arXiv subjects

Wentao Xie

Publications and source records attributed to Wentao Xie.

13 recordsLinked to original sources

arXiv2026

TrustMee: Self-Verifying Remote Attestation Evidence

Remote attestation allows a TEE to attest its own state to a remote party, and is used in the Confidential Computing ecosystem to provide assurance that data and code are available only to authorized hardware and software. This functionality can then be incorporated into higher-level platforms, allowing workloads to attest themselves to a remote party. However, supporting each new platform enlarges a Verifier's trusted computing base, since it must include code to verify the attestation evidence from each platform, often performing risky operations e.g., binary parsing. This means that emerging or niche Attester platforms cannot be supported by standard Verifier software, preventing them from interacting with the broader confidential computing ecosystem. We introduce self-verifying remote attestation evidence. Each attestation bundle identifies its verification logic in the form of a WebAssembly component that is downloaded by the Verifier and executed. This approach transforms evidence verification into a platform-agnostic functionality that is implemented once for all platforms: the Verifier executes the supplied verification logic to validate the evidence and incorporates its hash and, when available, the signer's public key as identity claims into the attestation result. As a result, Verifiers can validate attestation evidence without any platform-specific code; the verification logic is just another measurement whose reference value can be checked with existing mechanisms. We implement this concept as TrustMee, a platform-agnostic verification driver for the Trustee framework. We demonstrate self-verifying evidence for AMD SNP, Intel TDX, Intel SGX, Kata agent-policy appraisal, and ReCFA control-flow attestations, producing attestation claims in the standard EAT format. The last two are part of the software stack, and not supported by existing Verifiers such as Trustee.

cs.CR↗

arXiv2026

The cohomology groups of finite cyclic covers of complexified real arrangement complements

In this paper, we study the cohomology groups of finite cyclic covers of complexified real arrangement complements. An open problem is whether the torsion in the (co)homology of finite covering spaces of hyperplane arrangement complements, including the classical Milnor fiber, is combinatorially determined. Using the chamber cochain complex constructed by Yoshinaga, we obtain explicit upper bounds for the Betti numbers of these covers over arbitrary fields. Furthermore, we introduce a combinatorial condition analogous to the Cohen-Dimca-Orlik (CDO) condition. Under this condition, we prove that the integral cohomology groups of the finite cyclic covers are torsion-free. This partially generalizes the recent results on complex line arrangements obtained by the author and Liu \cite[Theorem 1.3]{LX26}.

math.AG↗

arXiv2026

Universal valley filtering via uniform dissipation and velocity contrast

Valley, as a ubiquitous degree of freedom in lattices, has found wide applications in both electronic and classical-wave devices in recent years. However, achieving valley-polarized states, a prerequisite for valley-based operations, still remains challenging. Here, we propose and experimentally demonstrate a universal non-Hermitian mechanism for valley filtering using only uniform background dissipation, which creates a propagation length contrast between valleys through their intrinsic group velocity differences. We implement this concept in an acoustic crystal, observing switchable and robust valley polarization of sound through large-scale field mapping. Remarkably, our approach is solely based on uniform loss, without the need for any special lattice structures, tailored excitations, or external fields. We further provide designs of our non-Hermitian valley filter on photonic and electronic platforms. Our results offer a simple and effective solution to valley-polarized state generation and may advance the development of novel valley-based devices in both classical and quantum regimes.

cond-mat.mes-hall↗

arXiv2026

AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems

Recommendation algorithm iteration is moving from an artisanal, engineer-bound process toward an industrialized research loop, but this transition remains blocked by a structural execution bottleneck: the idea-to-launch cycle still depends on human engineers to generate hypotheses, modify production code, launch A/B experiments, and attribute online results. Innovation therefore scales linearly with headcount rather than compounding with evidence, compute, and accumulated experimental knowledge. We present AgentX, a production-deployed multi-agent system that fundamentally restructures this production function. AgentX operates as a self-evolving development engine: it autonomously generates, implements, evaluates, and learns from recommendation experiments at a scale and pace that no manual workflow can sustain. The system orchestrates four tightly coupled stages in a closed loop. A Brainstorm Agent synthesizes evidence from historical experiments, system architecture, data analysis, and external research into ranked, executable proposals. A Developing Agent translates each proposal into production-ready code through repository-grounded generation and multi-dimensional reliability verification. An Evaluation Agent conducts safe online rollout with guardrail-vetoed A/B judgment, converting both successes and failures into structured knowledge assets. A Harness Evolution layer (SGPO) then distills execution trajectories into semantic-gradient updates that continuously sharpen the agents themselves -- making the system not merely automated, but self-improving.

