity-based retrieval (U2U2I and U2I2I), where each stage's requirements shape the others. Serving requires a co-learned cluster index to avoid expensive online KNN -- this pushes index co-training into the training objective. Training benefits from the observation that similarity-based retrieval tolerates pre-computed neighborhoods, eliminating online graph infrastructure -- this requires construction to produce self-contained data. Construction must also support hour-level refresh for item coverage. Acting on these cascading requirements, RankGraph-2 reduces hundreds of trillions of edges to hundreds of billions via subsampling with popularity bias correction, pre-computes multi-hop neighborhoods via personalized PageRank, and co-learns a residual-quantization cluster index that reduces serving computational cost by 83%. This lifecycle co-design enables a simple architecture to achieve 3.8 x higher recall than a GAT + Deep Graph Infomax model on a bipartite graph and 2.1 x higher than PyTorch-BigGraph on item retrieval. RankGraph-2 delivers up to +0.96% CTR and +2.75% CVR, and has powered 20+ retrieval launches across major surfaces.

cs.IR↗

arXiv2026

Approximation algorithms for the prize-collecting rural postman problem

In this paper, we study the prize-collecting rural postman problem (PCRPP), a variant of the rural postman problem. In an instance of the PCRPP, one is given an undirected graph whose edges have nonnegative lengths and nonnegative profits, together with a specified root vertex. The goal is to find a closed walk that starts and ends at the root vertex and minimizes the sum of the walk length and the profits of all edges that the walk does not traverse. A natural way to design an approximation algorithm for the PCRPP is to construct a prize-collecting traveling salesman problem (PCTSP) instance from the given PCRPP instance, apply an approximation algorithm to the PCTSP instance, and then convert the resulting solution to the PCTSP instance into a solution to the PCRPP instance. We show that this approach has an inherent factor-two barrier: even if the constructed PCTSP instance is solved exactly, the resulting solution to the PCRPP instance can have objective value arbitrarily close to twice the optimum value of the PCRPP instance. Our main result is a polynomial time approximation algorithm with an approximation ratio strictly smaller than 1.6 for the PCRPP. On a public benchmark set of 118 instances, the proposed algorithm has average and maximum optimality gaps of 3.39% and 12.12%, respectively.

cs.DS↗

arXiv2026

A short review on Quintom dark energy theory

In this paper, we provide a short review on the Quintom dark energy theory. Firstly, we discuss the No-Go theorem associated with dynamical dark energy, then present some examples of models in which the equation of state (EoS) evolves with time and can cross $w=-1$ . Secondly, we discuss the bouncing universe and emergent universe with Quintom matter. Finally, we discuss the possibility of studying the nature of dark energy by measuring the Cosmic Microwave Background (CMB) polarization rotation angle.

astro-ph.CO↗

arXiv2026

Reducing Prize-Collecting Stroll and Related Routing Problems to Prize-Collecting TSP

The prize-collecting stroll is the path version of the prize-collecting TSP. Given a complete metric graph, two distinct prescribed terminal vertices $s, t$, and nonnegative penalties on vertices, the prize-collecting stroll asks for an $s$-$t$ tour minimizing its length plus the total penalty of vertices that are not visited by it. We study a common generalization of the prize-collecting stroll and several related prize-collecting routing problems, which we call the prize-collecting-$Φ$-TSP. In this model, $Φ$ specifies a set of prescribed vertices together with their parity and connectivity requirements. We show that, if a $ρ$-approximation algorithm for the prize-collecting TSP is available, then, for every fixed $\varepsilon>0$, there is a polynomial-time $(ρ+\varepsilon)$-approximation algorithm for the prize-collecting-$Φ$-TSP when the number of prescribed vertices is bounded by a fixed constant. Consequently, the prize-collecting stroll can be approximated as well as the prize-collecting TSP up to an arbitrarily small additive loss in the approximation ratio. This yields a better-than-$1.6$-approximation algorithm for the prize-collecting stroll, improving the previous best-known approximation guarantee of $1.6662$.

