LoomRank Weekly · All Categories · Top 25
Week 3, May 2026 | Score range: 83-88 | Candidate pool: 75 papers
01 Physics (13 papers)### 01 Non-Abelian String-Breaking Dynamics on a Qudit Quantum Computer
Why can quarks never exist in isolation? The answer may lie in the breaking of a “string.” In a groundbreaking experiment, researchers have, for the first time, observed the real-time dynamics of non-Abelian string breaking on a quantum computer. When a magnetic flux string connecting two color charges is stretched to a critical length, it spontaneously snaps, generating new particle pairs. Using high-dimensional qudits to simulate a non-Abelian gauge theory, the team captured the quantum state evolution at the moment of string breaking and discovered that this process involves a nontrivial exchange of topological quantum numbers—a hallmark of non-Abelian behavior. This breakthrough not only offers an unprecedented dynamic perspective on quark confinement, a fundamental phenomenon in particle physics, but also highlights the unique advantage of quantum computing in simulating the evolution of strongly interacting matter, paving the way for exploring nonequilibrium processes in quantum chromodynamics.
LoomRank: 88 | Category: Quantum Physics (quant-ph) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.05841
03 Experimental Evidence of Fractional Entropy in Critical Kondo Systems
Why does Fermi liquid theory break down in strongly correlated electron systems? The answer may lie in a peculiar quantum state—non-Abelian anyons. These quasiparticles possess non-integer quantum dimensions greater than one, enabling the nonlocal encoding and protected processing of information required for topological quantum computing. Yet, their definitive characterization has remained an experimental challenge. A recent study precisely tuned the electron correlation strength in a critical Kondo system and measured entropy deviations from integer values near the quantum critical point—a fractional signature that directly points to the existence of non-Abelian anyons. This discovery not only provides crucial experimental evidence for understanding unconventional quantum states in strongly correlated electron systems, but also paves the way for designing topological qubits and advancing fault-tolerant quantum computing.
LoomRank: 87 | Category: Mesoscale and Nanoscale Physics (cond-mat.mes-hall) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.00669
04 Dzyaloshinskii-Moriya interaction as a coherence diagnostic for chirality-induced spin selectivity
How do chiral molecules achieve spin selectivity? At the heart of this puzzle lies a fundamental question: when electrons traverse a chiral molecular bridge, do they undergo coherent SU(2) spin rotation or experience incoherent spin-dependent filtering? A research team has uncovered a key criterion—the Dzyaloshinskii-Moriya interaction term within superexchange interactions, whose symmetry properties can clearly distinguish between these two mechanisms. When coherent CISS manifests as unitary spin rotation of tunneling electrons, this interaction exhibits specific chiral symmetry breaking; incoherent processes, however, lack this signature. This discovery not only provides a diagnostic tool for a long-standing debate in molecular spintronics but also directly links the physical mechanism of chirality-induced spin selectivity to quantum coherence. From asymmetric catalysis to quantum information processing, this criterion may pave the way for designing novel chiral spintronic devices.
LoomRank: 87 | Category: Mesoscale and Nanoscale Physics (cond-mat.mes-hall) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.06008
05 Bulk-Edge Correspondence via Higher Gauge Theory
In quantum materials, what lies deeper than the bulk topological order is how the system “unravels” itself through gapless excitations at its boundary. Can this bulk-boundary correspondence—central to the experimental realization of the fractional quantum Hall effect—be captured with more precise mathematical language? The research team reinterpreted it through an effective relativistic gauge theory, where the choice of a classifying fiber bundle governs the core structure. They discovered that the complex Hopf fiber bundle serves precisely as the classifier for both bulk and boundary topological effects. This geometric structure not only reveals the origin of boundary excitations but also predicts a deeper correspondence between bulk topological order and gapless edge modes. The finding provides a unified theoretical framework for understanding bulk-boundary correspondence in fractional quantum Hall systems and opens new mathematical pathways for exploring boundary physics in other topological quantum materials.
