SCOPE: Score-Isolated Agentic Optimization for Video World Models

Video world models are increasingly used as simulators for planning and embodied decision making, yet improving them at inference time introduces a subtle evaluation problem: prompts, samplers, verifiers, and selectors may evolve together, making it difficult to attribute gains or prevent held-out feedback from shaping the final policy. We introduce \scope (\emph{\scopefullname}), a framework for auditable inference-time adaptation of frozen video world models. \scope represents external controls as a typed state, updates this state only through bounded changes supported by development evidence, and freezes the resulting policy before held-out evaluation. On Physics-IQ benchmark, \scope improves over the exact frozen base by $+14.24$ (95\% CI $[+8.10,+21.23]$). Controlled ablations further identify gains from scene specification, sampling, and learned selection, while the margin over the strongest matched agentic baseline remains unresolved. Cross-backbone and prospective evaluations reveal a complementary result: useful inference-time updates exist, but their benefits do not transfer uniformly across models and settings. Together, these findings suggest that reliable inference-time adaptation requires not only better proposals, but also a principled mechanism for deciding which updates should become part of the deployed system. Code is available at https://github.com/YuhuaJiang2002/SCOPE.

New Mid-Band (FR3, 6-24 GHz) XL-MIMO for 6G: Channel Modeling, Algorithm Evaluation, and Field Trials

The new mid-band (FR3, 6-24 GHz) spectrum is expected to play an important role in future 6G networks by providing a favorable balance among coverage, capacity, and deployment feasibility. Meanwhile, extremely large-scale multiple-input multiple-output (XL-MIMO) has emerged as a key enabling technology to exploit the propagation and spatial multiplexing potential of these frequency bands. Firstly, this paper provides a systematic review of spectrum allocation and standardization activities for new mid-band spectrum, together with the 6G spectrum planning strategies of countries and regions. Secondly, the wideband massive MIMO channel sounder is also introduced, which is specially developed for channel measurements of new mid-band with over a thousand elements. Thirdly, propagation characteristics and channel modeling approaches of four representative XL-MIMO architectures, including co-located, cell-free, and intelligent XL-MIMO, are comprehensively reviewed and analyzed, with particular emphasis on near-field propagation, spatial non-stationarity, and capacity performance. Then, recent advances in channel estimation, beamforming, and artificial-intelligence-assisted signal processing are summarized. In addition, the performance of new mid-band XL-MIMO systems equipped with 1536 and 768 antenna elements is comparatively evaluated. Finally, real communication environment prototype system field trials conducted in the Upper 6 GHz (U6GHz) band are used to investigate practical system performance under realistic deployment conditions. The results indicate that the target signal-to-noise ratio is a critical factor affecting XL-MIMO performance in the U6GHz band.

Low-Latency Generative Semantic Communication via Channel-Realization Flow Matching

Generative semantic communication receivers deliver high perceptual quality but suffer from prohibitive decoding latency. This bottleneck arises because diffusion receivers rely on stochastic iterative decoding, while existing flow matching receivers employ independent endpoint coupling that ignores the physical source--channel link, yielding unnecessarily long and curved sampling trajectories. In this paper, we reformulate receiver-side recovery as a realization-coupled bridge flow matching problem under explicit bandwidth and power constraints. Specifically, we propose Realization-Coupled Bridge Flow Matching (RC-BFM), where the decoder initializes from a channel-induced semantic state rather than isotropic noise. Crucially, training pairs are linked via a realization-coupled entropic optimal transport (RC-OT) plan that preserves the physical channel realization of each transmission while maintaining robustness to stochastic fading. Furthermore, we identify independent coupling as the fundamental source of a conditional train--test distribution shift in conditional flow matching-based receivers, and derive an end-to-end distortion bound whose discretization error decays as \(O(K^{-2})\). Experiments on CIFAR-10 and FFHQ-64\(\times\)64 over AWGN and Rayleigh fading channels demonstrate that RC-BFM achieves a superior fidelity--perception trade-off, reducing decoding latency by over 10\(\times\) compared to diffusion-based receivers.

