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Kai Sun

Publications and source records attributed to Kai Sun.

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

Reactive Power Beyond Sinusoids: Scalar Measurement Readouts and Information Limits

Reactive power is not uniquely defined for nonsinusoidal voltage and current waveforms because different scalar readouts can assign different values to the same measured voltage--current waveform pair. This paper characterizes which scalar readouts are selected by specific measurement and physical requirements, and quantifies the current information lost through scalarization. The scalar readout is first restricted by requiring bilinearity, alternation, and invariance to the measurement time origin. Requiring it to vanish for all smooth, strictly monotone, time-invariant memoryless resistors then forces the harmonic weights to be proportional to harmonic order. With consistency across harmonic cutoffs and fundamental-frequency calibration, this uniquely yields the classical derivative, or Iliovici-type, scalar within the stated class. The selected weighting is also incompatible with conventional reactive-power calibration independently at every harmonic. Furthermore, for known nonzero voltage, active power and a finite set of skew scalar readouts leave an affine family of compatible currents of exact dimension \(d-1-r_v\). These results establish explicit selection and information-loss limits for scalar reactive-power measurements under nonsinusoidal conditions.

eess.SY↗
arXiv2026

Mineral Detection of Neutrinos and Dark Matter 2026 Proceedings

The fourth "Mineral Detection of Neutrinos and Dark Matter" (MDvDM'26) meeting was held April 14-17, 2026 in Karlsruhe, Germany, hosted by the Institute for Astroparticle Physics (IAP) at Karlsruhe Institute of Technology (KIT). These proceedings detail the contributions that were presented during MDvDM'26, illustrating the unprecedented progress in theoretical, computational and experimental studies towards the realization of the concept of mineral detectors. Mineral detectors represent an emerging particle detection concept that has risen in prominence in recent years due to the advent of modern computational and high-resolution microscopy techniques. Natural and synthetic crystals are capable of retaining microscopic damage features induced by nuclear recoils, which could be then read out with a variety of micrometer and nanometer resolution microscopy techniques. On laboratory time scales mineral detectors could be employed for reactor neutrino monitoring and dark matter detection, with the potential to measure the directions as well as the energies of the induced nuclear recoils. Uniquely, ancient natural crystals (so-called paleo-detectors) that have been recording nuclear recoils over geological timescales could be used for studying astrophysical neutrinos, cosmic rays, dark matter and heavy exotic particles, as well as the variation of their fluxes over our Galaxy's lifetime. In recent years the international MDvDM community has been successfully tackling the challenges associated with realizing the concept of mineral detectors, opening the pathway towards a fully fledged experimental program and potential future discoveries.

physics.ins-det↗
arXiv2026

Source--Grid Coupling in Power System Oscillations: Observational Equivalence, Feedback Detectability, and Modal Interaction

The conventional distinction between forced and natural oscillations treats the oscillation source as external to a fixed power-system boundary. This paper develops a coupling-aware dynamical interpretation by explicitly accounting for the directionality of source-grid coupling. It examines when prescribed forcing and a one-way autonomous source are observationally equivalent, and when grid measurements can distinguish the presence of grid-to-source feedback. Matched interface signals yield exact equivalence under the same deterministic grid initial-value problem and measurement map. Bidirectional feedback enables source-grid eigenvalue interaction in the augmented autonomous model, while a reduced two-mode model shows that the interaction depends jointly on loop coupling and complex modal separation. The resulting framework establishes source-grid coupling directionality as a feedback-based dynamical boundary and clarifies when feedback can be inferred from grid-side measurements and why weak feedback may remain unresolved. Simulation studies on a two-area system demonstrate feedback detection within a calibrated source family, while source-model mismatch can be confounded with feedback. A WECC 243-bus system study demonstrates feasibility on a large system and shows that feedback detection need not require exact candidate selection.

eess.SY↗
arXiv2026

LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction

Extracting informative representations from longitudinal data that can predict future outcomes remains a critical challenge in medicine. Medical datasets are inherently heterogeneous, consisting of a large number of variables collected from different sources, sampled with different temporal spacings, and representing different aspects of human health status. This requires identifying those variables with predictive value, processing longitudinal information, and integrating multiple variables for outcome prediction. Here, we propose a novel agent-based approach, LongAgent, that can autonomously search over combinations of variable sets, temporal windows and longitudinal aggregation functions, and identify candidates with promising predictive performance. LongAgent utilises a history memory of previous searches and numerical evidence to guide subsequent exploration. On synthetic data, LongAgent achieves a mean prediction RMSE of 1.7376 and improves over the strongest non-agent baseline by 0.0151 (95% CI: [0.0045,0.0260]; p=0.0273). On a real clinical dataset, it performs comparably to the best baseline.