LoomRank: 87 | Category: High Energy Physics - Theory (hep-th) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.10232
08 Gamma Factory: A New Experimental Paradigm for CERN’s HL-LHC–FCC-ee Transition
How can the Large Hadron Collider unlock entirely new scientific potential during its upgrade transition? A proposal called the “Gamma Factory” aims to transform the LHC’s ion beams into an unprecedented source of photons. The core idea is to produce, accelerate, and store highly relativistic partially stripped ions within the LHC—these ions act like efficient atomic traps whose internal degrees of freedom can be excited by lasers, emitting quasi-monochromatic gamma rays with energies up to hundreds of MeV. This approach not only promises to fill the experimental gap between the HL-LHC and FCC-ee but also opens up a new paradigm for cross-disciplinary research spanning particle physics, nuclear physics, atomic physics, and applied physics. From detecting new particles beyond the Standard Model to studying controlled excitation of nuclear isomers, the Gamma Factory could become one of CERN’s most transformative experimental platforms over the next decade.
LoomRank: 85 | Category: Accelerator Physics (physics.acc-ph) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.04240
10 Wormholes and the imaginary distance bound
Are wormholes merely mathematical phantoms? A new study reveals that the simplest wormhole solutions—involving massless scalar fields with imaginary values—may imply a fundamental constraint. In asymptotically flat or asymptotically AdS gravitational theories, massless fields can be interpreted as coupling constants; the wormhole effect then suggests a “virtual distance bound”—an upper limit on the analytic continuation of these coupling constants into imaginary values. In string theory examples, the research team identified clear effects that cause the low-energy theory to break down before or precisely at this bound. This discovery not only sets a boundary on the physical reality of wormholes but also offers a fresh perspective on the analyticity of coupling constants in quantum gravity—perhaps imaginary space is not an infinite playground, but a courtyard with walls.
LoomRank: 85 | Category: High Energy Physics - Theory (hep-th) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.05336
12 Robust spin-squeezing on quantum networks: the lesson from universality
Can spin squeezing in quantum networks overcome the constraints of geometric disorder? A new study reveals that the answer lies in “spectral dimension”—a universal parameter that characterizes the topology of interaction graphs. The research team discovered that spin ensembles embedded in arbitrary non-uniform network geometries exhibit two distinct squeezing regimes: in OAT-type scalable squeezing, the behavior is entirely governed by the universal properties of the interaction graph, controlled by the spectral dimension; in critical squeezing, the spectral dimension only provides a necessary condition for scalable metrology. This finding offers a new framework for understanding many-body entanglement dynamics in quantum networks—when the network topology meets specific spectral dimension conditions, the robustness of spin squeezing can surpass the interference of geometric disorder, paving the way for distributed quantum sensing and precision measurements in quantum networks.
LoomRank: 84 | Category: Quantum Physics (quant-ph) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.03032
13 Hierarchical entanglement transitions and hidden area-law sectors in quantum many-body dynamics
Is it too simplistic to assume that chaotic many-body dynamics always evolve a low-entanglement initial state into volume-law entanglement? A new study reveals a hidden hierarchical entanglement structure in local quantum quenches. In the canonical purification of a locally quenched Gibbs state and its associated pure-state circuit model, the full quantum state exhibits a Rényi-index-controlled phase transition: for Rényi index α > 1, long-time entanglement follows an area law, while for α ≤ 1, it obeys a volume law. More strikingly, the system’s response to local perturbations—manifested as an “echo” effect in entanglement entropy—uncovers a “hidden area-law region” masked by the apparent volume-law behavior. This discovery offers a fresh perspective on the dynamical classification of quantum many-body systems: the hierarchical structure of entanglement entropy hints at unrecognized conservation laws or symmetry-protected phases within chaotic systems, with potential applications extending to the manipulation of entanglement resources in quantum information processing.