cs.DS↗

arXiv2026

CMSL: Constructive Multi-Sequence Learning for Recommendation Systems

Sequence learning has emerged as the promising paradigm in recommendation systems, surpassing traditional Deep Learning Recommendation Models (DLRM) by capturing the temporal nuances of user behavior. However, current state-of-the-art architectures operate under a limiting analogy: they treat user history as a monolithic chronological sequence like a sentence in a Large Language Model (LLM). We observe a fundamental divergence between natural language and recommendation data: unlike the linear, logical flow of text, user history is inherently multi-faceted. A user's journey is a fragmented reflection of diverse interests, resulting in much weaker coherence between items than is found in LLM training data. This lack of structural unity leads to context pollution. In single-sequence modeling, unrelated behaviors compete for the same attention budget. This "noisy" signal dilutes the model's focus, effectively capping its ability to discern high-intent patterns from background activity. To address this, we propose Constructive Multi-Sequence Learning (CMSL), a paradigm shift from passive sequence ingestion to active "context engineering" that constructs multiple coherent sequences in latent space. CMSL leverages a learnable Sequence Construction Module to disentangle user history into "pure" thematic strands, followed by a linear attention mechanism to efficiently model these strands at scale. CMSL has been deployed across ranking and retrieval tasks and across four major surfaces at Meta.

cs.IR↗

arXiv2026

Human-as-Humanoid: Enabling Zero-Shot Humanoid Learning from Ego-Exo Human Videos with Human-Aligned Embodiments

Vision-language-action (VLA) models across robot embodiments require high-quality observation--action supervision to learn deployable action distributions, yet scaling such robot data remains difficult, especially for high-DoF humanoids. Teleoperation provides controller-aligned supervision, while human egocentric videos capture diverse bimanual manipulation but do not directly provide executable robot actions. We introduce Human-as-Humanoid, a human-to-humanoid supervision framework that enables near-real-time human-centric action generation, making human demonstrations usable for high-DoF humanoid VLA training by jointly aligning the robot embodiment, the sensing setup, and the action-label interface. Built on PrimeU, a human-aligned 60-DoF upper-body humanoid, Human-as-Humanoid uses synchronized ego-exo videos to pair deployment-aligned egocentric observations with exocentric motion recovery, retargets the recovered human motion through staged Inverse Kinematics (IK) into controller-aligned 60-DoF action chunks, and trains the VLA model with Forward Kinematics (FK)-aware supervision to preserve wrist and fingertip task-space geometry. This converts large-scale human demonstrations from visual observations into executable observation--action supervision for the target humanoid. Experiments validate the conversion chain at the motion-recovery, robot-action-space, and real-robot deployment levels. Human-as-Humanoid yields a 4.8--7.2x raw demonstration-throughput gain over humanoid teleoperation in our data-collection analysis, and on several downstream tasks, policies post-trained only with the converted human labels generalize to real-robot deployment without target-task robot demonstrations. The official project website is available at https://zgc-embodyai.github.io/Human-as-Humanoid.

cs.RO↗

arXiv2026

Understanding Binary Code Similarity for Real-World Vulnerability Detection: A Large-Scale Empirical Study

Firmware lies at the heart of IoT devices. Its development depends heavily on third-party libraries (TPLs), which greatly accelerate the process but simultaneously introduce associated vulnerabilities. Binary Code Similarity Detection (BCSD) is an effective technique for identifying vulnerabilities in firmware by comparing pairs of code segments. However, existing studies either evaluate their performance only on small-scale datasets or lack diversity in terms of vulnerabilities, TPLs, and firmware. Consequently, a comprehensive understanding of BCSD for real-world vulnerability detection remains absent. To bridge this gap, we conduct a large-scale study of vulnerability detection across 60,000 firmware images from 200 vendors using BCSD. Rather than introducing a novel model, we examine the influence of four key factors -- vulnerable function versions, vulnerability search space, function sizes, and compilation toolchains on BCSD performance. Our results reveal that these factors substantially affect performance, often by wide margins. To address this, we propose a build-aware query strategy that derives queries from representative real-world binaries, effectively closing the gap and raising the mean reciprocal rank (MRR) from 0.818 to 0.981. Furthermore, we demonstrate that a TPL-aware, two-stage search process significantly enhances accuracy, improving MRR by 18.5\% by limiting the search space.

cs.CR↗

arXiv2026

Monocular Avatar Reconstruction via Cascaded Diffusion Priors and UV-Space Differentiable Shading

