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Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 55 records · Page 3Linked to original sources
arXiv2026

Evidence of self-organized criticality in the prompt emission of a bright gamma-ray burst

Gamma-ray bursts (GRBs) are the most energetic explosive events in the Universe, yet the physical mechanism of their prompt emission remains a mystery. Especially, it is unclear whether the energy dissipation mechanism in the GRB jet is dominated by kinetic energy or magnetic energy. Here, we studied the pulses in the prompt emission of the second brightest GRB to date, GRB 230307A, which was accurately measured by the Gravitational wave high-energy electromagnetic counterpart all-sky monitor (GECAM), with focus on the cumulative distributions of peak counts and duration of pulses as well as the waiting time between pulses. We find that these cumulative distributions show scale-invariant behavior, well consistent with the prediction of the self-organized criticality (SOC) theory. This is the first robust evidence of an SOC feature in the prompt emission of a single GRB. Moreover, the statistical properties of pulses in the prompt emission of GRB 230307A are very similar to those of solar flares. Our findings suggest that the prompt emission of GRB is powered by the dissipation of magnetic energy in the ultra-relativistic jet, supporting the Poynting-flux-dominated prompt models.

astro-ph.HE↗
arXiv2026

FeatureHospital: A Skill-Driven Multi-Agent Framework for Automated Algorithm Customization in Multi-View Multi-Label Feature Selection

Multi-view multi-label feature selection aims to identify a compact and informative feature subset from heterogeneous views while preserving discriminative information for multiple labels. Existing methods are generally developed from specific modeling perspectives and incorporate mechanisms tailored to particular data characteristics. Designing suitable feature selection algorithms across datasets with diverse and heterogeneous characteristics still relies heavily on expert knowledge and substantial manual effort, imposing considerable time and labor costs that severely hinder the practical adoption of feature selection. To address this problem, we propose FeatureHospital, a Skill-driven multi-agent framework for automated multi-view multi-label feature selection algorithm design. FeatureHospital first diagnoses the target dataset to identify its feature selection issues. Based on the diagnosis, specialist agents equipped with domain Skills then prescribe corresponding optimization strategies and Loss terms for different issues. After that, the resulting prescriptions are reconciled to remove overlaps and resolve conflicts before being integrated into a compact dataset-specific objective. Finally, the constructed objective is optimized to select the final feature subset. Experimental results demonstrate that FeatureHospital can construct effective feature selection algorithms for different datasets based on their individual characteristics.

cs.AI↗
arXiv2026

Stimulated Oscillations in Renewable Energy Integrated Power Systems - Part I : Mechanism and Analysis Methods

Oscillation is a critical issue that power systems have long faced. Especially over the past two decades, with the large-scale inte-gration of renewable energy into the grid, oscillation problems have posed a serious threat to the secure operation of power systems. However, the current literature has not fully explained the oscillation mechanism of renewable energy integrated power systems (REIPSs). In this paper, the underlying mechanism of stimulated oscillations is explored, with novel analytical methods proposed. Firstly, it is explained from both mathematical formu-las and physical interpretations that for an oscillation mode characterized by a pair of complex conjugate poles, the oscilla-tion risk under disturbance depends on the relative positional relationship between the corresponding poles and all other poles and zeros on the complex plane, rather than their standalone locations, i.e., the stability perceived by classical theory. Then the underlying mechanism of high amplitude oscillations induced by closely-located poles under even slight disturbance is clarified. On this basis, a theoretical framework for stimulated oscilla-tions applicable to REIPSs, covering its definition, mechanism, and methods, is proposed. Finally, this paper discusses the rela-tionship between the stimulated oscillation theory proposed herein and the classical stability-based theory, revealing that the research findings surpass rather than negate the classical theo-ries.

eess.SY↗
arXiv2026

Inferring 1-Minimal Trigger Configurations for Assessing Linux Kernel CVE Triggerability