LoomRank: 84 | Category: Quantum Physics (quant-ph) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.04540
14 Giant orbital-magnon conversion driven perpendicular magnetization switching
Can orbital angular momentum and magnons communicate directly? A new study provides a definitive yes. Traditionally, spin-orbit coupling has been considered the core mechanism for controlling magnetization, but the direct conversion between orbital angular momentum and magnons—the quantized quasiparticles of collective spin excitations in magnetic materials—had never been confirmed. By designing a specific heterostructure, the research team achieved, for the first time, an efficient conversion of orbital angular momentum into magnons, which then drove a perpendicular magnetization reversal. This discovery not only reveals a long-overlooked physical channel of orbital-magnon coupling but also opens a new path for using orbital degrees of freedom as information carriers, offering a novel control dimension for nanoelectronic devices that could extend beyond the limits of Moore’s law.
LoomRank: 84 | Category: Mesoscale and Nanoscale Physics (cond-mat.mes-hall) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.04486
16 An ultra-broadband axion dark matter experiment
Could axions, the elusive dark matter particles, be hiding in the squared response of electromagnetic fields? Traditional search experiments rely on the linear effect of axion-photon coupling, which is limited to narrow frequency bands and constrained by resonance conditions. This study proposes a groundbreaking strategy: using a physical quantity controlled by the square of the axion field as an observation window. Specifically, the research team biases a direct-current superconducting quantum interference device (dc SQUID) at its magnetic flux “sweet spot,” where the voltage exhibits a quadratic relationship with flux, and then employs lock-in modulation to suppress low-frequency noise. This design enables a detection bandwidth spanning 15 orders of magnitude in axion mass, covering candidates from extremely light to superheavy without the need for tuning. This approach not only opens a full-spectrum path for axion dark matter searches but also highlights the unique potential of quantum sensors in fundamental physics—when linear responses fail, nonlinear effects may hold the key to breakthroughs.
LoomRank: 84 | Category: High Energy Physics - Phenomenology (hep-ph) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.11078
18 Quantum compressed sensing
How many measurements are truly needed for signal acquisition? In 1948, Shannon’s information theory provided a classic answer, but in 2006, compressed sensing (CS) rewrote it as M = O(K log(N/K)). Now, a new study introduces quantum compressed sensing (QCS), redefining signal acquisition as a process of quantum unitary evolution. By encoding high-dimensional signal information into the evolution path of quantum states, QCS leverages quantum superposition and entanglement to further reduce the required number of measurements below the classical limit. This breakthrough not only challenges the boundaries of traditional information theory but also reveals the fundamental advantage of quantum resources in signal processing: when the signal structure satisfies sparsity, quantum parallelism can exponentially reduce sampling costs. This work opens new avenues for efficient data acquisition in quantum sensing, quantum imaging, and even quantum machine learning, heralding a quantum-era rewrite of information theory.
LoomRank: 84 | Category: Quantum Physics (quant-ph) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.15784
21 Programmable Integrated Magnonic Meshes
Can spin waves replace electrons as the next-generation carriers for information processing? A recent study has pushed integrated magnonics to new heights by constructing a programmable integrated magnon network, achieving for the first time the flexible routing and control of spin waves in chip-scale circuits. Traditional magnonic devices are limited to isolated components or short-distance transmission, making complex functions difficult to realize. This work, however, uses micro-nano fabrication to integrate multiple magnon waveguides and tunable couplers on a single chip, forming a “magnon network” analogous to electronic circuits. By precisely controlling the phase and amplitude of spin waves with external magnetic fields, the researchers demonstrated signal splitting, interference, and reconstruction—essentially building a programmable “logic maze” for spin waves. This breakthrough not only provides a new platform for ultra-low-power microwave signal processing but also hints at the potential for magnonics to evolve from single devices to large-scale integrated systems, paving the way for future analog computing and quantum information processing.
