arXiv subjects

Jing Ding

Publications and source records attributed to Jing Ding.

At least 19 recordsLinked to original sources

arXiv2026

A Photonic-CXL Memory Appliance for Scalable KV Cache Management in LLM Inference

LLM inference at scale faces a memory wall. The KV cache demands tens of terabytes at hundreds of gigabytes per second, yet no current memory tier delivers both at once. Characterization across multi-generation GPU systems with various LLaMA models shows host memory retrieval achieves up to 100x speedup over re-computation but supports only tens of concurrent long-context users. Electrical CXL pooling theoretically bridges this gap, but switch latency, cable reach limits, and power-scaling issues prevent practical TB-scale deployments. We present the Marvell Photonic Fabric Memory Appliance, a photonic-CXL hybrid architecture replacing electrical switches with a passive fiber shuffle to deliver 32 TB shared memory across 16 hosts via a switch-free full- crossbar topology. Emulation results demonstrate over 50 percent latency reduction versus electrical CXL pools. Simulation results show that the PF Memory Appliance eliminates cache eviction cliffs by improving time-to-first-token by 6.6x for multi-turn conversations workloads.

cs.PF↗

arXiv2026

Quantum Anomalous Hall Effect in Rhombohedral Multilayer Graphene/hBN Moir\'e Superlattices

The recent discovery of robust quantum anomalous Hall (QAH) effect in rhombohedral multilayer graphene (RMG) aligned with hexagonal boron nitride (hBN) has established a highly versatile platform for correlated topological matter. This review synthesizes the experimental and theoretical progress in understanding these interaction-driven topological phases. Experimentally, the landscape has rapidly expanded from initial Chern insulators in trilayer systems to fully quantized QAH states in thicker systems. Theoretically, it is believed that moir\'e potential and electron-electron interaction cooperate and produce the QAH effect in such systems. Theoretical calculations also bring interesting questions, such as the formation of an interaction-driven topological phase known as an anomalous Hall crystal (AHC). This review comprehensively covers the experimental hallmarks, the theoretical frameworks, including continuum models and many-body approaches, and the ensuing physical picture that reconciles the roles of interactions, displacement fields, and the moir\'e potentials. We conclude by outlining outstanding open questions and future directions, positioning RMG/hBN systems at the forefront of topological quantum matter.

cond-mat.str-el↗

arXiv2026

Spin-orbit-driven quarter semimetals in rhombohedral graphene

Semimetals exhibit intriguing characteristics attributed to the coexistence of both electrons and holes. In rhombohedral multilayer graphene, a strong trigonal warping effect gives rise to a semi-metallic state near the Fermi surface, offering unique opportunities to explore the interplay of semi-metallic properties with strong correlations and topologies. Here, the observation of quarter semimetals in rhombohedral multilayer graphene by introducing spin-orbit coupling (SOC) is reported. The semi-metallic characteristics of rhombohedral graphene manifest as nearly vanished Hall resistance and parabolic longitudinal resistance. The strong correlations arising from the surface flat band lead to spontaneous symmetry breaking. SOC proximitized by WSe2 further lifts the valley degeneracy, resulting in the spontaneous time-reversal symmetry breaking, as evidenced by the hysteretic anomalous Hall effect. The coexistence of fully polarized electrons and holes allows for the observation of a non-monotonic temperature dependence of the anomalous Hall resistance. Furthermore, the application of moderate magnetic fields induces a phase transition from quarter semimetals to Chern insulators. These findings establish rhombohedral multilayer graphene as an ideal platform for studying strong correlations and topologies in semimetals.

cond-mat.mes-hall↗

arXiv2026

Layer-engineered quantum anomalous Hall effect in twisted rhombohedral graphene

Realizing programmable topological states in quantum anomalous Hall (QAH) insulators requires the ability to design and dynamically tune their topological invariant, the Chern number C. Here, we report a designer QAH platform based on twisted rhombohedral graphene family, in which C becomes a programmable and electrically tunable degree of freedom. By engineering the layer configuration in twisted monolayer-rhombohedral N-layer graphene, denoted as (1+N)L, we realize QAH states with C=N at moire filling v=1, where the layer number N=3,4,5 directly sets the Chern number. Beyond such static layer programming, we demonstrate in-situ electrical control. In a twisted monolayer-trilayer device, the sign of C (chirality) can be switched by electrostatic doping or displacement field. Most strikingly, in twisted Bernal bilayer-rhombohedral tetralayer graphene denoted as (2+4)L, we drive a displacement-field-induced topological phase transition between two distinct QAH states with C=3 and C=4 in a single device. Our work establishes a layer-engineered and electrically tunable platform that transitions topological quantum matter from discovery to design, opening the way toward on-demand engineering of correlated topological states and reconfigurable topological electronics.