cs.AI↗

arXiv2026

Topological antilaser

Coherent perfect absorption (CPA)-the time-reversed operation of lasing at threshold-relies on finely tuned interference and is intrinsically fragile to disorder and structural imperfections. Whether absorption can be endowed with topological protection, by analogy to topological lasing, has remained an open question. Here, we experimentally demonstrate a topological antilaser: the time-reversed counterpart of a topological laser, in which chiral edge modes of a photonic lattice enable perfect light absorption protected by topology. Using a nonreciprocal microwave network with low intrinsic loss, we show that the topological antilaser preserves near-unity absorption under strong disorder, and, unlike conventional antilasers, remains functional for arbitrary placements of dissipation and input ports, even when the lattice is strongly perturbed. This robustness arises from the disorder-immune propagation and stable spatial profile of the topological edge modes. Our results establish topologically protected absorption as the missing counterpart of topological lasing, opening new directions for studying robust energy dissipation, wave control, and coherent-absorption-based detection technologies.

physics.optics↗

arXiv2026

From Classification to Generation: An Open-Ended Paradigm for Adverse Drug Reaction Prediction Based on Graph-Motif Feature Fusion

Computational biology offers immense potential for reducing the high costs and protracted cycles of new drug development through adverse drug reaction (ADR) prediction. However, current methods remain impeded by drug data scarcity-induced cold-start challenge, closed label sets, and inadequate modeling of label dependencies. Here we propose an open-ended ADR prediction paradigm based on Graph-Motif feature fusion and Multi-Label Generation (GM-MLG). Leveraging molecular structure as an intrinsic and inherent feature, GM-MLG constructs a dual-graph representation architecture spanning the atomic level, the local molecular level (utilizing fine-grained motifs dynamically extracted via the BRICS algorithm combined with additional fragmentation rules), and the global molecular level. Uniquely, GM-MLG pioneers transforming ADR prediction from multi-label classification into Transformer Decoder-based multi-label generation. By treating ADR labels as discrete token sequences, it employs positional embeddings to explicitly capture dependencies and co-occurrence relationships within large-scale label spaces, generating predictions via autoregressive decoding to dynamically expand the prediction space. Experiments demonstrate GM-MLG achieves up to 38% improvement and an average gain of 20%, expanding the prediction space from 200 to over 10,000 types. Furthermore, it elucidates non-linear structure-activity relationships between ADRs and motifs via retrosynthetic motif analysis, providing interpretable and innovative support for systematic risk reduction in drug safety.

cs.LG↗

arXiv2025

The homology groups of finite cyclic covering of line arrangement complement

In this paper, we study the first homology group of finite cyclic covering of complex line arrangement complement. We show that this first integral homology group is torsion-free under certain condition similar to the one used by Cohen-Dimca-Orlik. In particular, this includes the case of the Milnor fiber, which generalizes the previous results obtained by Williams for complexified line arrangement to any complex line arrangement.

math.AG↗

arXiv2025

Observation of returning Thouless pumping

Introduced by David Thouless in 1983, Thouless pumping exemplifies topological properties in topological systems, where the transported charge is quantized by the Chern number. Recently, returning Thouless pumping was theoretically proposed, in which quantized charge is pumped during the first half of the cycle but returns to zero in the second half. This mechanism leads to crystalline symmetry-protected delicate topological insulators. Unlike conventional topological bands, a delicate topological band is Wannierizable but not atomically obstructed, which features multicellular Wannier functions extending beyond a single unit cell. Here, by replacing the second dimension with a synthetic dimension, we realize a two-dimensional delicate topological insulator via a set of one-dimensional acoustic crystals with fine-tuned geometric parameters. Through acoustic bands and wavefunction measurements, we directly observe returning Thouless pumping and symmetric multicellular Wannier functions, followed by establishing the bulk-boundary correspondence between sub-Brillouin zone Chern numbers and gapless boundary modes. As enriched by crystalline symmetries, our experimental demonstration of returning Thouless pumping expands the current understanding of topological phases of matter.

cond-mat.mes-hall↗

arXiv2025

Double star arrangement and the pointed multinet

Let $\mathcal{A}$ be a hyperplane arrangement in a complex projective space. It is an open question if the degree one cohomology jump loci (with complex coefficients) are determined by the combinatorics of $\mathcal{A}$. By the work of Falk and Yuzvinsky \cite{FY}, all the irreducible components passing through the origin are determined by the multinet structure, which are combinatorially determined. Denham and Suciu introduced the pointed multinet structure to obtain examples of arrangements with translated positive-dimensional components in the degree one cohomology jump loci \cite{DS}. Suciu asked the question if all translated positive-dimensional components appear in this manner \cite{Suc14}. In this paper, we show that the double star arrangement introduced by Ishibashi, Sugawara and Yoshinaga \cite[Example 3.2]{ISY22} gives a negative answer to this question.

