arXiv subjects

Ping Zhang

Publications and source records attributed to Ping Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Evolving Intelligent Complex Systems via Intellicise Networks: Architecture, Technologies, and Pathways

Future engineering infrastructures are evolving into large-scale, open, heterogeneous, and wirelessly interconnected complex systems. These systems present significant challenges in optimizing network resource utilization, managing high-dimensional information spaces, and accommodating diverse business requirements. Intellicise networks, characterized by Intent-driven operation, semantic-native capability, and distributed intelligence, offer a promising paradigm for enabling such intelligent complex systems. We provide a systematic exploration of future intelligent complex systems from the perspective of intellicise networks. Specifically, we propose a cross-domain intelligent communication network architecture based on intellicise networks, grounded in information theory, systems theory, game theory, and cybernetics. The architecture comprises a cross-layer organizational framework, multi-functional planes, and novel information flows. The cross-layer framework defines the vertical evolution from perception and cognition to decision, while the control, user, data, computation, intelligence, and security planes deliver horizontal intellicise capabilities. Moreover, data, knowledge, model, and task flows interconnect the various layers and planes, forming a closed-loop process that derives simplicity from high-level intelligene while concurrently pursuing enhanced. Building on this architecture, we review key enabling technologies, tracing their evolution from semantic extraction to intent understanding, from heterogeneous resource integration to self-configuration and self-optimization, from generative artificial intelligence (AI) to agentic AI, and from embodied AI to symbodied AI. Additionally, we present a case study on intellicise networks for embodied agent communications and discuss representative applications and services for intelligent complex systems.

eess.SP↗

arXiv2026

Ontic state recovery through prediction

We encounter a system through its responses, yet its state concerns how the system is. Predicting further responses promises knowledge beyond what is immediately observed. What that knowledge tells us about the system's state depends on what we mean by a state in the first place. We give this notion a model-relative meaning: a state is the complete response profile over a specified query universe, and configurations agreeing on every query are regarded as the same state. From this perspective, a learned state is understood through the distinctions between these profiles that it retains. Prediction concerns selected observations; recovery concerns the profile as a whole. We examine their relationship through query ambiguity, state-space geometry, and the choice of prediction targets. Synthetic systems with known response laws and neural recordings provide complementary settings for this investigation. The aim is to clarify how prediction can become a way of knowing a system, and what it would mean for a learned state to preserve the state being studied.

cs.LG↗

arXiv2026

Representing short-term movement to reveal animal identity and learning progress

How learning progresses over time and varies across individuals is a longstanding question in cognitive and behavioral science. However, a major challenge is identifying observable behavioral measures that faithfully capture learning progression. Existing approaches commonly rely on predefined behavioral features or task-level performance, while fine-scale posture and movement in freely behaving animals remain less explored. In this paper, we ask: can short-term movement trajectories reveal learning progress and individual identity? We introduce a self-supervised representation learning framework built on a simple hypothesis: behavioral characteristics associated with identity and learning remain relatively stable over nearby time intervals. Using temporal proximity as supervision, our framework learns compact representations directly from movement trajectories without manually defining behavioral states or kinematic features. Using behavior data from freely behaving rats, we show that the learned representations not only improve prediction of identity and learning-related readouts over existing methods, but also provide a tool for investigating broader scientific questions about when and how these signals appear in behavior. Specifically, trajectories as short as one second contain informative learning and identity signals. Egocentric pose dynamics are more predictive of identity, whereas allocentric motion is more predictive of learning-related variation, and both signals are strongest at decision junctions in the alternation cohort. We further use a representation-conditioned diffusion model to inspect movement patterns associated with identity and learning progress. Our findings establish short-term movement as an informative behavioral "fingerprint" for studying learning and individual differences. Our code is available at https://github.com/pizhn/TRAIL.

cs.LG↗

arXiv2026

SemVideoSte: Agentic AI for Cross-Modal Semantic Video Steganography Communication

We propose an agentic artificial intelligence (AI)-driven cross-modal semantic video steganography communication (SemVideoSte) scheme for secure cross-modal information transmission. Agentic AI interprets the service context to determine a semantically matched public prompt and coordinates video generation, message embedding, semantic transmission, and message recovery process. A multi-mask mechanism combines variational autoencoder (VAE) reconstruction robustness, semantic transmission robustness, and latent separability to select reliable embedding positions. Meanwhile, a private digital token controls the random seed and guidance offset to construct positive and negative terminal latent states, enabling coverless message embedding during the final diffusion sampling step. Simulation results on open-source datasets demonstrate that SemVideoSte achieves high embedding capacity, reliable message recovery, and strong semantic consistency under different channel conditions. In addition, eavesdroppers without the correct digital token cannot reliably recover the secret message, and the generated stego videos effectively resist the steganalysis detection. We further establish a prototype testbed to verify the practical feasibility of SemVideoSte.