cond-mat.mes-hall↗

arXiv2025

Dissecting Embedding Bag Performance in DLRM Inference

As the size of DLRMs gets larger, the models must be partitioned across multiple GPUs or nodes of GPUs due to the size limitation of total HBM memory that can be packaged in a GPU. This partitioning adds communication and synchronization overhead of sending and receiving data across GPUs. We use the NCCL and NVSHMEM libraries to measure the performance of an Embedding Bag kernel implemented on H100 GPUs. We compare its performance across diOerent batch sizes, number of tables, table sizes, pooling factors, and embedding dimensions. For a large embedding table that spans multiple GPUs, we project the performance slowdown from distributing an embedding table across multiple GPUs.

cs.PF↗

arXiv2025

FAPEX: Fractional Amplitude-Phase Expressor for Robust Cross-Subject Seizure Prediction

Precise, generalizable subject-agnostic seizure prediction (SASP) remains a fundamental challenge due to the intrinsic complexity and significant spectral variability of electrophysiological signals across individuals and recording modalities. We propose FAPEX, a novel architecture that introduces a learnable fractional neural frame operator (FrNFO) for adaptive time-frequency decomposition. Unlike conventional models that exhibit spectral bias toward low frequencies, our FrNFO employs fractional-order convolutions to capture both high and low-frequency dynamics, achieving approximately 10% improvement in F1-score and sensitivity over state-of-the-art baselines. The FrNFO enables the extraction of instantaneous phase and amplitude representations that are particularly informative for preictal biomarker discovery and enhance out-of-distribution generalization. FAPEX further integrates structural state-space modeling and channelwise attention, allowing it to handle heterogeneous electrode montages. Evaluated across 12 benchmarks spanning species (human, rat, dog, macaque) and modalities (Scalp-EEG, SEEG, ECoG, LFP), FAPEX consistently outperforms 23 supervised and 10 self-supervised baselines under nested cross-validation, with gains of up to 15% in sensitivity on complex cross-domain scenarios. It further demonstrates superior performance in several external validation cohorts. To our knowledge, these establish FAPEX as the first epilepsy model to show consistent superiority in SASP, offering a promising solution for discovering epileptic biomarker evidence supporting the existence of a distinct and identifiable preictal state and clinical translation.

q-bio.NC↗

arXiv2025

Photonic Fabric Platform for AI Accelerators

This paper presents the Photonic FabricTM and the Photonic Fabric ApplianceTM (PFA), a photonic-enabled switch and memory subsystem that delivers low latency, high bandwidth, and low per-bit energy. By integrating high-bandwidth HBM3E memory, an on-module photonic switch, and external DDR5 in a 2.5D electro-optical system-in-package, the PFA offers up to 32 TB of shared memory alongside 115 Tbps of all-to-all digital switching. The Photonic FabricTM enables distributed AI training and inference to execute parallelism strategies more efficiently. The Photonic Fabric removes the silicon beachfront constraint that limits the fixed memory-to-compute ratio observed in virtually all current XPU accelerator designs. Replacing a local HBM stack on an XPU with a chiplet that connects to the Photonic Fabric increases its memory capacity and correspondingly its memory bandwidth by offering a flexible path to scaling well beyond the limitations of on-package HBM alone. We introduce CelestiSim, a lightweight analytical simulator validated on NVIDIA H100 and H200 systems. It is used to evaluate the performance of LLM reference and energy savings on PFA, without any significant change to the GPU core design. With the PFA, the simulation results show that up to 3.66x throughput and 1.40x latency improvements in LLM inference at 405B parameters, up to 7.04x throughput and 1.41x latency improvements at 1T parameters, and 60-90% energy savings in data movement for heavy collective operations in all LLM training scenarios. While these results are shown for NVIDIA GPUs, they can be applied similarly to other AI accelerator designs (XPUs) that share the same fundamental limitation of fixed memory to compute.

cs.PF↗

arXiv2025

Diverse high-Chern-number quantum anomalous Hall insulators in twisted rhombohedral graphene

