arXiv subjects

Jie Xu

Publications and source records attributed to Jie Xu.

At least 73 records · Page 4Linked to original sources

arXiv2026

Importance-Aware Resource Allocation for Collaborative Task-Oriented Semantic Communication

Task-oriented semantic communication must allocate scarce radio resources to semantic features under fast fading wireless conditions and strict end-to-end latency budgets. Existing solutions are either optimization-heavy, leading to prohibitive computational overhead during online operation, or rely on end-to-end retraining procedures together with slowly varying channel assumptions. We propose iCoTASC (importance-aware Collaborative Task-Oriented Semantic Communication), a hybrid offline-online framework designed for collaborative multi-device semantic communication systems. iCoTASC leverages attribution-based importance to guide per-dimension embedding selection as a practical communication control signal, models diminishing semantic returns of quantization through a data-driven utility function, and precomputes per-transmitter utility lookup tables offline, which together enable lightweight online scheduling via table lookup and low-complexity refinement under time-varying channels. The proposed framework supports real-time, channel-adaptive semantic resource allocation in distributed systems without requiring retraining of the underlying task inference model.

cs.DC↗

arXiv2026

Streaming-dLLM: Accelerating Diffusion LLMs via Suffix Pruning and Dynamic Decoding

Diffusion Large Language Models (dLLMs) offer a compelling paradigm for natural language generation, leveraging parallel decoding and bidirectional attention to achieve superior global coherence compared to autoregressive models. While recent works have accelerated inference via KV cache reuse or heuristic decoding, they overlook the intrinsic inefficiencies within the block-wise diffusion process. Specifically, they suffer from spatial redundancy by modeling informative-sparse suffix regions uniformly and temporal inefficiency by applying fixed denoising schedules across all the decoding process. To address this, we propose Streaming-dLLM, a training-free framework that streamlines inference across both spatial and temporal dimensions. Spatially, we introduce attenuation guided suffix modeling to approximate the full context by pruning redundant mask tokens. Temporally, we employ a dynamic confidence aware strategy with an early exit mechanism, allowing the model to skip unnecessary iterations for converged tokens. Extensive experiments show that Streaming-dLLM achieves up to 68.2X speedup while maintaining generation quality, highlighting its effectiveness in diffusion decoding. The code is available at https://github.com/xiaoshideta/Streaming-dLLM.

cs.LG↗

arXiv2026

Enhancing Brain MRI Anomaly Detection and Reasoning with ROI Rethink and Synthetic Data

Medical vision-language models typically generate diagnoses through single-pass inference without indicating which image regions support their conclusions. This lack of spatial grounding limits clinical utility: outputs cannot be audited, and models may hallucinate findings on normal scans. We present BrReMark (Brain Rethink via ROI Marking), a framework that introduces explicit region marking into brain MRI diagnosis. The model first generates hypotheses about potential abnormalities and grounds them through explicit bounding box marking, then verifies conclusions by re-examining the marked evidence. Training combines supervised fine-tuning on structured reasoning trajectories with reinforcement learning using a composite reward over localization accuracy and diagnostic reasoning. Furthermore, we integrate a domain randomization-based pathology synthesis augmentation strategy to improve the model's generalizability to out-of-distribution (OOD) data. On internal benchmark, BrReMark improves mAP50 from 0.74% to 37.54% compared to the base model, while achieving 21.57% Clinical F1 and 45.26% diagnostic accuracy. On NOVA OOD benchmark, it also achieves competitive overall performance with a 45.7% reduction in false positives compared to the state-of-the-art, indicating reduced hallucination on rare pathologies. These findings suggest that explicit hypothesis-verification grounding is a practical path toward trustworthy open-ended brain MRI diagnosis across both in-distribution and OOD settings.

cs.CV↗

arXiv2026

Sense Smarter, Think Better: A Survey on Edge Perception for Next-Generation Networks