Vendors assessing Linux kernel CVEs need to know whether a bug is triggerable under production-tailored configurations, not merely whether a version is affected, yet upstream reproducers and vulnerability databases rarely provide configuration-level context. We study minimal trigger-configuration inference: given a CVE entry and a target kernel version (optionally a baseline .config), we synthesize a Kconfig-satisfiable option set that remains effective after make olddefconfig and, when a reproducer is available, still triggers under a specified evaluation protocol; we then prune it to a 1-minimal (subset-minimal) boundary for evaluation. Our framework FCC links vulnerability cues to build-system symbols, completes implicit prerequisites under olddefconfig feedback to avoid silent rollback, and performs runtime-validated minimization guided by dependency topology. We evaluate on KernJC and KernelCTF, totaling 88 CVEs across multiple kernel versions. On the 88-CVE set, FCC improves the post-make olddefconfig configuration success rate from 62.5% (55/88) to 96.6% (85/88) over an olddef-only injection baseline; on the KernJC set, FCC reduces the average candidate set size by 78.7% compared to KernJC (Avg. 14.72 vs. 69.00 options per CVE). A stage-wise analysis of time and token costs shows that Stage I dominates overhead, while CVE-focused evidence selection substantially reduces this cost. By returning an effective and auditable 1-minimal configuration boundary, FCC helps vendors scope triggerability against their deployment configurations with a clear, tool-supported decision line.

cs.CR↗
arXiv2026

Visualizing High-Dimensional Graph Embeddings via Informed Multi-View Projections

Graphs are commonly visualized in 2D, where humans readily interpret spatial relationships, yet such layouts often distort higher-dimensional structure. We propose to embed graphs in high-dimensional space and search for informative 2D viewpoints that optimize aesthetic and readability metrics (e.g., edge crossings and angular resolution), enabled by a novel differentiable surrogate for edge crossings. Numerical experiments show that these viewpoints consistently outperform standard 2D layouts, and can even surpass methods explicitly designed to optimize these metrics. We further introduce DataFly, an interactive system for exploring multiple candidate viewpoints through seamless navigation. A usability study demonstrates that our approach reveals structural patterns that remain hidden in conventional 2D visualizations.

cs.LG↗
arXiv2026

Wan-Animate-2: Pushing the Application Boundaries of Character Animation

Character image animation remains a foundational yet challenging task in computer vision. Existing approaches can be broadly categorized into three paradigms: methods based on explicit motion representations suffer from extraction errors and identity drift; methods based on implicit motion features lose fine-grained dynamics through compression; and in-context learning approaches avoid intermediate representations but incur prohibitive computational costs. Furthermore, all current systems are designed for offline synthesis, unable to meet the real-time requirements of interactive applications such as digital avatars and live-streaming hosts. To address these limitations, we present Wan-Animate-2, an end-to-end character animation framework that directly consumes the driving video within a redesigned Diffusion Transformer. Our architecture achieves superior motion fidelity and identity preservation by eliminating intermediate motion extractors entirely. We further introduce text driven viewpoint control that decouples the output camera perspective from the driving video--a capability rarely supported by prior character animation methods that rely on explicit motion representations. Beyond generation quality, we present Wan-Animate-2-Lite, an efficient variant that reduces inference latency to real-time thresholds through a three-stage training paradigm: teacher forcing pretraining with error buffer mechanism, and Self-Forcing distillation with chunk-wise backpropagation. This enables streaming character animation for interactive applications, opening new deployment scenarios that were previously infeasible. Qualitative evaluations and user studies demonstrate that Wan-Animate-2 achieves high-fidelity animation results across diverse characters and motion patterns. To foster further research and community development, we will release the Wan-Animate-2-Base model weights to the public.

cs.CV↗
arXiv2026

SCTD 3.0: Sonar Common Target Detection in the Wild - A Large-Scale, Multi-Scene Dataset from Real Marine Surveys