Quantum anomalous Hall (QAH) insulators with high Chern number (C) enables multiple dissipationless edge channels for low-power-consumption electronics. We report the realization of multiple high-C QAH insulators including C=3,5,6, and 7 in twisted monolayer-rhombohedral pentalayer graphene. In twist angles of approximately 1.40{\deg}, we observe QAH effect with C=5 at a filling of one electron per moir\'e unit cell, persisting up to 2 Kelvin. Furthermore, incommensurate QAH insulators with C=5,6, and 7 emerge at partial fillings. In twist angles of 0.89{\deg}, Chern insulators with C=3 and C=6 appear at fillings of two and three electrons, respectively. Our findings establish twisted rhombohedral multilayer graphene as a highly tunable platform for multichannel, dissipationless electronics and for the exploration of exotic quantum Hall states beyond traditional Landau level paradigm.

cond-mat.mes-hall↗

arXiv2025

Vision-Language Models Are Not Pragmatically Competent in Referring Expression Generation

Referring Expression Generation (REG) is a core task for evaluating the pragmatic competence of vision-language systems, requiring not only accurate semantic grounding but also adherence to principles of cooperative communication (Grice, 1975). However, current evaluations of vision-language models (VLMs) often overlook the pragmatic dimension, reducing REG to a region-based captioning task and neglecting Gricean maxims. In this work, we revisit REG from a pragmatic perspective, introducing a new dataset (RefOI) of 1.5k images annotated with both written and spoken referring expressions. Through a systematic evaluation of state-of-the-art VLMs, we identify three key failures of pragmatic competence: (1) failure to uniquely identify the referent, (2) inclusion of excessive or irrelevant information, and (3) misalignment with human pragmatic preference, such as the underuse of minimal spatial cues. We also show that standard automatic evaluations fail to capture these pragmatic violations, reinforcing superficial cues rather than genuine referential success. Our findings call for a renewed focus on pragmatically informed models and evaluation frameworks that align with real human communication.

cs.CL↗

arXiv2025

Continuously tunable anomalous Hall crystals in rhombohedral heptalayer graphene

The interplay of electronic interactions and nontrivial topology can give rise to a wealth of exotic quantum states. A notable example is the formation of Wigner crystals driven by strong electron-electron interactions. When these electronic crystals emerge in a parent band carrying a large Berry curvature, they can exhibit topologically nontrivial properties as anomalous Hall crystals, spontaneously breaking both continuous translational symmetry and time-reversal symmetry. Here, we report the experimental observation of tunable anomalous Hall crystals in rhombohedral heptalayer graphene moir\'e superlattices. At filling factors near one electron per moir\'e unit cell (v=1), we identify a series of incommensurate Chern insulators with a Chern number of C=1. Furthermore, we observe spontaneous time-reversal symmetry breaking spanning the entire filling range from v=1 to v=2, manifesting as anomalous Hall effects with pronounced magnetic hysteresis. Notably, anomalous Hall crystals with a high Chern number C=3 are observed over generic fillings ranging from v=1.5 to v=2. These anomalous Hall crystals are incommensurate with the moir\'e superlattice and exhibit dispersive fan diagrams consistent with the Streda formula, with their positions continuously tunable through displacement fields. Remarkably, these partially filled Chern insulators display Chern numbers distinct from their parent bands. Our findings demonstrate the rich variety of electronic crystalline states in rhombohedral graphene moir\'e superlattices, offering valuable insights into the strongly correlated topological phases.

cond-mat.mes-hall↗

arXiv2025

InsQABench: Benchmarking Chinese Insurance Domain Question Answering with Large Language Models

The application of large language models (LLMs) has achieved remarkable success in various fields, but their effectiveness in specialized domains like the Chinese insurance industry remains underexplored. The complexity of insurance knowledge, encompassing specialized terminology and diverse data types, poses significant challenges for both models and users. To address this, we introduce InsQABench, a benchmark dataset for the Chinese insurance sector, structured into three categories: Insurance Commonsense Knowledge, Insurance Structured Database, and Insurance Unstructured Documents, reflecting real-world insurance question-answering tasks.We also propose two methods, SQL-ReAct and RAG-ReAct, to tackle challenges in structured and unstructured data tasks. Evaluations show that while LLMs struggle with domain-specific terminology and nuanced clause texts, fine-tuning on InsQABench significantly improves performance. Our benchmark establishes a solid foundation for advancing LLM applications in the insurance domain, with data and code available at https://github.com/HaileyFamo/InsQABench.git.