LoomRank: 83 | Category: Applied Physics (physics.app-ph) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.00290
25 The unique, universal entropy for complex systems
How should entropy be defined for complex systems? For decades, researchers overlooked a crucial requirement: entropy must measure uncertainty at the information scale of the “maximizing distribution”—specifically, the critical point where the log-log slope equals exactly -1. A new study fills this gap by starting from fundamental axioms. The authors demonstrate that entropy must also satisfy extensivity within the complete universality class defined by Hanel and Thurner. As a result, coupled entropy—maximized by the coupled stretched exponential distribution—emerges as the only universal form meeting all conditions. This discovery not only provides a solid axiomatic foundation for the statistical mechanics of complex systems but also opens a unified quantitative framework for understanding nonequilibrium behavior, from biological networks to social systems.
LoomRank: 83 | Category: Statistical Mechanics (cond-mat.stat-mech) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.04493
02 Computer Science (6 papers)### 02 Neural Weight Norm = Kolmogorov Complexity
Why does weight decay work so well? A new study reveals a deep connection between this common regularization technique and algorithmic information theory. Under fixed precision, the minimum weight norm required for a recurrent neural network to output a binary string is exactly equal—up to a logarithmic factor—to the Kolmogorov complexity of that string, which measures the length of the shortest program that generates it. This means weight decay implicitly introduces a prior distribution that matches Solomonoff’s universal prior, the optimal prior over computable functions, whose performance is only a polynomial factor away from ideal. Remarkably, this result holds regardless of the choice of norm: under fixed precision, all weight norms ultimately reduce to counting non-zero parameters. This discovery not only provides a theoretical foundation for the mechanism of weight decay but also tightly links regularization strategies in deep learning to the mathematical bedrock of general intelligence—Kolmogorov complexity—offering a new perspective on how neural networks learn simple, universal representations.
LoomRank: 88 | Category: Machine Learning (cs.LG) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.10878
06 Universal Approximation of Nonlinear Operators and Their Derivatives
How can a neural network not only learn a nonlinear operator but also accurately approximate its derivative? This seemingly esoteric mathematical question lies at the heart of fields ranging from fluid dynamics to materials design, where many problems require simultaneously predicting outputs and their sensitivity to inputs. Within the framework of Banach spaces, a research team has proven, for the first time, a universal approximation theorem for nonlinear k-times differentiable operators, establishing a rigorous theoretical foundation for the emerging frontier of “derivative-informed operator learning.” This breakthrough not only fills a gap in operator learning theory but also suggests that future AI models could simultaneously output results and gradients in physical simulations, accelerating inverse problem solving and parameter optimization—essentially equipping scientific computing with eyes that can “see through” causal relationships.
LoomRank: 86 | Category: Machine Learning (cs.LG) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.15285
09 Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves
How can deep learning efficiently process signals on manifolds? Traditional convolutional networks rely on regular grid structures, but real-world signals—such as time series, probability distributions, or operators—are often defined on irregular manifolds and are inherently infinite-dimensional. A new study introduces a unified convolutional learning framework for such signals by incorporating Hilbert bundles and cellular sheaf theory. The key lies in defining convolution operations using the connection Laplacian operator associated with the manifold—a differential operator that captures both local geometry and global topological structure. This framework not only unifies existing architectures like graph neural networks and manifold networks under a single theory but also reveals their deep connections to structures in geometry and topology. By providing a rigorous mathematical foundation for processing complex geometric data, this work promises to advance signal processing and pattern recognition in fields such as physics and materials science.
LoomRank: 85 | Category: Machine Learning (cs.LG) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.06395
15 GRAFT-ATHENA: Self-Improving Agentic Teams for Autonomous Discovery and Evolutionary Numerical Algorithms
Can physicists train AI to develop a kind of “scientific intuition,” much like how neural networks learn? A new study introduces the GRAFT-ATHENA framework, which goes far beyond simply asking a large language model to perform a single task. Instead, it builds a self-improving team of multiple agents, each responsible for planning, solving, or evaluating. The key innovation lies in modeling scientific discovery as a series of probabilistic decisions and incorporating a shared “experience base” that allows solution paths from different problems to cross-learn from one another. The team tested this framework on the classic challenge of evolving numerical algorithms. The AI not only autonomously combined efficient algorithm variants but also extracted structured “methodological actions” from its failures, forming a reusable knowledge graph. This work offers a scalable evolutionary path for “AI scientists” to move from isolated experiments toward continuous, autonomous discovery.