math.AG↗

arXiv2025

Non-Hermitian Dirac cones with valley-dependent lifetimes

Relativistic quasiparticles emerging from band degeneracies in crystals play crucial roles in the transport and topological properties of materials and metamaterials. Quasiparticles are commonly described by Hermitian Hamiltonians, with non-Hermiticity usually considered detrimental. In this work, we show that such an assumption of Hermiticity can be lifted to bring quasiparticles into non-Hermitian regime. We propose a concrete lattice model containing two Dirac cones with valley-dependent lifetimes. The lifetime contrast enables an ultra-strong valley selection rule: only one valley can survive in the long-time limit regardless of the excitation, lattice shape and other details. This property leads to an effective parity anomaly with a single Dirac cone and offers a simple way to generate vortex states. Additionally, extending non-Hermitian features to boundaries generates valley kink states with valley-locked lifetimes, making them effectively unidirectional and more resistant against inter-valley scattering. All these phenomena are experimentally demonstrated in a non-Hermitian electric circuit lattice.

cond-mat.mes-hall↗

arXiv2024

Two-Stage Constrained Actor-Critic for Short Video Recommendation

The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users sequentially interact with the system and provide complex and multi-faceted responses, including watch time and various types of interactions with multiple videos. One the one hand, the platforms aims at optimizing the users' cumulative watch time (main goal) in long term, which can be effectively optimized by Reinforcement Learning. On the other hand, the platforms also needs to satisfy the constraint of accommodating the responses of multiple user interactions (auxiliary goals) such like, follow, share etc. In this paper, we formulate the problem of short video recommendation as a Constrained Markov Decision Process (CMDP). We find that traditional constrained reinforcement learning algorithms can not work well in this setting. We propose a novel two-stage constrained actor-critic method: At stage one, we learn individual policies to optimize each auxiliary signal. At stage two, we learn a policy to (i) optimize the main signal and (ii) stay close to policies learned at the first stage, which effectively guarantees the performance of this main policy on the auxiliaries. Through extensive offline evaluations, we demonstrate effectiveness of our method over alternatives in both optimizing the main goal as well as balancing the others. We further show the advantage of our method in live experiments of short video recommendations, where it significantly outperforms other baselines in terms of both watch time and interactions. Our approach has been fully launched in the production system to optimize user experiences on the platform.

cs.LG↗

arXiv2023

Reinforcing User Retention in a Billion Scale Short Video Recommender System

Recently, short video platforms have achieved rapid user growth by recommending interesting content to users. The objective of the recommendation is to optimize user retention, thereby driving the growth of DAU (Daily Active Users). Retention is a long-term feedback after multiple interactions of users and the system, and it is hard to decompose retention reward to each item or a list of items. Thus traditional point-wise and list-wise models are not able to optimize retention. In this paper, we choose reinforcement learning methods to optimize the retention as they are designed to maximize the long-term performance. We formulate the problem as an infinite-horizon request-based Markov Decision Process, and our objective is to minimize the accumulated time interval of multiple sessions, which is equal to improving the app open frequency and user retention. However, current reinforcement learning algorithms can not be directly applied in this setting due to uncertainty, bias, and long delay time incurred by the properties of user retention. We propose a novel method, dubbed RLUR, to address the aforementioned challenges. Both offline and live experiments show that RLUR can significantly improve user retention. RLUR has been fully launched in Kuaishou app for a long time, and achieves consistent performance improvement on user retention and DAU.

cs.LG↗

arXiv2021

Video Relation Detection with Trajectory-aware Multi-modal Features

Video relation detection problem refers to the detection of the relationship between different objects in videos, such as spatial relationship and action relationship. In this paper, we present video relation detection with trajectory-aware multi-modal features to solve this task. Considering the complexity of doing visual relation detection in videos, we decompose this task into three sub-tasks: object detection, trajectory proposal and relation prediction. We use the state-of-the-art object detection method to ensure the accuracy of object trajectory detection and multi-modal feature representation to help the prediction of relation between objects. Our method won the first place on the video relation detection task of Video Relation Understanding Grand Challenge in ACM Multimedia 2020 with 11.74\% mAP, which surpasses other methods by a large margin.

cs.CV↗