Edge perception has emerged as a foundational capability for future wireless networks, enabling the network edge to proactively sense, interpret, and interact with the physical environment in a task-oriented and resource-aware manner. This survey provides a comprehensive and structured overview of edge perception. We first review representative sensing modalities and edge artificial intelligence (AI) techniques as the fundamental building blocks. We then examine their synergistic interactions. We systematically analyze how edge AI enhances sensing capabilities, encompassing both in-band and out-of-band modalities, as well as multi-modal sensor data fusion. Moreover, we discuss the role of task-driven sensing in facilitating edge AI, including integrated sensing-communication-computation designs, and active perception frameworks that dynamically adapt sensing strategies for downstream applications. Finally, we identify key challenges and open issues. By consolidating fragmented research across sensing, communication, and edge AI, this survey provides forward-looking insights for the design and implementation of edge perception systems for sixth-generation (6G) networks.

eess.SP↗

arXiv2026

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining

Effective data selection is essential for pretraining large language models (LLMs), enhancing efficiency and improving generalization to downstream tasks. However, existing approaches often require leveraging external pretrained models, making it difficult to disentangle the effects of data selection from those of the external pretrained models. In addition, they often overlook the long-term impact of selected data if the model is trained to convergence, primarily due to the prohibitive cost of full-scale LLM pretraining. In this paper, we introduce BLISS (\textbf{B}ileve\textbf{L} \textbf{I}nfluence \textbf{S}coring method for data \textbf{S}election): a lightweight data selection method that operates entirely \emph{from scratch}, without relying on any external pretrained oracle models, while explicitly accounting for the long-term impact of selected data. BLISS leverages a small proxy model as a surrogate for the LLM and employs a score model to estimate the long-term influence of training samples if the proxy model is trained to convergence. We formulate data selection as a bilevel optimization problem, where the upper-level objective optimizes the score model to assign importance weights to training samples, ensuring that minimizing the lower-level objective (i.e., training the proxy model over the weighted training loss until convergence) leads to best validation performance. Once optimized, the trained score model predicts influence scores for the dataset, enabling efficient selection of high-quality samples for LLM pretraining. We validate BLISS by pretraining 410M/1B/2.8B Pythia and LLaMA-0.5B models on selected subsets of the C4 dataset. Notably, under the 1B model setting, BLISS achieves $1.7\times$ speedup in reaching the same performance as the state-of-the-art method, demonstrating superior performance across multiple downstream tasks.

cs.LG↗

arXiv2026

Online LLM Selection via Constrained Bandits with Time-Varying Demand

Large Language Models (LLMs) are increasingly deployed in edge-cloud inference systems to handle diverse user tasks with heterogeneous accuracy, latency, and cost profiles. Selecting the appropriate LLM for each incoming task is critical for ensuring service quality and efficient resource utilization. However, model heterogeneity, stochastic and unknown performance characteristics, and time-varying task demands make static selection strategies inadequate. Real-world deployments often impose hard resource budgets such as monetary expenditure limits, along with soft service-level requirements such as latency guarantees. These constraints introduce additional challenges for online decision-making. We formulate this problem as a constrained stochastic bandit learning task, where the learner sequentially selects models under both packing-type (hard) and covering-type (soft) constraints, while adapting to time-varying task demand. The learner operates without access to the underlying reward, cost, or latency distributions and must rely on partial feedback. We develop a novel online learning algorithm that leverages confidence-bound estimates and demand predictions to balance reward maximization with long-term constraint satisfaction. We provide theoretical guarantees showing sublinear regret and sublinear covering constraint violations compared to an offline benchmark with full information. Experimental results on synthetic workloads demonstrate the effectiveness and robustness of our approach in dynamic, resource-constrained environments.