cs.AI↗
arXiv2026

ConfRAG: Confidence-Guided Retrieval-Augmenting Generation

Can Large Language Models (LLMs) be trained to avoid hallucinating factual statements, and can Retrieval-Augmented Generation (RAG) be triggered only when necessary to reduce retrieval and computation costs? In this work, we address both challenges simultaneously. We introduce ConfQA, a fine-tuning strategy that reduces hallucination rates from 20-40% to below 5% across multiple factuality benchmarks. The approach is simple: when the model answers correctly, it is trained to output the answer; otherwise, it is trained to respond with "I am unsure". Two design choices make this training effective: (1) a dampening prompt ("answer only if you are confident") that explicitly discourages overconfident hallucinations, and (2) training data drawn from atomic factual statements (e.g., knowledge graph attribute values), which calibrates model confidence and yields robust generalization across domains and question types. Building on ConfQA, we propose ConfRAG, a triggering strategy that invokes RAG only when the model responses with unsure. This framework achieves accuracy above 95% in ideal case while reducing unnecessary external retrievals by over 30%.

cs.CL↗
arXiv2026

Interlayer Exciton Condensate Stiffness Is Non-Monotonic in Quantum Metric

Identifying the origin of the superfluid stiffness of electron-electron and electron-hole pair condensates is an important issue in flat-band physics. Here, we study the stiffness of bilayer exciton condensates using exact diagonalization and realistic Coulomb interactions across a wide variety of flat Chern band systems, including Landau levels, mixed Landau levels, and moiré bands. We find that stiffness is non-monotonic in the trace of the quantum metric and that it develops peaks when the band wavefunctions are engineered to be similar to those of Landau levels. The stiffness predicted by mean-field theory agrees quantitatively with exact diagonalization in these optimal cases, but systematically overestimates it otherwise. Flat bands with identical quantum geometry tensors can exhibit substantial differences in stiffness. The stiffness of condensates formed between moiré flat bands, which typically have strong variations in Berry curvature and quantum metric across their Brillouin zones, tends to be larger when the bands have non-zero Chern numbers and can be larger than that of Landau levels. Our results reveal a behavior that is richer than that suggested by simple geometric bounds and provide new guiding principles for the design of robust flat-band condensates with large superfluid stiffness.

cond-mat.mes-hall↗
arXiv2026

Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process

We propose LLM-PeerReview, an unsupervised LLM Ensemble method that selects the most ideal response from multiple LLM-generated candidates for each query, harnessing the collective wisdom of multiple models with diverse strengths. LLM-PeerReview is built on a novel, peer-review-inspired framework that offers a transparent and interpretable mechanism, while remaining fully unsupervised for flexible adaptability and generalization. Specifically, it operates in three stages: For scoring, we use the emerging LLM-as-a-Judge technique to evaluate each response by reusing multiple LLMs at hand; For reasoning, we can apply a straightforward averaging strategy or a principled graphical model-based truth inference algorithm to aggregate multiple scores to produce a final score for each response; Finally, the highest-scoring response is selected as the best ensemble output. LLM-PeerReview is conceptually simple and empirically powerful. Our results across four datasets show that the two variants of the proposed approach outperform the advanced model Smoothie-Global by 6.9% and 7.3% points, cross diverse task types including factual recall QA, math reasoning, and instruction following. Notably, we also establish a carefully curated benchmark suite for LLM Ensemble, integrating 12 methods across four classic datasets and three task families, all evaluated under a rigorous and consistent protocol. We hope this repository will help researchers reproduce the LLM Ensemble baselines.