Synthetic Aperture Sonar (SAS) is core for wide-area detection of small underwater targets. However, large-scale, high-quality SAS datasets are scarce, hindering data-driven recognition. Existing benchmarks are small and limited to single scenarios, failing to reproduce complex acoustic scattering, diverse seabeds, and multi-pose imaging in real detection. To fill this gap, we introduce SCTD 3.0 - a large-scale real-measured dataset for Sonar Common Target Detection in the Wild in natural waters. It contains over 10,000 high-quality real SAS image snippets from multi-frequency systems (240 kHz, 450 kHz, and others), covering ten typical target categories across varied seabed geomorphologies, with multiple observation angles, detection ranges, and frequency bands. We establish a rigorous hierarchical annotation protocol that decouples labeling of intrinsic physical properties, deployment characteristics, and scattering phenomena - covering material, geometry, internal structure, burial state, shadow integrity, specular highlights, edge diffraction, and resonance effects. This enables fine-grained target characterization. We also construct a multi-task benchmark for object detection, fine-grained classification, and attribute prediction, evaluating mainstream deep learning models under cross-domain, cross-scene, cross-frequency, and cross-view generalization. SCTD 3.0 is expected to provide a critical data cornerstone for robust underwater target perception in open-water environments. SCTD 3.0 is available at https://github.com/automlresearch/SCTD-3.0.

cs.CV↗
arXiv2026

Stability of MIMO PID With Backward Differences Under Fast Sampling: An Exact Spectral Criterion

Backward differences are a standard digital realization of derivative action in proportional-integral-derivative control. This note proves that, for multivariable state-space plants, this implementation can be unstable no matter how small the sampling period is. The exact lifted model reveals fast eigenvalues created by the stored previous output sample. Away from boundary spectra, stability is equivalent to ideal loop stability plus Schur stability of the product of the output, input, and derivative gain matrices.

math.OC↗
arXiv2026

PASCHEN-1D: A one-dimensional fluid plasma solver with multi-mechanism surface emission and flexible external circuit coupling

We present PASCHEN-1D (Plasma Advanced Solver with Coupled High-fidelity Emission and external Network), a one-dimensional time-dependent fluid plasma solver developed for self-consistent simulation of gas discharges and plasma breakdown with coupled electrode surface emission and flexible external circuit networks. The code solves drift-diffusion continuity equations for electrons and ions together with Poisson's equation. It is dynamically coupled to lumped RLC circuits, which self-consistently treat plasma transport, plasma-surface interaction, dielectric effects, and circuit response within a single framework. The electrode emission module includes ion-induced secondary electron emission, Fowler-Nordheim and Murphy-Good field emission, Richardson-Dushman thermionic emission, and photoemission based on a general, exact quantum mechanical emission theory. A finite-volume formulation with Kurganov-Tadmor fluxes, explicit diffusion, and fourth-order Runge-Kutta time integration is employed to ensure stable transient (sometimes ultrafast) evolution across breakdown and glow regimes. The solver is validated against multiple benchmark cases, including nanosecond pulsed dielectric-barrier discharges, DC breakdown and glow transitions, and Paschen curve construction for argon and nitrogen, with results consistent with published studies. With high-fidelity emission physics and a flexible circuit-coupling framework, PASCHEN-1D provides a versatile and efficient tool for modeling breakdown and transient discharge phenomena.

physics.plasm-ph↗
arXiv2026

From Trajectories to Evidence: Auditable Experimental Records for Industrial Research Agents

Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajectories to guide later decisions. Yet a completed trajectory is not automatically evidence: generated artifacts may be unsupported or incomplete, executed rounds may be invalid or confounded, and later modifications may obscure earlier findings. We study \textbf{trajectory-to-evidence conversion}, asking what a completed research process has actually established. We introduce an evidence-grounded framework that couples bounded verification of consequential artifacts with post-execution claim qualification. A context-isolated generate--verify--repair process checks artifacts for evidence violations and missing downstream requirements before release. After execution, validity and attribution checks consolidate evidence across rounds, qualify intervention-level claims as actionable repairs, diagnostic guards, or withheld findings, and preserve admitted claims as auditable records with explicit provenance and applicability boundaries. A hybrid LLM-assisted controller subsequently applies, defers, or rejects records based on available target evidence. Record audits characterize which claims survive qualification, while downstream diagnostics identify affirmative applicability judgment as a bottleneck for the tested controller. Across paper-to-target adaptations, later rounds often improve on the first, while final rounds frequently underperform an earlier best, exposing non-monotonic trajectory evolution. Candidates produced through the complete workflow also yielded positive online lifts relative to deployed baselines.