cs.CR↗

arXiv2026

Router Drift Control: Mitigating Off-Policy Degradation in Mixture-of-Experts Reinforcement Learning

Reinforcement learning (RL) can become off-policy due to training-inference mismatch, mini-batch updates, and asynchronous training. The resulting mismatch is especially harmful in Mixture-of-Experts (MoE) models, where rollout-training discrepancies can change routing decisions and lead to router drift. Rollout Routing Replay (R3) mitigates this mismatch by reusing expert selections from the rollout stage during training. Our analysis shows that routing replay introduces gradient bias by losing contributions from experts selected by the training router but not the rollout router. To address this, we propose Router Drift Control (RDC), which uses routing disagreement between rollout and training as a signal of off-policy staleness and selectively rejects high-drift responses. The sequence-level filtering avoids gradient bias introduced by replay while preserving native expert selection and the associated learning signals for retained responses. Experiments show that RDC achieves larger gains as policy staleness increases and remains effective in asynchronous RL. In asynchronous training, RDC combined with Truncated Importance Sampling (TIS) improves the average peak validation accuracy across AIME 2024 and AIME 2025 by 5.5 points over TIS and 4.5 points over R3.

cs.LG↗

arXiv2026

Maximizing Smith-Purcell radiation intensity via multi-parameter optimization

Smith Purcell radiation offers a versatile platform for terahertz light matter interactions and compact source development, with intensity enhancement as a key priority. Using particle in cell simulations across a duty cycle range of from 0.1 to 0.9 at 0.4, 0.5, and 0.6 THz, we optimize the grating period, electron kinetic energy and duty cycle. The intensity exhibits a unimodal dependence on the grating period at all three frequencies, with peak enhancements of 96, 34, and 17.5 fold relative to our reference baseline . The optimal duty cycle shifts from 0.2 to 0.4 with increasing frequency, a trend arising from the trade off between coherent superposition and beam-wave coupling under fixed length conditions. These results provide systematic design guidelines for compact tabletop terahertz sources.

physics.app-ph↗

arXiv2026

Tokenized Joint Source-Network Coding for Loss-Resilient Video Streaming

For decades, conventional video streaming systems have relied on packet-level forward error correction (FEC) codes and retransmissions to ensure reliable video delivery. However, in real-time communications, FEC is inherently inefficient under unknown and time-varying packet-loss conditions, while retransmission can incur excessive latency and fail to meet stringent playout deadlines. Emerging deep joint source-channel coding (DeepJSCC) provides an alternative by learning channel-noise-resilient representations, yet its tight coupling with the physical layer makes it difficult to integrate into today's layered network architectures and protocols. In this paper, we propose TokJSNC, a simple-but-effective tokenized joint source-network coding paradigm for packet-loss-resilient video streaming. Rather than first compressing video into a compact bitstream and then protecting it with packet-level FEC, TokJSNC directly encodes video into variable-length discrete token-stream, whose transmission rate is jointly adapted to video content complexity and network conditions. By exploiting the spatiotemporal correlations among learned video tokens, TokJSNC enables robust video reconstruction over a wide range of packet-loss rates without explicit FEC or retransmissions. Consequently, TokJSNC brings the JSCC-like graceful degradation property to the network layer while remaining compatible with existing network architectures and transport protocols. Experimental results show that TokJSNC maintains an average playback frame rate above 22 fps even at a 70% packet-loss rate, whereas the best-performing baseline drops below 12 fps. Overall, TokJSNC establishes a practical pathway for repurposing modern generative video tokenizers for loss-resilient streaming, while providing a fundamental framework for video token communications.

eess.IV↗

arXiv2026

Local profiles of self-similar solutions of the planar stationary Navier--Stokes equations

In this paper, we revisit self-similar solutions of the two-dimensional stationary incompressible Navier-Stokes equations under scaling symmetries, also known as Jeffery-Hamel solutions. We investigate the local patterns of smooth Jeffery-Hamel solutions in a conical subdomain $Ω$ with vertex at the origin, without imposing any boundary conditions on $Ω$. For radial Jeffery-Hamel solutions, we obtain all the explicit local profiles in $Ω$ with arbitrary opening angles. In the non-radial case, we show that some Jeffery-Hamel solutions can be obtained via solving a Liénard equation, and we derive new explicit local profiles expressible in terms of Weierstrass elliptic functions.