cs.CL↗
arXiv2026

Cross-Relational Preference Learning for Better LLM Instruction Following

Large Language Models (LLMs) still exhibit limited capability in following complex instructions. While existing approaches often rely on preference learning to enhance this ability, they typically overlook the relationships between the permissible response spaces of different instructions, which restricts a model to align with subtle and diverse constraint variations. To address this, we propose Cross-Relational Preference Learning (CRPL), a novel framework for constructing preference data that explicitly models inter-instruction relationships through two key techniques: Cross-Relationship Perturbation and Cross-Region Pair Sampling. This enables the generation of more diverse preference data that captures a wide spectrum of constraint variations. Additionally, we introduce an atomic constraint-based verification mechanism to rigorously assess response satisfaction, ensuring high-quality preference pair construction. Extensive experiments across multiple preference learning methods (e.g., DPO, KTO), LLM backbones and four instruction-following benchmarks demonstrate that our approach achieves substantial improvements over prior baselines and exhibits strong generalization.

cs.AI↗
arXiv2026

Why Three Phases? A Historical and Engineering Reassessment of Phase Order in AC Power Transmission

Three-phase alternating current is so deeply embedded in modern electric-power infrastructure that its phase order is often treated as self-evident. Historically, however, 1-phase and true 2-phase systems were commercially important, while commercial 6-phase transmission was later demonstrated. This paper reassesses why 3 phases became the dominant architecture for bulk AC transmission. A general balanced \(m\)-phase formulation is used to show that constant aggregate instantaneous power is not unique to 3 phases: an ideal balanced 2-phase system also cancels the double-frequency power term and can generate a constant-magnitude rotating field. Consequently, the historical displacement of 2 phases cannot be explained by power smoothness alone. The comparison is instead organized around conductor architecture, insulation stress, machine and transformer utilization, conversion requirements, right-of-way utilization, and technological path dependence. A historical, commercial 6-phase demonstration showed that high-phase-order transmission was technically feasible and could improve corridor utilization. The paper then asks: if PE conversion and protection make phase count less costly, could \(m>3\) offer intrinsic advantages? Six such advantages are identified: modular decomposition into interleaved 3-phase groups, redundant control degrees of freedom, structured modal/fault analysis, increased corridor power density under field constraints, potentially higher natural loading and loadability, and enhanced harmonic/field cancellation. A companion derivation shows that phase count alone does not intrinsically reduce \(I^2R\) loss at fixed total conductor material and phase voltage. As a conditional conclusion, 3 phases are an unusually favorable historical optimum for electromechanical grids, but not a mathematically universal optimum for a future converter-dominated grid.

eess.SY↗
arXiv2026

SCRIBES: Web-Scale Script-Based Semi-Structured Data Extraction with Reinforcement Learning

Semi-structured content in HTML tables, lists, and infoboxes accounts for a substantial share of factual data on the web, yet the formatting complicates usage, and reliably extracting structured information from them remains challenging. Existing methods either lack generalization or are resource-intensive due to per-page LLM inference. In this paper, we introduce SCRIBES (SCRIpt-Based Semi-Structured Content Extraction at Web-Scale), a novel reinforcement learning framework that leverages layout similarity across webpages within the same site as a reward signal. Instead of processing each page individually, SCRIBES generates reusable extraction scripts that can be applied to groups of structurally similar webpages. Our approach further improves by iteratively training on synthetic annotations from in-the-wild CommonCrawl data. Experiments show that our approach outperforms strong baselines by over 13% in script quality and boosts downstream question answering accuracy by more than 4% for GPT-4o, enabling scalable and resource-efficient web information extraction.

cs.CL↗
arXiv2026

PBD-AG: Persistent Baseline-Delta Active Graphs with Uncertainty-Aware Inspection for Long-Horizon Service Robots

Long-horizon service robots require persistent world models that can be built autonomously in unseen environments and revised as task-relevant objects change. Existing methods rely on online mapping, which accumulates localization and observation errors, static scene representations that cannot capture persistent object changes, or holistic vision-language predictions that lack verifiable 3D geometric evidence. We present PBD-AG, a persistent baseline-delta active graph framework that decouples robot-verified stable fixtures from revisable dynamic object events. Under our framework, the robot autonomously bootstraps the structural baseline from onboard exploration and inspects discovered fixtures to ground hierarchical object beliefs. PBD-AG maintains reliability-weighted object states over geometry, semantics, identity, existence, and support relations, utilizing a geometric visibility gate to mitigate false deletions under occlusion. Inspection viewpoints are selected by a graph-conditioned policy that balances target coverage, travel cost, collision risk, and redundant observation. Simulation experiments in multiple environments and under controlled dynamic evaluation show higher aggregate coarse-fixture F1 than capability-matched controls, as well as stronger identity continuity and event recall. A qualitative physical-robot demonstration further illustrates integration with onboard sensing, providing a traceable world model for long-horizon robotic perception. The project page of PBD-AG is available at https://shuobao214.github.io/PBD-AG/