ISAC and Vision Fusion for Fine-Grained Low-Altitude Target Recognition

In this paper, we propose an integrated sensing and communications (ISAC) and vision fusion framework for fine-grained low-altitude target recognition. Specifically, we first utilize ISAC system to estimate the position of the low-altitude target. Then we adjust the working parameters of the Pan-TiltZoom (PTZ) camera based on the estimated target position, such that the camera can capture the image of tiny low-altitude target from several hundred meters away. After obtaining the wireless echo signal and visual image of low-altitude target, we employ the short-time Fourier transform (STFT) to obtain the micro-Doppler (mD) spectrum of the target from wireless echo signal, and design a conditional generative adversarial network (cGAN)-based denoising network to optimize the quality of the mD spectrum. Meanwhile, we employ YOLOv11 to detect the low-altitude target from visual image, and then crop the smallsized feature image of the target from the original image. Next, we design a fine-grained low-altitude target recognition network with MobileViT, which can fuse the optimized mD spectrum and the cropped feature image to distinguish the subcategory of low-altitude target. Moreover, we generate a joint ISAC and vision dataset (JIVD) for low-altitude target monitoring based on AirSim and Wireless InSite, which includes diverse target subcategories, scenarios, and weather conditions. The effectiveness and superiority of the proposed scheme have been demonstrated by simulation results.

Mode Switching for RDARS-Aided ISAC Systems: From Optimization to Deep Unfolding

Reconfigurable distributed antennas and reflecting surface (RDARS) has recently emerged as a promising architecture for integrated sensing and communication (ISAC), owing to its flexible element-wise mode switching between connection and reflection modes. In this paper, to fully reap the benefits of mode configuration, muting elements that can absorb the incident energy are introduced into RDARS-aided ISAC systems to mitigate multi-user interference (MUI) and enhance sensing performance. To draw useful insights, we first investigate the special cases of single-UE communication, single-target sensing, and two-UE communication to reveal the importance of muting elements. Specifically, the maximum communication and sensing signal-to-noise ratio (SNR), and the signal-to-interference-plus-noise ratio (SINR) expressions are respectively derived for the three cases, together with the optimal number of muting elements for explicitly characterizing the tradeoff between reflection gain loss and MUI suppression. Next, we consider the joint waveform and tri-mode switching design for RDARS-aided ISAC systems, where an alternating optimization (AO)-based penalty dual decomposition (APDD) algorithm is proposed to solve the mixed-integer nonlinear programming (MINLP) problem. Furthermore, a model-driven APDD-Net is developed by deeply unfolding the APDD iterations into a layer-wise architecture, where key parameters are learned to reduce the computational complexity and accelerate convergence. Simulation results verify the theoretical findings on the muting gain and demonstrate that the proposed APDD-Net achieves a better tradeoff between communication and sensing performance compared with benchmark schemes.

Networked ISAC Enabled Target Recognition Towards Low-Altitude Economy

In this paper, we propose a low-altitude target (LAT) recognition scheme based on multi-base station (BS) collaboration and multi-scale feature fusion for integrated sensing and communications (ISAC) network. Firstly, we formulate the motion equations, echo channels, and echo signals for unmanned aerial vehicle (UAV), bird, vehicle, and pedestrian under multi-BS collaborative monitoring scenario. Then we extract the velocityresolution-preferred time-frequency spectrum, time-resolutionpreferred time-frequency spectrum, and the velocity-transfer time-frequency spectrum observed by each BS from echo signals. We collectively refer to these three types of time-frequency spectrum as the multi-scale feature of the LAT. Next, we design a multi-BS and multi-scale feature fusion enabled LAT recognition network with Swin Transformer, which employs the visualized images of multi-scale feature to jointly recognize the target through deep feature extraction, intra-BS feature interaction, inter-BS feature interaction, and target recognition output. We generate a massive echo signal dataset comprising 1,440,000 samples for LAT recognition within ISAC network. This dataset can serve as a public benchmark to evaluate our proposed scheme and facilitate future research. Simulation results demonstrate that the proposed scheme realizes high recognition accuracy and robust unseen-subtype generalization, confirming the effectiveness of multi-scale feature fusion and the additional gains brought by multi-BS collaboration. The project page is available at: https://alivn999.github.io/COSMOS-Networked-ISAC-Enabl ed-Target-Recognition-Towards-Low-Altitude-Economy/.