cs.LG↗

arXiv2026

EAGG: Embodiment-Aligned Grasp Generation via Geometry-Aware Graph Conditioning

Cross-end-effector grasp generation seeks a unified model that generalizes across objects and across embodiments ranging from parallel grippers to dexterous end effectors. Existing grasp generators are typically designed for a fixed embodiment or encode embodiment identity with a static descriptor, which weakens transfer when topology, actuation coupling, and contact geometry differ substantially. We present EAGG, an embodiment-aligned grasp generator that represents each embodiment with a topology-aware end-effector graph and an embodiment-specific low-dimensional end-effector control space. A frozen end-effector-cognition backbone converts the current articulated state into geometry-aware tokens that act as a reusable morphology prior, and iterative geometry injection refreshes these tokens throughout sampling so that conditioning remains synchronized with the evolving end-effector geometry. On the MultiGripperGrasp benchmark, EAGG reaches 56.17% average success across six training end effectors, remaining within 1.10 percentage points of specialized training while preserving transfer to finetuning and zero-shot end effectors. Iterative geometry injection further reduces the pooled median contact distance from 0.239 cm to 0.189 cm. These results show that cross-end-effector grasp generation is strengthened by aligning embodiment structure inside a shared generator rather than suppressing embodiment differences. Code is available at https://github.com/wanhaoniu/EAGG.

cs.RO↗

arXiv2026

A geometric reformulation and structure-preserving rotational discrete gradient scheme for the full Ericksen-Leslie model

In this paper, we present a structure-preserving rotational discrete gradient method of the full Ericksen-Leslie model for nematic liquid crystal flows with general anisotropic Oseen-Frank elasticity and full Leslie stress. The main difficulty resides in preserving both the pointwise unit-length constraint on the director field and the energy-dissipation at the discrete level, which involves high nonlinearities in the interplay between the length constraint, anisotropic elastic energy, and hydrodynamic couplings. To address this issue, we first reformulate the Ericksen-Leslie system into an equivalent rotational form in which the director evolution is intrinsically tangent to the unit sphere and the coupling terms in the Leslie and Ericksen stresses are reorganized so that the energy exchange and dissipation structure become explicit. Based on this reformulation, we construct rotational discrete gradient schemes that preserve the director length and satisfy an unconditional discrete energy law, together with a fully discrete scheme based on an exact divergence-free spectral approximation. Numerical experiments verify the accuracy, unit-length preservation and energy-stability of the proposed method, and illustrate dynamical effects induced by anisotropic elastic coefficients and shear flow in the full Ericksen-Leslie model.

math.NA↗

arXiv2026

Fully decoupled, linear and structure-preserving block-centered finite difference methods for the Keller-Segel chemotaxis system on staggered non-uniform grids

In this paper, we propose two fully decoupled, linear and structure-preserving block-centered finite difference schemes for the classical Keller-Segel chemotaxis system on staggered non-uniform spatial grids. Both novel schemes are second-order accurate in space; one is first-order accurate in time, while the other achieves second-order temporal accuracy. Moreover, we show that the schemes preserve several inherent physical laws at the discrete level: (i) the positivity of both the cell density and the chemoattractant concentration; (ii) the conservation of total cell mass; and (iii) a discrete energy dissipation property for the first-order scheme. In particular, the temporally first-order scheme unconditionally preserves positivity, mass conservation, and energy dissipation, whereas the second-order scheme ensures positivity under a sufficient (but not necessary) time-step condition. The proposed methods yield more accurate and efficient simulations of chemotactic dynamics, especially in the presence of rapid blow-up phenomena, on specified non-uniform spatial grids. Numerical experiments are conducted to validate the theoretical findings and to illustrate the accuracy and reliability of the proposed schemes.

math.NA↗

arXiv2026

Beyond Knowledge to Agency: Evaluating Expertise, Autonomy, and Integrity in Finance with CNFinBench