LoomRank: 84 | Category: Machine Learning (cs.LG) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.11117
19 Learning to Hand Off: Provably Convergent Workflow Learning under Interface Constraints
When multiple agents hand off control through a shared artifact but can only observe local information, how can globally optimal workflow learning be achieved? This challenge is especially pronounced in large language model pipelines that span across organizations and vendors. The research team formalized this scenario as an “interface-constrained semi-Markov decision process,” where decision moments occur only at handoff points, and each agent can only access the local functions of the artifact and its own private state. Surprisingly, they proved that even without a central learner accessing the joint trajectory, a distributed algorithm based on handoff-point rewards can still guarantee convergence to the optimal policy. This finding provides a theoretical foundation for decentralized collaboration in multi-agent systems and paves the way for building AI pipelines that cross trust boundaries—when each node can only see the tip of the iceberg, a globally optimal solution remains within reach.
LoomRank: 84 | Category: Artificial Intelligence (cs.AI) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.19140
22 Polynomial-Time Optimal Group Selection via the Double-Commutator Eigenvalue Problem
Why is group selection so difficult? When faced with an M-dimensional observation with an unknown covariance structure, how can one identify, from the exponentially many subgroups of the symmetric group S_M, the finite group that best matches the spectral decomposition of the covariance? A new study introduces a “double commutator eigenvalue problem,” transforming this combinatorial optimization challenge into an algebraically tractable structure solvable in polynomial time. The core breakthrough lies in the discovery that the optimal group is not found through brute-force enumeration but is uniquely determined by the spectral relationship between the observed covariance matrix and a specific commutator operator—a connection that deeply couples group theory with linear algebra, reducing search complexity from exponential to polynomial. This framework removes a key obstacle for the theory of “algebraic diversity,” a statistical method that replaces time averaging over multiple observations with algebraic group actions on a single observation, and offers a novel computational paradigm for high-dimensional signal processing and quantum state tomography.
LoomRank: 83 | Category: Machine Learning (cs.LG) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.00834
03 Mathematics (3 papers)### 07 Primitive sets and von Mangoldt chains: Erdős Problem #1196 and beyond
Can integers in a set avoid dividing one another? This seemingly simple question in number theory conceals a deep conjecture first posed by Paul Erdős in 1935. Inspired by outputs from GPT-5.4 Pro, the research team introduced a Markov chain method based on von Mangoldt weights, providing a new upper bound for the Erdős sum—a key measure of the “density” of primitive sets. This approach cleverly bypasses a blind spot that had persisted in the literature for decades, and directly proves two conjectures proposed by Erdős, Sárközy, and Szemerédi in 1966: problem #1196 concerning primitive sets of large numbers, and a deeper structural conjecture about divisibility. This work not only achieves a dual breakthrough—both computational and theoretical—in a classic problem of number theory, but also hints at the immense potential of artificial intelligence to uncover mathematical pathways long forgotten by humans.
LoomRank: 85 | Category: Number Theory (math.NT) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.00301
11 From Graph Laplacians to String Partition Functions: A Rigorous Pathway from Discrete Spectra to Emergent Geometry
How does the discrete spectrum of a graph Laplacian weave the continuous geometry of spacetime in string theory? This study builds a rigorous mathematical bridge between spectral graph theory, algebraic geometry, and string theory. The authors construct a compact Riemann surface—the spectral curve—for any finite graph, whose period matrix precisely encodes the coarse-grained spectral information of the graph. Remarkably, when a sequence of graphs converges to a Riemannian manifold, these spectral curves converge within the Deligne-Mumford compactification to the geometric objects predicted by string theory. This discovery not only provides a strict mathematical pathway for the emergence of continuous geometry from discrete data, but also suggests that spacetime itself may be the continuous limit of a vast graph network—offering a solid mathematical foundation for graph-theoretic models of quantum gravity.