CKT-WAM: Parameter-Efficient Context Knowledge Transfer Between World Action Models

World action models (WAMs) provide a powerful generative framework for embodied control, yet transferring knowledge across heterogeneous WAMs remains challenging due to mismatched latent interfaces, high adaptation cost, and the rigidity of conventional distillation objectives. We propose \textbf{CKT-WAM}, a parameter-efficient \textbf{C}ontext \textbf{K}nowledge \textbf{T}ransfer framework that transfers teacher WAM's knowledge into a student WAM through a compact context in the text embedding space, rather than output imitation or dense hidden-state matching. Specifically, CKT-WAM extracts intermediate teacher hidden states, reduces the number of tokens via compressors' learnable-query cross attention (LQCA), and transforms them through an always-on generalized adapter, a lightweight router, and sparsely activated specialized adapters. The resulting context is then appended to the student's conditioning textual embeddings, thereby injecting the transferred knowledge into the student with minimal architectural modification. Experiments show that CKT-WAM consistently improves zero-shot generalization and achieves the best overall performance on LIBERO-Plus, reaching 86.1\% total success rate with only 1.17\% trainable parameters, while approaching full fine-tuning performance. Beyond simulation, CKT-WAM also demonstrates strong real-world long-horizon manipulation ability, achieving the best average success rate of 83.3\% across four multi-step and long-horizon tasks. Code is available at https://github.com/YuhuaJiang2002/CKT-WAM.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts

Vision-language-action (VLA) models inherit rich visual-semantic priors from pre-trained vision-language backbones, but adapting them to robotic control remains challenging. Full fine-tuning (FFT) is prone to overfitting on downstream robotic data and catastrophic forgetting of pretrained vision-language capabilities. Parameter-efficient fine-tuning (PEFT) better preserves pre-trained knowledge, yet existing PEFT methods still struggle to adapt effectively to robot control tasks. To address this gap, we propose VLA-GSE, a parameter-efficient VLA fine-tuning framework that improves control adaptation while retaining PEFT's knowledge preservation advantage. Specifically, VLA-GSE (Generalized and Specialized Experts) is initialized by spectrally decomposing the frozen backbone, assigning leading singular components to generalized experts (shared experts) and disjoint residual components to specialized experts (routed experts). This decomposition improves adaptation capacity under a fixed trainable-parameter budget. Under a comparable parameter budget, VLA-GSE updates only 2.51% of the full model parameters and consistently outperforms strong FFT and PEFT baselines. It achieves 81.2% average zero-shot success on LIBERO-Plus, preserves pre-trained VLM capability comparably to LoRA on multimodal understanding benchmarks, and improves real-world manipulation success under multiple distribution shifts. Code is available at: https://github.com/YuhuaJiang2002/VLA-GSE

AirGuard: UAV and Bird Recognition Scheme for Integrated Sensing and Communications System

In this paper, we propose an unmanned aerial vehicle (UAV) and bird recognition scheme with signal processing and deep learning for integrated sensing and communications (ISAC) system. We first provide the basic scene of low-altitude targets monitoring, and formulate the motion equations and echo signals for UAVs and birds. Next, we extract the centralized micro-Doppler (cmD) spectrum and the high resolution range profile (HRRP) of the low-altitude target from the echo signals. Then we design a dual feature fusion enabled low-altitude target recognition network with convolutional neural network (CNN), which employs both the images of cmD spectrum and HRRP as inputs to jointly distinguish between UAV and bird. Meanwhile, we generate 237600 cmD and HRRP image samples to train, validate, and evaluate the designed low-altitude target recognition network. The proposed scheme is termed as AirGuard, whose effectiveness has been demonstrated by simulation results.

AsyncVLA: Asynchronous Flow Matching for Vision-Language-Action Models

Vision-language-action (VLA) models have recently emerged as a powerful paradigm for building generalist robots. However, traditional VLA models that generate actions through flow matching (FM) typically rely on rigid and uniform time schedules, i.e., synchronous FM (SFM). Without action context awareness and asynchronous self-correction, SFM becomes unstable in long-horizon tasks, where a single action error can cascade into failure. In this work, we propose asynchronous flow matching VLA (AsyncVLA), a novel framework that introduces temporal flexibility in asynchronous FM (AFM) and enables self-correction in action generation. AsyncVLA breaks from the vanilla SFM in VLA models by generating the action tokens in a non-uniform time schedule with action context awareness. Besides, our method introduces the confidence rater to extract confidence of the initially generated actions, enabling the model to selectively refine inaccurate action tokens before execution. Moreover, we propose a unified training procedure for SFM and AFM that endows a single model with both modes, improving KV-cache utilization. Extensive experiments on robotic manipulation benchmarks demonstrate that AsyncVLA is data-efficient and exhibits self-correction ability. AsyncVLA achieves state-of-the-art results across general embodied evaluations due to its asynchronous generation in AFM. Our code is available at https://github.com/YuhuaJiang2002/AsyncVLA.