math.AP↗

arXiv2026

VenusRL: A Fully Disaggregated Agentic RL System with Priority Scheduling and Scalable Interaction

Agentic Reinforcement Learning (RL) trains LLM agents through multi-turn interactions with external tool environments. Its multi-turn nature exposes two system-level bottlenecks unaddressed by existing agentic RL frameworks. First, end-to-end training throughput is constrained by the slowest trajectories to complete, yet optimizing per-GPU utilization alone scatters rollout progress across many groups, delaying the completion of enough groups to unblock the next training step. Second, tool sandboxes are statically over-provisioned by their declared memory ceilings, leaving most physical memory stranded while replicating near-identical state across sandboxes launched from the same prompt. We present VenusRL, a fully disaggregated agentic RL system that addresses both bottlenecks. VenusRL's priority-aware action scheduler uses length-prediction heuristics to identify sample groups whose completion is most likely to unblock the next training step, and pushes them ahead of others across batch admission, KV Cache residency, and cross-worker request orchestration. VenusRL's environment resource manager combines a memory-aware admission threshold with a template-keyed page-sharing pool, packing more sandboxes per node while preserving strict memory isolation via write-protected page table entry aliasing and copy-on-write. Across representative agentic RL workloads, VenusRL achieves up to 4.24x end-to-end training speedup over state-of-the-art baselines and reduces environment cost by up to 89%.

cs.DC↗

arXiv2026

STRIDE: Spatial-Temporal Representation for Interval-conditioned Disease Evolution in Longitudinal Glioblastoma MRI

Glioblastoma (GBM), an aggressive primary brain tumor, is routinely monitored with longitudinal MRI after treatment. Distinguishing stable disease (SD), pseudoprogression (PsP), and true progression (TP) remains challenging because these states can show overlapping MRI appearances despite different temporal trajectories. Existing longitudinal methods still face challenges in modeling scan-specific spatial variability, variable follow-up intervals, and complementary information from the observed follow-up state and its longitudinal change. We propose STRIDE, a framework for spatial-temporal representation of interval-conditioned disease evolution that takes paired post-treatment MRI scans and their inter-scan interval as input and predicts SD, PsP, or TP. The lesion-prior-guided spatial representation combines SoftGate and an Adaptive-window Hierarchical Transformer (AWHT) to emphasize lesion-related regions while preserving surrounding context. The time-conditioned latent transition uses pair-level context and the actual inter-scan interval to estimate interval-dependent representation changes between visits. The observed--transition fusion integrates the transition-estimated follow-up representation with the directly observed follow-up representation to jointly characterize the follow-up state and its longitudinal change. BraTS2024 is used to develop and evaluate the lesion-prior generator, while longitudinal pretraining on LUMIERE supports transfer before downstream adaptation to Burdenko. On the Burdenko three-class task, STRIDE achieves a macro ROC--AUC of 0.816 and a macro F1-score of 0.796. These results support its potential for more reliable longitudinal post-treatment GBM state assessment.

eess.IV↗

arXiv2026

Determination of van der Waals thin film thickness via characteristic X-ray photon yield

Characteristic X ray generation is essential for producing X ray sources and for investigating light matter interactions. Here, we demonstrate that characteristic X ray generation can also be employed to determine the thickness of van der Waals thin films in a non destructive manner. Specifically, we establish a sixth order polynomial model that relates film thickness to the characteristic X ray yield. The model can both predict the X ray yield of graphite films at arbitrary thicknesses and infer the film thickness from a corresponding characteristic X ray yield. We validate the model by comparing its predicted X ray yields for graphite films with those obtained from Monte Carlo simulations. Additionally, we experimentally validated the reliability and feasibility of the proposed method for extracting graphite film thickness via X ray photon yield characterization. Our results provide a non destructive approach for determining the thickness of van der Waals thin films.