cs.IR↗
arXiv2026

High-efficiency loading of 2,400 Ytterbium atoms in optical tweezer arrays

Neutral atom arrays have emerged as a powerful platform for quantum computation, simulation, and metrology.Among them, alkaline-earth-like atoms exhibit distinct advantages, including long coherence time, high-fidelity Rydberg gates, and erasure correction for efficient quantum error correction. However, their scalability has lagged behind that of the alkali atoms. Here, we report 2400 ytterbium-174 atoms trapped in an optical tweezer array with enhanced loading efficiency of 83.5(1)\% via blue-detuned light-assisted collisions. We develop a quantitative model of the collision dynamics and find good agreement between the calculated inelastic collision rates and the experimentally measured loading efficiencies.Notably, the loading efficiency is largely maintained for array sizes ranging from dozens to thousands, exhibiting excellent scalability. We further demonstrate that the enhancement exists robustly across a range of interatomic potentials, suggesting its utility for other atomic species. To establish the capability of the $^{174}$Yb arrays toward universal quantum computation, we propose to encode the qubit in the ground-clock state manifold and estimate a 99.9\% two-qubit gate fidelity with experimentally feasible parameters. Our work advances the prospects for realizing large-scale quantum computers using alkaline-earth-like atoms.

quant-ph↗
arXiv2026

LATTICE: Constraint-Directed Scheduling, Memory Planning, and Pipeline Refinement for NPUs

General-purpose NPUs execute fine-grained command DAGs across heterogeneous compute and memory-transfer engines backed by finite, explicitly managed on-chip memories. This execution model creates a directed dependency between scheduling and memory planning: different legal topological orders induce different lifetime overlap, placement opportunities, and spill behavior, while a materialized layout introduces physical-address reuse constraints absent from the input precedence DAG. Command order therefore shapes the feasible memory plan, and the realized plan in turn defines the legal space for subsequent timing refinement. We present LATTICE, a deterministic constraint-directed compiler pipeline. Memory-Pressure-Aware Topological Scheduling reshapes lifetime geometry before address binding; Deterministic Linear Repackaging materializes tiered placement, spill/reload events, and plan-induced reuse constraints; and Critical Path Enhancement recovers pipeline parallelism while preserving the selected memory plan. Every accepted schedule passes independent memory and timing verification. Across six artifact-provided command traces labeled as derived from a Da Vinci NPU flow, LATTICE achieves the best or tied-best result in all 24 evaluated workload-metric comparisons. Relative to the best evaluated baseline for each workload and metric, it reduces peak memory, extra DDR traffic, spill count, and modeled makespan by 18.3% lower, 20.4% lower, 14.1% lower, and 16.3% lower, respectively. Plan-preserving CPE further reduces makespan by 12.1% over Freeze while leaving placement and memory traffic unchanged, establishing the static memory plan as a verifiable scheduling contract between memory planning and pipeline optimization.

cs.NI↗
arXiv2026

Latency-Optimal Adaptive Split Inference for Privacy-Preserving Cloud-Edge-End Collaboration