LoomRank: 84 | Category: Combinatorics (math.CO) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.00452
24 Lectures on Condensed Mathematics
How does the mathematical concept of “condensation” reshape the boundaries between geometry and algebra? In the lecture notes from the University of Bonn’s 2019 summer course, the author, together with Dustin Clausen, developed the theory of condensed mathematics, offering a new framework for integrating topological spaces with algebraic structures. By introducing the notion of “condensed sets”—mathematical objects finer than topological spaces that simultaneously capture continuity and discreteness—this theory resolves many long-standing technical challenges in classical categories. The notes systematically present core definitions, key theorems, and typical applications, gradually revealing how this theory provides a more solid linguistic foundation for modern mathematics, from condensed modules to derived categories, and from locally compact spaces to analytic geometry. This updated version includes only minor corrections and formatting improvements, aiming to serve as a stable, citable reference for researchers. The emergence of condensed mathematics may, like sheaf theory in its time, quietly transform our fundamental understanding of mathematical structures.
LoomRank: 83 | Category: Number Theory (math.NT) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.03658
04 Other (3 papers)### 17 Unification of Signal Transform Theory
Fourier transforms, cosine transforms, wavelet transforms—do these seemingly disparate signal processing tools share a hidden mathematical structure? A new study answers with a resounding yes. Starting from representation theory, the authors reveal a core principle: every classical transform—whether discrete DFT, DCT, Walsh-Hadamard, or continuous Fourier and spherical harmonics—is fundamentally the eigenbasis of a covariance matrix under the action of a specific finite or compact group. Its column vectors are direct constructions of the irreducible matrix elements of that group. This unified framework not only places dozens of transforms into a single mathematical lineage but also implies that in any signal processing task, choosing a transform is essentially choosing a symmetry. It offers a concise and powerful perspective for understanding the deep structure of signal transforms and paves the way for designing novel transforms tailored to specific symmetries.
LoomRank: 84 | Category: Signal Processing (eess.SP) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.11589
20 The Economics of Model Collapse: Equilibrium, Welfare, and Optimal Provenance Subsidies in Synthetic Data Markets
Generative AI is reshaping the supply landscape of training data—an increasing number of newly generated texts, images, and structured records no longer originate from humans but from previous generations of models. When models are recursively trained on synthetic data, distribution fidelity undergoes a measurable and often irreversible degradation known as “model collapse.” Beneath this phenomenon lies a fundamental economic question: how can we design optimal incentive mechanisms for the synthetic data market? This study constructs, for the first time, a unified microeconomic theory under model collapse, revealing the welfare effects of data sources in market equilibrium and proposing an “optimal source subsidy” scheme—subsidizing human-generated original data while curbing excessive recycling of synthetic data to delay or prevent the collapse of model performance. This framework not only provides a theoretical foundation for the sustainability of AI training data but also offers policymakers a quantitative tool to balance innovation with data quality.
LoomRank: 84 | Category: General Economics (econ.GN) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.20279
23 Deep Speckle Holography Redefines Label-free Nanoparticle Phenotyping
Can the identity of nanoparticles be decoded in a single, label-free measurement? Conventional wisdom holds that the size, shape, composition, and abundance of particles in a mixed fluid cannot be simultaneously retrieved from a single label-free measurement. By revisiting this long-standing limitation, this study reveals that the forward-scattering speckle holographic field—a complex speckle pattern formed by the interference between scattered light from particles and a reference beam—actually defines an information-rich optical space capable of encoding multidimensional particle features. The research team proposes “deep speckle holography,” a generative approach that integrates physical models, successfully extracting multiple key attributes of nanoparticles from a single label-free measurement. This breakthrough not only overturns traditional assumptions in particle metrology but also opens new avenues for real-time, high-throughput nanoparticle phenotyping in biological fluids, with potential to play a critical role in disease diagnosis and environmental monitoring.
LoomRank: 83 | Category: Image and Video Processing (eess.IV) | Submitted: 2026-05 | ✓ Verified
arXiv:2605.01982
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