physics.app-ph↗

arXiv2026

A Mathematical Theory of Pragmatic Information

We propose a mathematical theory of pragmatic information that connects communication, control, and decision-making. Its central notion is the isoteleia mapping, which formalizes equifinality: distinct semantic paths that lead to the same optimal action are treated as pragmatically equivalent. This mapping yields a three-tier hierarchy of syntactic, semantic, and pragmatic information, in which each successive abstraction removes distinctions that are irrelevant to the task. We then define pragmatic entropy, up/down pragmatic mutual information, channel capacity, and rate-distortion, and prove lossless source coding, channel coding, and rate-distortion theorems that extend Shannon's results. These measures quantify decision uncertainty, reliable transmission, and task-oriented compression at the level of terminal actions. We further introduce pragmatic value of information (VoI) and pragmatic cost of information (CoI) as decision-theoretic duals to rate-distortion and capacity, and develop a Lagrangian dual framework for cross-layer optimization. The resulting pragmatic efficiency bound $\mathcal{E}_p(λ)=\sup_R[Φ_p(R)-λ\mathrm{CoI}_p(R)]$ characterizes the maximum net utility attainable by a resource-constrained intelligent system under a given resource price, yielding a behavioral capacity that extends Shannon's symbol-level capacity to goal-directed action. Extensions to continuous messages provide closed-form expressions for Gaussian channels and sources, while dynamic settings are addressed through a Bellman equation for sequential decision-making. The framework supports task-oriented communication, networked control, autonomous systems, and embodied AI by shifting emphasis from symbol fidelity to the effectiveness of information in guiding actions. In this way, it offers a common language for systems that extract value from information under resource constraints.

cs.IT↗

arXiv2026

From Retrieval to Reasoning: Agentic Mechanism Prediction from Cell Painting Profiles

Cell Painting is a high-content morphological profiling assay widely used for phenotype-based biological inference, with mechanism of action (MOA) prediction as a central application. Existing approaches largely formulate Cell Painting-based inference as representation matching, assigning predictions from nearby reference perturbations in morphological feature space. However, retrieved neighbors are often noisy and partially misleading evidence due to batch effects, non-specific cytotoxicity, phenotypic convergence, and source-dependent variability. We reformulate Cell Painting-based MOA prediction as a calibrated evidence reasoning problem, where retrieved neighbors are treated as uncertain observations that must be evaluated, compared, and sometimes rejected before supporting a mechanistic conclusion. We propose PhenoAIR, a reliability-aware multi-agent framework that maintains a candidate-centric evidence memory and performs controller-guided refinement over phenotype- and mechanism-side evidence. PhenoAIR uses offline reference-set calibration to weight evidence by source reliability, phenotype stability, and mechanism-level confusion. We evaluate PhenoAIR on a benchmark constructed from JUMP Cell Painting profiles and annotations, covering controlled, realistic, and discovery-oriented open-world MOA prediction settings. PhenoAIR outperforms representation-matching and LLM-based baselines across all settings.

cs.AI↗

arXiv2026

Efficient Agentic LLM Serving over SSD-based Sparse KV Storage

Agentic sessions driven by Large language models (LLMs) often alternate between model inference and tool use, accumulating long histories across successive rounds. Serving these sessions efficiently requires reducing attention computation and retaining history key-value (KV) caches to avoid recomputation. Recently, frontier open-source LLMs adopt sparse attention to reduce computation by selecting only part of the history, while SSDs provide a cheaper alternative to CPU DRAM for storing KV caches. However, sparse KV selection depends on the ad hoc intermediate values during model inference, so it forces SSD reads to lie on the inference critical path. These reads are further slowed by fragmented accesses and read-write interference in SSDs. To address these challenges, we present Janus, an agentic serving framework for sparse attention LLMs with SSD-centric KV storage. Janus focuses on append prefill, which processes each round's newly added inputs and accounts for most history KV loading. To move SSD reads out of the critical path, Janus runs the model's own KV selection module on earlier intermediate values, predicting KV demand without additional training. The predicted reads overlap with model computation, and any prediction misses are fetched before attention executes to preserve model outputs. To improve SSD efficiency, Janus coalesces adjacent reads, packs scattered KV pages into sequential writes on the CPU, and limits background writes while reads are active. Across three models and three agentic traces, Janus outperforms existing works by up to 1.57-3.69 times (1.22-1.85 times on average) in terms of the time to first token latency, while maintaining decode efficiency.

cs.DC↗

arXiv2026

Deco: Extending Cherished Physical Objects into AI Companion Agents through Dual Embodiment

Physical objects (e.g., plush toys) can transcend materiality to become emotional anchors and provide companionship. However, these bonds remain one-sided because most physical objects cannot reciprocate. AI companions offer responsiveness and personalization, but typically entail building bonds from scratch. We investigated how AI companions might inherit and extend users' existing bonds with physical objects. A formative study (N=9) informed four design principles (Faithful Identity, Calibrated Agency, Ambient Presence, Reciprocal Memory), shaping our Dual-Embodiment Companion Framework. We instantiated it as Deco to create digital embodiments of physical companions. In a within-subjects lab study (N=25), Deco was rated higher than a personalized digital-only companion on six companion-related measures (all p<.01). A subsequent seven-day field deployment (N=17) showed sustained engagement, higher post-deployment well-being (p=.040), and three key relational patterns: digital activities retroactively vitalized physical objects, bond deepening centered on emotional engagement depth, and participants sustained bonds while navigating companions' AI nature. Dual embodiment offers a promising framework for revitalizing physical objects with AI agents.