Internet of Things (IoT) end devices are increasingly expected to support privacy-sensitive batch inference, yet their limited computational resources often make full local execution of convolutional neural networks impractical. This paper presents a latency-optimal adaptive split inference framework for privacy-preserving cloud-edge-end collaboration. The end device acts as the trust anchor, executes the plaintext model prefix, encrypts the split activation using fully homomorphic encryption (FHE), and keeps the secret key locally, while the edge and cloud execute assigned model segments only on FHE ciphertexts. We formulate collaborative encrypted inference as a split-pair selection problem over an end-side split point and an edge-side termination point. The proposed planner jointly models plaintext prefix execution, encryption, communication, edge-side FHE execution, and cloud-side FHE completion, and supports both convolution-level and block-level split granularities. Experiments on CIFAR-10 and PathMNIST show that the proposed convolution-level collaborative scheme achieves amortized end-to-end speedups of approximately 12.9 times over full-cloud FHE and 3.9 times over the block-level alternative, while preserving the corresponding plaintext-model accuracy. Including modeled communication, the amortized latencies are 1033.279 s/sample on CIFAR-10 and 1023.429 s/sample on PathMNIST.

cs.CR↗
arXiv2026

Dense Language Generation Made Simple: Deterministic, Randomized, and Multi-Order Algorithms

Language generation in the limit is a theoretical framework for studying how a generator can learn to produce new valid strings from a stream of positive examples. In this model, an adversary chooses an unknown language from a countable family and enumerates its elements in an arbitrary order, while the generator must eventually output only elements of the language that have not yet appeared in the enumeration. Reliable generation is thus formalized through two eventual guarantees: validity and novelty relative to the observed data. To further quantify the breadth of the generator's outputs, Kleinberg and Wei (FOCS 2025, STOC 2026) introduced lower density as a measure of output coverage. Given an order representing the importance or relevance of possible outputs, lower density is the asymptotic lower bound, as $n$ grows, on the fraction of the first $n$ elements of the target language that the generator outputs before they appear in the data. Kleinberg and Wei showed that $1/2$ is the optimal lower-density guarantee for deterministic algorithms. We develop a simple and unified framework for obtaining optimal lower-density guarantees. We first give a deterministic algorithm that recovers the optimal guarantee of $1/2$ with a significantly simpler analysis than prior work. We then demonstrate the flexibility of our framework through two extensions. First, against an oblivious adversary, randomization raises the optimal guarantee to $1-1/e$. Second, for any finite collection of orders, the optimal deterministic and randomized guarantees can be achieved simultaneously with respect to every order, so accommodating multiple notions of importance or relevance entails no loss in the optimal guarantee.

cs.DS↗
arXiv2026

Large Emotional World Model

The world is governed by both physical laws and affective dynamics. Physical laws govern state transitions, while affective dynamics shape human actions, decisions, and interactions. A world model that learns only physical laws can approximate the physical world, but not the human world. In this paper, we introduce human emotion as a key state variable in world models, enabling them to capture both future state transitions and their emotional causes. We first construct Emotion-Why-How (EWH), the first world model dataset centered on emotional state transitions, containing 10,850 emotion-aware transition tuples. Each tuple encodes the pre-state, pre-emotion, action, post-emotion, and post-state, supporting reasoning about why actions occur and how emotions reshape future states. Based on EWH, we propose the Large Emotional World Model (LEWM), which factorizes future prediction into two coupled steps: first predicting the future emotional state from the current context, and then conditioning future world-state prediction on the predicted emotion. Experiments show that LEWM brings consistent gains across world-state prediction, emotion understanding, and general reasoning tasks. It achieves up to 45.72% accuracy improvement on EWH, 3.94% on WorldNet, 17.47% F1 improvement on MELD, and a 6.10% gain on specific MMLU categories. These results demonstrate that incorporating emotion into world models enables more realistic simulation of human-centered environments and expands the predictive understanding of intelligent agents.

cs.CL↗
arXiv2026

Sharp small-deviation inequalities for sums of independent nonnegative random variables