As large language models (LLMs) become high-privilege agents in risk-sensitive settings, they introduce systemic threats beyond hallucination, where minor compliance errors can cause critical data leaks. However, existing benchmarks focus on rule-based QA, lacking agentic execution modeling, overlooking compliance drift in adversarial interactions, and relying on binary safety metrics that fail to capture behavioral degradation. To bridge these gaps, we present CNFinBench, a comprehensive benchmark spanning 29 subtasks grounded in the triad of expertise, autonomy, and integrity. It assesses domain-specific capabilities through certified regulatory corpora and professional financial tasks, reconstructs end-to-end agent workflows from requirement parsing to tool verification, and simulates multi-turn adversarial attacks that induce behavioral compliance drift. To quantify safety degradation, we introduce the Harmful Instruction Compliance Score (HICS), a multi-dimensional safety metric that integrates risk-type-specific deductions, multi-turn consistency tracking, and severity-adjusted penalty scaling based on fine-grained violation triggers. Evaluations over 22 open-/closed-source models reveal: LLMs perform well in applied tasks yet lack robust rule understanding, suffer a 15.4 decline from single modules to full execution chains, and collapse rapidly in multi-turn attacks, with average violations surging by 159.05\% in Round 2. CNFinBench is available at https://cnfinbench.opencompass.org.cn and https://github.com/open-compass/CNFinBench.

cs.CE↗

arXiv2026

WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents

Large language model (LLM) agents are increasingly capable of tool use, code execution, artifact inspection, and iterative revision, creating new opportunities for automating scientific and engineering research. To the best of our knowledge, this paper presents the first end-to-end autoresearch framework for the wireless domain, with a focus on wireless resource allocation optimization. We propose the Wireless AutoResearch Agent (WARA), a closed-loop multi-agent system for automated wireless optimization research. Given only an initial topic, WARA decomposes the workflow into three phases: research gap identification and problem proposal, wireless optimization modeling, algorithm design and experimentation, and research deliverable construction. Across these phases, WARA uses artifact-mediated control: upstream artifacts are consumed as inputs, structured outputs are stored for downstream use, and controller-managed gates validate consistency among models, algorithms, experiments, and claims. When validation fails, WARA repairs only the responsible artifact instead of restarting the whole workflow. We present a representative wireless resource allocation case study showing how WARA converts an initial topic into a complete research package with executable evidence and a synthesized technical manuscript. We further design a structured LLM-based ScoringAgent to evaluate manuscript-level research validity and optimization research maturity. Comparative results show that WARA substantially outperforms one-shot LLM generation and approaches the quality profile of recently accepted peer-reviewed technical papers. These results indicate that closed-loop artifact control is a promising path toward end-to-end LLM-assisted wireless optimization research. The source code is available at https://github.com/guoyuan-dotcom/WARA_CUHKSZ.

cs.NI↗

arXiv2026

Solving the Yamabe Problem by an Iterative Method on a Small Riemannian Domain

We introduce an iterative scheme to solve the Yamabe equation $ - aΔ_{g} u + S u = λu^{p-1} $ on small domains $(Ω,g)\subset {\mathbb R}^n$ equipped with a Riemannian metric $g$. Thus $g$ admits a conformal change to a constant scalar curvature metric. The proof does not use the traditional functional minimization.

math.DG↗

arXiv2026

The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook

Latent space is rapidly emerging as a native substrate for language-based models. While modern systems are still commonly understood through explicit token-level generation, an increasing body of work shows that many critical internal processes are more naturally carried out in continuous latent space than in human-readable verbal traces. This shift is driven by the structural limitations of explicit-space computation, including linguistic redundancy, discretization bottlenecks, sequential inefficiency, and semantic loss. This survey aims to provide a unified and up-to-date landscape of latent space in language-based models. We organize the survey into five sequential perspectives: Foundation, Evolution, Mechanism, Ability, and Outlook. We begin by delineating the scope of latent space, distinguishing it from explicit or verbal space and from the latent spaces commonly studied in generative visual models. We then trace the field's evolution from early exploratory efforts to the current large-scale expansion. To organize the technical landscape, we examine existing work through the complementary lenses of mechanism and ability. From the perspective of Mechanism, we identify four major lines of development: Architecture, Representation, Computation, and Optimization. From the perspective of Ability, we show how latent space supports a broad capability spectrum spanning Reasoning, Planning, Modeling, Perception, Memory, Collaboration, and Embodiment. Beyond consolidation, we discuss the key open challenges, and outline promising directions for future research. We hope this survey serves not only as a reference for existing work, but also as a foundation for understanding latent space as a general computational and systems paradigm for next-generation intelligence.