cs.HC↗

arXiv2026

Dual-Branch Vector-Quantization-Aided Satellite Digital Semantic Communication with Index Compression for High-Resolution RSI Over AFDM

High-resolution remote sensing imagery (RSI) transmission is constrained by satellite-ground bandwidth and channel impairments, yet existing methods struggle to simultaneously achieve extreme compression and robust transmission. To address this, we propose a dual-branch vector-quantization aided satellite digital semantic communication (DVQ-SDSC) framework for RSI transmission over affine frequency division multiplexing (AFDM), whose bandwidth savings arise from two interrelated aspects. First, at the source-coding level, a dual-branch framework is developed to unify deep joint semantic coding, VQ-aided index transmission, channel estimation and adaption in an end-to-end architecture; departing from symmetric encoder designs, the codec is recast as an asymmetric dual-branch architecture that separately processes the high-frequency residuals and the low-frequency structural semantics, with gated fusion and channel-adaptive reconstruction jointly restoring the semantic content. Second, at the index-coding level, we develop a principal component analysis (PCA)-aided codebook reordering to align index topology with latent correlations, and devise group differential pulse-code modulation (G-DPCM) to encode prediction residuals rather than absolute indices, lowering the index bitrate while locally isolating clipping and channel errors. A two-stage training strategy further decouples channel impairments from the semantic codec. FAIR1M experiments over 3GPP NTN-TDL-D demonstrate that DVQ-SDSC with G-DPCM index coding outperforms the conventional JPEG-LDPC scheme at the base rate of 0.0625 bits per pixel (BPP), and that G-DPCM applies directly to the trained codec without retraining, yielding additional index compression at no extra cost.

cs.IT↗

arXiv2026

Equation of state and transport coefficients of warm dense aluminum from mixed deterministic-stochastic density functional theory

Aluminum is a reference standard in high-energy-density research and serves as a liner material in megampere Z-pinch facilities. Using mixed deterministic-stochastic finite-temperature density functional theory, we compute the equation of state and transport coefficients of liquid aluminum up to temperatures of 1000 eV and compare the results with model-based approaches. We find that for T<200 eV the density-functional results differ from the models by more than 10% at high density, with a maximum deviation of over 30% (at T = 10 eV), leading to significant discrepancies in the Hugoniot curve at high compression; the calculated electrical and thermal conductivities deviate from model predictions by 26% to 63% at T~100 eV. We further employ the Drude model and the Epperlein-Haines framework to examine the magnetic-field dependence of the conductivities. These results can provide valuable input data for radiation-hydrodynamics codes.

physics.plasm-ph↗

arXiv2026

From Ideal Motion to Flight-Executable Communications: LLM-Evolved Multi-UAV Deployment for Cell-Free Massive MIMO

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a promising paradigm for future wireless networks, providing user-centric services and cooperative coverage. By using unmanned aerial vehicles (UAVs) as aerial access points, CF-mMIMO networks can exploit UAV mobility to enhance three-dimensional (3D) coverage and spectral efficiency (SE). However, most existing studies on UAV deployment for communication optimization typically assume that UAVs follow ideal point-mass motion (IM), neglecting flight-control constraints and finite-horizon position errors in real flight execution. Consequently, IM-trained deployment policies may degrade severely or become difficult to execute in practice. Motivated by this, we model each UAV as a six-degree-of-freedom quadrotor rigid body with a cascaded flight controller to capture the impact of flight-control-constrained motion (FM) on communication optimization. Based on this model, we formulate a joint UAV 3D deployment and power allocation problem under FM to maximize the downlink average SE in CF-mMIMO networks. To address this problem, we propose LERE, a large language model (LLM)-enhanced multi-agent reinforcement learning (MARL) framework. In LERE, the LLM evolves hybrid rewards with both global and local components via multi-level feedback. The evolved hybrid rewards guide MARL policy optimization and promote multi-UAV cooperation. Experimental results demonstrate that LERE achieves higher SE than reward-design baselines while substantially reducing UAV position errors. Notably, when tested under FM execution, the FM-trained LERE policy achieves a 60.49\% SE gain over its IM-trained counterpart, confirming the necessity of incorporating flight-control constraints into UAV-enabled CF-mMIMO optimization.

eess.SP↗