cs.RO↗
arXiv2026

Tunable Multiband Geometry and Fractional Phases in Higher Vortexable Systems

Higher vortexability is often viewed as a route to topological flat bands with higher-Landau-level-like quantum geometry. Here we emphasize a complementary perspective: it provides a tunable multiband structure in which wave function geometry can be varied continuously while the band dispersion, degeneracy, and topology remain fixed. We perform systematic exact-diagonalization studies of many-body phases in fractionally filled higher vortexable moiré systems, retaining the full flat-band Hilbert space rather than projecting onto a single band. The multiband treatment reveals a cascade of Abelian and non-Abelian phases at zero magnetic field, including integer and fractional exciton insulators, Abelian fractional Chern insulators, Moore-Read and Read-Rezayi states. At fixed filling, different phases are connected through transitions or crossovers driven solely by changes in wave function geometry, highlighting quantum geometry itself as a direct tuning parameter between competing topological states. At fillings associated with Moore-Read and Read-Rezayi states, our calculations show that interband mixing shifts the optimal quantum geometry regime without suppressing non-Abelian topological order under screened Coulomb interaction. Our results establish higher vortexable moiré bands as a tunable platform for exploring geometry-driven multiband topological phases at zero magnetic field.

cond-mat.str-el↗
arXiv2026

An LMM for Precisely Grounding Elements in Documents

Visual grounding in documents is a crucial ability for Large Multimodal Models (LMMs) in areas such as document understanding, deep research and document error detection. However, existing approaches exhibit poor grounding precision in text-rich document images, often failing to accurately locate the critical document elements needed for reliable reasoning. To address this gap, we introduce PreciseDoc, an LMM specifically designed for precise element grounding and can be further optimized for Document VQA tasks. Specifically, to enhance the basic localization capability, we construct challenging training data by two pipelines capable of mass-producing high-quality documents with paired metadata of fine-grained coordinates, including synthetic hand-filled documents with camera effects. The model develops more real-world functions beyond straightforward localization of single text, such as locating personal information from CVs. Furthermore, we introduce a training paradigm for visual grounded reasoning where the grounding and reasoning are supervised jointly with reinforcement learning to improve the contribution of the grounded evidence. A comprehensive evaluation on various benchmarks demonstrates the advantage of the proposed data and methods in document spatial grounding and document understanding.

cs.CV↗
arXiv2026

Nonlocal Manipulation of Backflow with Quantum Correlations

Quantum correlations are central resources for quantum information processing, yet their ability to manipulate dynamical transmission processes remains largely unexplored. Here, we investigate this ability through backflow, a uniquely interference phenomenon in which local probability flow propagates opposite to the momentum direction. We report the first nonlocal manipulation of backflow in double-slit interference using polarization-path-entangled photons. By performing local measurements on one photon, we remotely engineer the relative amplitude and phase of the two paths associated with its partner, manipulating the emergence, spatial distribution, and propagation dynamics of backflow without directly accessing the interfering system. Combining weak measurements to extract the transverse momentum and reconstruct Bohmian trajectories, we provide a direct visualization of the manipulation process with single-pixel spatial resolution. Furthermore, using Werner states with tunable correlation strengths, we reveal a distance-dependent resource requirement for nonlocal backflow manipulation: the minimum correlation strength required to induce backflow increases with propagation distance, progressing from entanglement to EPR-steering and ultimately Bell nonlocality. Our results show quantum correlations as operational resources for manipulating transmission dynamics and open new avenues for non-contact manipulation of fragile or inaccessible systems.