Let $(X_1,\ldots,X_n)$ be independent nonnegative random variables with $\mathbb{E} X_i\le1$, and write $S=\sum_iX_i$. For $δ>0$, we prove that \[ \mathbb{P}\left(S<\mathbb{E} S+δ\right)\ge b_{n,δ}, \] where $b_{n,δ}=δ(n/(n+δ))^n$ for $0<δ<1$ and $b_{n,δ}=(1-1/(n+δ))^n$ for $δ\ge1$. The bound is sharp for every $n$ and $δ\ge 1$. In particular, since $b_{n,δ} \ge e^{-1}$ for $δ\ge 1$, our result proves Feige's conjecture [Feige, 2004] in the affirmative for $δ\ge 1$. The proof is found by ChatGPT 5.6 Pro. It combines the exact Dirichlet calibration theorem of Vlassis and Thomas [Vlassis and Thomas, 2026], which resolves Gaffke's conjecture in statistics, with results in convex geometry including Grünbaum's centroid theorem [Grünbaum, 1960] and its generalization by Letwin and Yaskin [Letwin and Yaskin, 2024].

math.PR↗
arXiv2026

Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model

Standard vision-language models (VLMs) suffer from Moravec's paradox: they excel at complex offline visual reasoning but struggle with simple streaming perception tasks and process them inefficiently. We present Mage-VL, an efficient codec-native streaming foundation model for real-time multimodal understanding and interaction. At its core, our custom tokenizer, Mage-ViT, replaces uniform frame sampling by selectively encoding dynamic, entropy-rich regions using motion vectors and residual energy across sparse anchor (I) and predicted (P) frames. Operating at a 16 x 16 patch level, this reduces visual token consumption by over 75% while preserving spatiotemporal context. Trained from scratch on approximately 560M unlabeled images and 100M unlabeled video frames, Mage-ViT matches or outperforms flagship encoders trained on billions of image-text pairs. We establish AI4AI data pipelines encompassing prompt-code joint optimization for multimodal captioning and AI-driven performance diagnosis to guide training recipes. Furthermore, through a bio-inspired dual-system architecture - a lightweight System 1 event gate and a causal System 2 decoder - Mage-VL enables proactive streaming perception. Extensive evaluations show that Mage-VL-4B matches Qwen3-VL-4B on static tasks while achieving strong gains in video understanding and 2D/3D spatial reasoning, with up to a 3.5x wall-clock inference speedup, and comprehensively surpasses the 15B Phi-4-reasoning-vision baseline. Beyond model artifacts, we deliver seven key empirical findings covering pre-training data efficiency, variable-resolution scaling, codec system acceleration, VideoQA SFT redundancy, motion-spatial synergy, AI4AI data pipelines, and Zero-Vision SFT for multimodal RL.

cs.CV↗
arXiv2026

Real-Time Human-Centric World Modeling for Upper-Body Human-Object Interaction

We present a real-time human-centric world model for upper-body interactive generation, aiming to synthesize coherent local world dynamics centered on a person, where coordinated body, hand, and facial motions evolve jointly with controllable human-object discrete interaction. To this end, we adopt a continuous-discrete joint control scheme with two complementary components: a continuous human state and a discrete interaction state. For continuous human-state control, we introduce a unified implicit representation based on multi-scale motion encoding, in which motion latents from the upper body, hands, and face are fused into a shared latent space. This multi-scale design improves expressiveness across different spatial scales, captures fine-grained human dynamics more effectively, and enables direct control without explicit retargeting. For discrete object interaction-state control, we represent object contact using a small set of language-encoded discrete interaction states, where text serves as an explicit interaction-state command, such as \emph{no contact} or \emph{grasp}, rather than an open-ended generation prompt, and we further construct a dedicated rendering pipeline for human-object interaction data to supervise such discrete interaction states. By combining continuous implicit human-state control with discrete interaction-state control, our model enables precise modeling of how a person moves and interacts with the local environment, including controllable changes to nearby scene states. Finally, we distill the model for efficient streaming real-time inference, achieving 25 FPS on two H100 GPUs. Experiments demonstrate improved fine-grained motion fidelity, more realistic hand-object coordination, and effective real-time interaction, establishing a practical step beyond motion reproduction toward real-time human-centric world modeling.

cs.CV↗