Reconstructing high-fidelity, relightable 3D avatars from a single in-the-wild image is a challenging ill-posed problem, primarily hindered by the scarcity of high-quality PBR data and the complexity of disentangling illumination from intrinsic materials. In this paper, we present a data-efficient framework that leverages the robust priors of a unified pre-trained diffusion backbone to sequentially address texture completion, delighting, and material decomposition. Unlike existing methods that rely on fragmented pipelines or extensive proprietary datasets, we utilize cascaded Low-Rank Adaptations (LoRAs) to adapt the strong generative prior of the diffusion model for each sub-task in UV space. Specifically, we first employ an Inpainting LoRA to complete missing UV textures caused by occlusion, leveraging the model's semantic understanding to generate semantically and photometrically coherent details. Subsequently, a Light-Homogenization LoRA and a novel Cross-Intrinsic Attention mechanism are introduced to remove baked-in lighting and collaboratively synthesize pixel-aligned PBR maps (Albedo, Normal, Roughness, Specular, and Displacement). To ensure physical plausibility, we impose a UV-space differentiable BRDF shading loss during the decomposition stage, forcing the generative process to adhere to the rendering equation without the artifacts typical of rasterization-based supervision. Extensive experiments demonstrate that our method, trained on fewer than 100 real 3D scans, generates comprehensive, 4K-resolution PBR assets with superior realism and generalization compared to state-of-the-art methods, and all training code and model weights will be released upon acceptance.

cs.CV↗

arXiv2026

Structuring and Tokenizing Distributed User Interest Context for Generative Recommendation

Generative recommendation is an emerging paradigm that has shown promise in industrial recommendation systems, aiming to predict users' next interactions from their historical behaviors. At the core of generative recommendation lies item tokenization, which bridges item semantics and recommendation models. However, existing methods often struggle to effectively organize and inject complex user-behavioral and item-semantic contexts into recommendation models simultaneously. On the one hand, existing graph-based integration methods, such as graph serialization and graph neural networks, either suffer from scalability issues or exploit only local graph information. On the other hand, existing semantic tokenization methods typically rely on heuristics and lack explicit supervision signals, which may lead to inaccurate or suboptimal semantic representations. To address these limitations in user interest context modeling, we propose G2Rec, a scalable framework that unifies holistic graph-based user co-engagement modeling with semantic tokenization for industrial-scale generative recommendation. Overall, G2Rec enables recommendation models to capture holistic and semantically grounded user interest prototypes without requiring ground-truth user interests, thereby providing more comprehensive and accurate modeling of user behavior contexts in industrial sequential recommendation. Online deployment across product surfaces and extensive experiments on public datasets demonstrate the superiority of G2Rec over existing methods.

cs.IR↗

arXiv2026

AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning

Force and tactile sensing are indispensable in contact-rich manipulation. However, force-aware robot learning faces critical challenges due to the incompatible assembly of tactile and force sensors in handheld or wearable devices. To address these limitations, we first introduce AetheRock for gripper-force, vision, and tactile data collection, which is an arm-worn device featuring a modular and easily manufactured visuo-tactile sensor, GelSlim-MiniFab, at the fingertip, a resistive pressure sensor at the human finger contact region, a customized PCB module, and a wearable kit for comfortable and robust collection. Building on this, we propose ForceVT, a representation learning framework that uses force and vision to guide fidelity-agnostic tactile learning, enabling robust inference in any tactile situation. Real-world experiments show that AetheRock achieves qualified data efficiency and that ForceVT effectively alleviates inefficiencies when visuo-tactile sensors exhibit manufacturing and utilization inconsistencies. Overall, our work mitigates the limitations of gripper-force vision-tactile robot learning through innovative hardware design and algorithms.

cs.RO↗

arXiv2026

ChronoID: Infusing Explicit Temporal Signals into Semantic IDs for Generative Recommendation

Semantic IDs are crucial in generative recommendation, but with a fundamental limitation: temporal information is not well incorporated into semantic IDs. Instead, time influences recommendation only implicitly (e.g., through session construction heuristics, preference alignment, or sequence order), while existing semantic ID learning remains entirely time-agnostic. This design conflates interactions occurring under distinct temporal contexts into identical semantic representations, implicitly assuming that item semantics and user intent are temporally stationary. Such an assumption is misaligned with real-world recommendation scenarios, where evolving interaction rhythms play a central role. In this work, we investigate where and how the explicit time should be incorporated into semantic ID for generative recommendation. First, we systematically characterize the design space along three orthogonal dimensions of temporal signals and present a unified framework, ChronoID, for time-aware semantic ID learning. Then, by contributing a new time-explicit generation recommendation benchmark, ChronoID answers the questions: what is the effective way of infusing time, how to design the architecture, and where does the gain come from.

cs.IR↗