quant-ph↗
arXiv2026

CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection

Multimodal Large Language Models (MLLMs) excel in diverse vision tasks, but full-parameter retraining is computationally expensive as real-world knowledge evolves. Existing continual learning methods often suffer from semantic entanglement in parameter spaces across tasks, impeding the continuous deployment of models. This challenge is especially pronounced in Anomaly Detection (AD), which exhibits triple heterogeneity across modalities, domains, and defect scale variability, significantly complicating multi-task knowledge transfer. In this paper, we propose CL-Anomaly, a parameter-efficient fine-tuning framework based on an isolation-sharing collaboration to enable continual learning for anomaly detection with MLLMs. We introduce the task-private expert PrivLoRA, which physically isolates task-specific subspaces in the parameter space to prevent semantic entanglement of anomaly knowledge in diverse scenarios. The Layer-Adaptive Shared Experts maintain cross-task representations within a unified feature space, enabling knowledge sharing between previous and new tasks. Furthermore, we propose a Layer-Adaptive Knowledge Transfer strategy that automatically selects and dynamically updates the layer-wise key shared experts of each task via a momentum-based mechanism, promoting effective knowledge transfer across related anomaly detection tasks. Extensive experiments across three continual learning scenarios for anomaly detection, including class-incremental, cross-domain, and cross-modal, demonstrate that CL-Anomaly outperforms state-of-the-art methods. Code is available at https://github.com/WenDongyp/CL-Anomaly.

cs.CV↗
arXiv2026

Hi-DREAM: Brain-Inspired Hierarchical Diffusion for fMRI-to-Image Reconstruction via ROI Encoder and VisuAl Mapping

Reconstructing natural images from fMRI requires bridging neural activity with both the structural and semantic representations used by modern generative models. Existing diffusion-based decoders often condition on a single global fMRI embedding, which limits their ability to exploit the hierarchical organization of the visual cortex and makes the contribution of different visual areas difficult to inspect. We propose Hi-DREAM, a brain-inspired hierarchical diffusion framework that structures fMRI conditioning according to early, middle, and late visual Regions of Interest (ROI) streams. A ROI adapter converts these streams into a multi-scale cortical pyramid, and a lightweight ROI-conditioned ControlNet injects the resulting anatomy-aware priors into matched U-Net depths during denoising. Experiments on the Natural Scenes Dataset (NSD) show that Hi-DREAM achieves state-of-the-art high-level semantic reconstruction while retaining strong low-level structure. Further ablation and attribution analyses show that the proposed hierarchy-aware conditioning is effective, and that different ROI streams provide complementary, inspectable contributions to reconstruction.

cs.CV↗
arXiv2026

Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks

Spiking neural networks (SNNs), which are brain-inspired and spike-driven, achieve high energy efficiency. However, a performance gap between SNNs and artificial neural networks (ANNs) still remains. Knowledge distillation (KD) is commonly adopted to improve SNN performance, but existing methods typically enforce uniform alignment across all timesteps, either from a teacher network or through inter-temporal self-distillation, implicitly assuming that per-timestep predictions should be treated equally. In practice, SNN predictions vary and evolve over time, and intermediate timesteps need not all be individually correct even when the final aggregated output is correct. Under such conditions, effective distillation should not force every timestep toward the same supervision target, but instead provide corrective guidance to erroneous timesteps while preserving useful temporal dynamics. To address this issue, we propose Selective Alignment Knowledge Distillation (SeAl-KD), which selectively aligns class-level and temporal knowledge by equalizing competing logits at erroneous timesteps and reweighting temporal alignment based on confidence and inter-timestep similarity. Extensive experiments on static image and neuromorphic event-based datasets demonstrate consistent improvements over existing distillation methods. The code is available at https://github.com/KaiSUN1/SeAl

cs.LG↗
arXiv2026

Multipartite quantum resource distillation through local measurement programs

Distributed quantum resources in practical multi-user quantum networks are inevitably degraded by environmental noise, channel loss, and device-induced imperfections. To address these issues, quantum resource distillation offers a fundamental approach to recovering stronger resources from imperfect states. However, conventional implementations often require additional copies, dedicated physical filtering elements, or restrict to bipartite systems, posing challenges for scalable multipartite networks. Here, we introduce the method of quantum resource distillation based on the local measurement program (LMP), which transfers completely positive maps into programmable measurement processes. We experimentally demonstrate the performance of resource distillation through LMP in both bipartite and tripartite photonic systems, including the activation and enhancement of multipartite steering configurations. To demonstrate the flexibility and extensibility of the LMP framework, we also show that virtual resource distillation can be naturally reformulated within it. Our results establish a programmable and experimentally economical approach for distilling quantum resources in multipartite and higher-dimensional systems, thereby providing a practical route toward scalable quantum networks.

quant-ph↗