cs.CL↗

arXiv2024

PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling

Multimodal document understanding is a challenging task to process and comprehend large amounts of textual and visual information. Recent advances in Large Language Models (LLMs) have significantly improved the performance of this task. However, existing methods typically focus on either plain text or a limited number of document images, struggling to handle long PDF documents with interleaved text and images, especially for academic papers. In this paper, we introduce PDF-WuKong, a multimodal large language model (MLLM) that is designed to enhance multimodal question-answering (QA) for long PDF documents. PDF-WuKong incorporates a sparse sampler that operates on both text and image representations, significantly improving the efficiency and capability of the MLLM. The sparse sampler selects the paragraphs or diagrams most pertinent to user queries. To effectively train and evaluate our model, we construct PaperPDF, a dataset consisting of a broad collection of English and Chinese academic papers. Multiple strategies are proposed to build high-quality 1.1 million QA pairs along with their corresponding evidence sources. Experimental results demonstrate the superiority and high efficiency of our approach over other models on the task of long multimodal document understanding, surpassing proprietary products by an average of 8.6% on F1. Our code and dataset will be released at https://github.com/yh-hust/PDF-Wukong.

cs.CV↗

arXiv2024

Electric-field switchable chirality in rhombohedral graphene Chern insulators stabilized by tungsten diselenide

Chern insulators host topologically protected chiral edge currents with quantized conductance characterized by their Chern number. Switching the chirality of a Chern insulator, namely, the direction of the edge current, is highly challenging due to topologically forbidden backscattering but is of considerable importance for the design of topological devices. Nevertheless, this can be achieved by reversing the sign of the Chern number through a topological phase transition. Here, we report electrically switchable chirality in rhombohedral multilayer graphene-based Chern insulators. By introducing moire superlattices in rhombohedral heptalayer graphene, we observed a cascade of topological phase transitions at quarter electron filling of a moire band with the Chern number tunable from -1, 1 to 2. Furthermore, integrating monolayer tungsten diselenide at the moireless interface of rhombohedral decalayer graphene/h-BN superlattices stabilizes the Chern insulators, enabling quantized anomalous Hall resistance of h/2e^2. Remarkably, the Chern number can be switched from -1 to 2 using displacement fields. Our work establishes rhombohedral multilayer graphene moire superlattices as a versatile platform for topological engineering, with switchable chirality offering significant promise for integrating chiral edge currents into topological electronic circuits.

cond-mat.mes-hall↗

arXiv2024

Engineering band structures of two-dimensional materials with remote moire ferroelectricity

The stacking order and twist angle provide abundant opportunities for engineering band structures of two-dimensional materials, including the formation of moire bands, flat bands, and topologically nontrivial bands. The inversion symmetry breaking in rhombohedral-stacked transitional metal dichalcogenides (TMDCs) endows them with an interfacial ferroelectricity associated with an out-of-plane electric polarization. By utilizing twist angle as a knob to construct rhombohedral-stacked TMDCs, antiferroelectric domain networks with alternating out-of-plane polarization can be generated. Here, we demonstrate that such spatially periodic ferroelectric polarizations in parallel-stacked twisted WSe2 can imprint their moire potential onto a remote bilayer graphene. This remote moire potential gives rise to pronounced satellite resistance peaks besides the charge-neutrality point in graphene, which are tunable by the twist angle of WSe2. Our observations of ferroelectric hysteresis at finite displacement fields suggest the moire is delivered by a long-range electrostatic potential. The constructed superlattices by moire ferroelectricity represent a highly flexible approach, as they involve the separation of the moire construction layer from the electronic transport layer. This remote moire is identified as a weak potential and can coexist with conventional moire. Our results offer a comprehensive strategy for engineering band structures and properties of two-dimensional materials by utilizing moire ferroelectricity.

cond-mat.mtrl-sci↗

arXiv2024

Electronic ferroelectricity in monolayer graphene for multifunctional neuromorphic electronics

Ferroelectricity is intriguing for its spontaneous electric polarization, which is switchable by an external electric field. Expanding ferroelectric materials to two-dimensional limit will provide versatile applications for the development of next-generation nonvolatile devices. Conventional ferroelectricity requires the materials consisting of at least two constituent elements associated with polar crystalline structures. Monolayer graphene as an elementary two-dimensional material unlikely exhibits ferroelectric order due to its highly centrosymmetric hexagonal lattices. Nevertheless, two-dimensional moire superlattices offer a powerful way to engineer diverse electronic orders in non-polar materials. Here, we report the observations of electronic ferroelectricity in monolayer graphene by introducing asymmetric moire superlattice at the graphene/h-BN interface. Utilizing Hall measurements, the electric polarization is identified to stem from electron-hole dipoles, suggesting the electronic dynamics of the observed ferroelectricity. Standard polarization-electric field hysteresis loops, as well as unconventional multiple switchable polarization states, have been achieved. By in-situ comparing with control devices, we found that the electronic ferroelectricity in graphene moire systems is independent of layer number of graphene and the corresponding fine band structures. Furthermore, we demonstrate the applications of this ferroelectric moire structures in multi-state non-volatile data storage and the emulation of versatile synaptic behaviors, including short-term plasticity, long-term potentiation and long-term depression. This work not only enriches the fundamental understanding of ferroelectricity, but also demonstrates the promising applications of graphene in multi-state memories and neuromorphic computing.

cond-mat.mes-hall↗

arXiv2023

Inversion symmetry-broken tetralayer graphene probed by second harmonic generation

Symmetry breaking governs most fascinating phenomena in crystals, such as ferroelectricity, nonlinear optics, piezoelectricity, ferromagnetism, and superconductivity. In two-dimensional materials, a wide variety of tuning knobs presents extraordinary opportunities for engineering symmetry breaking, leading to the emergence and manipulation of novel physical properties. Recently, tetralayer graphene with ABCB stacking order is predicted to possess atypical elemental ferroelectricity arising from the symmetry breaking induced by its specific stacking configuration. Experimentally unveiling the stacking-order dependent symmetry in tetralayer graphene is crucial to understand the intricate properties in the emergent graphene allotropes. Here, we observe pronounced nonlinear optical second harmonic generation (SHG) in ABCB-stacked tetralayer graphene, but absent in both ABAB- and ABCA-stacked allotropes. Our results provide direct evidence of symmetry breaking in ABCB-stacked tetralayer graphene. The remarkable contrast in the SHG spectra of tetralayer graphene allows straightforward identification of ABCB domains from the other two kinds of stacking order and facilitates the characterization of their crystalline orientation. The employed SHG technique serves as a convenient tool for exploring the intriguing physics and novel nonlinear optics in ABCB-stacked graphene, where spontaneous polarization and intrinsic gapped flat bands coexist. Our results establish ABCB-stacked graphene as a unique platform for studying the rare ferroelectricity in non-centrosymmetric elemental structures.

cond-mat.mes-hall↗

arXiv2023

Surface ferromagnetism in rhombohedral heptalayer graphene moire superlattice

The topological electronic structure of crystalline materials often gives rise to intriguing surface states, such as Dirac surface states in topological insulators, Fermi arc surface states in Dirac semimetals, and topological superconductivity in iron-based superconductors. Recently, rhombohedral multilayer graphene has emerged as a promising platform for exploring exotic surface states due to its hosting of topologically protected surface flat bands at low energy, with the layer-dependent energy dispersion. These flat bands can promote electron correlations, leading to a plethora of quantum phenomena, including spontaneous symmetry breaking, superconductivity, ferromagnetism, and topological Chern insulators. Nevertheless, the intricate connection between the surface flat bands in rhombohedral multilayer graphene and the highly dispersive high-energy bands hinders the exploration of correlated surface states. Here, we present a method to isolate the surface flat bands of rhombohedral heptalayer (7L) graphene by introducing moire superlattices. The pronounced screening effects observed in the moire potential-modulated rhombohedral 7L graphene indicate its essential three-dimensional (3D) nature. The isolated surface flat bands favor correlated states on the surface in the regions away from charge-neutrality points. Most notably, we observe tunable surface ferromagnetism, manifested as an anomalous Hall effect with hysteresis loops, which is achieved by polarizing surface states using finite displacement fields. Our work establishes rhombohedral multilayer graphene moire superlattice as a unique 3D system for exploring correlated surface states.

cond-mat.mes-hall↗

arXiv2021

Conformal prediction for text infilling and part-of-speech prediction

Modern machine learning algorithms are capable of providing remarkably accurate point-predictions; however, questions remain about their statistical reliability. Unlike conventional machine learning methods, conformal prediction algorithms return confidence sets (i.e., set-valued predictions) that correspond to a given significance level. Moreover, these confidence sets are valid in the sense that they guarantee finite sample control over type 1 error probabilities, allowing the practitioner to choose an acceptable error rate. In our paper, we propose inductive conformal prediction (ICP) algorithms for the tasks of text infilling and part-of-speech (POS) prediction for natural language data. We construct new conformal prediction-enhanced bidirectional encoder representations from transformers (BERT) and bidirectional long short-term memory (BiLSTM) algorithms for POS tagging and a new conformal prediction-enhanced BERT algorithm for text infilling. We analyze the performance of the algorithms in simulations using the Brown Corpus, which contains over 57,000 sentences. Our results demonstrate that the ICP algorithms are able to produce valid set-valued predictions that are small enough to be applicable in real-world applications. We also provide a real data example for how our proposed set-valued predictions can improve machine generated audio transcriptions.

stat.ML↗