cs.AI↗

arXiv2026

From Ground to Sky: Architectures, Applications, and Challenges Shaping Low-Altitude Wireless Networks

In this article, we introduce a novel low-altitude wireless network (LAWN), which is a reconfigurable, three-dimensional (3D) layered architecture. In particular, the LAWN integrates connectivity, sensing, control, and computing across aerial and terrestrial nodes that enable seamless operation in complex, dynamic, and mission-critical environments. Different from the conventional aerial communication systems, LAWN's distinctive feature is its tight integration of functional planes in which multiple functionalities continually reshape themselves to operate safely and efficiently in the low-altitude sky. With the LAWN, we discuss several enabling technologies, such as integrated sensing and communication (ISAC), semantic communication, and fully-actuated control systems. Finally, we identify potential applications and key cross-layer challenges. This article offers a comprehensive roadmap for future research and development in the low-altitude airspace.

eess.SP↗

arXiv2026

Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes

Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level. We reformulate kidney CT characterization as a per-lesion set-prediction task: one model emits a variable number of lesions per kidney, each with four clinical attributes. We curated 2,619 CT volumes from 788 patients at one academic medical center, with multi-granularity side- and per-lesion labels, and used KiTS23 (489 cases) for zero-shot external validation. We propose \textbf{LesionDETR}, a DETR-style architecture with size-distance Hungarian matching and a hierarchical loss that aggregates per-slot outputs to side-level objectives. Across four input representations and six encoder initializations, two design choices dominate: a segmentation mask as an input channel, and same-domain abdominal pretraining (SuPreM); generic large-corpus pretraining is no better than random initialization. LesionDETR reaches bilateral side-level abnormality AUC $0.799 \pm 0.009$ on UF-Health and $0.817 \pm 0.072$ on KiTS23. A count-conditioned variant reaches per-lesion mAP $0.190 \pm 0.083$ on cystic lesions; rare solid-lesion AP stays at the noise floor, pointing to targeted data collection, not architecture, as the next bottleneck. The framework yields verified per-lesion predictions for downstream structured report generation.

cs.CV↗

arXiv2026

Microwave Linear Analog Computers Aided Multiuser Communication: General Impedance Matching and Precoding Optimization

Microwave linear analog computers (MiLACs) have recently emerged as a hardware-efficient solution for implementing multi-antenna communication systems. Unlike existing MiLAC designs based on the ideal assumption of perfect impedance matching (PIM) with reflection-free transmission, this paper investigates MiLAC-aided precoding optimization under a general impedance matching (GIM) model, which enables more flexible precoder design at the cost of a potential reduction in radiated power. Specifically, we consider a downlink multiuser multiple-input single-output (MISO) communication system and aim to maximize the system sum rate by optimizing the MiLAC-enabled transmit precoding subject to physical circuit constraints. The formulated problem is challenging to solve due to the intricate coupling between the precoding and impedance parameters. To address this challenge, we first develop a singular value decomposition (SVD)-based parametric search framework for small or medium size systems. This framework exploits the feasible precoder structure and explicitly captures the tradeoff between power radiation efficiency and precoder design flexibility. We then propose a unified algorithm for solving the optimization problem based on the projected weighted minimum mean-square error (WMMSE) principle for arbitrary size systems with GIM- or PIM-based MiLAC precoding. Simulation results demonstrate that the GIM-based MiLAC design consistently outperforms its PIM counterpart as a special realization, especially in interference-limited scenarios, by allowing a moderate reduction in radiated power in exchange for additional precoder design flexibility and more effective interference mitigation. It is also shown that GIM-based MiLAC design achieves performance close to that of the baseline fully digital precoding system.

eess.SP↗

arXiv2026

Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee

Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine attacks. Existing robust approaches can neutralize these threats but incur substantial computational overhead during high-dimensional gradient aggregation, an overhead that scales poorly with model size and increasingly dominates the training cost as modern models grow larger. To address this computational bottleneck, we propose Projected Dimensionality Reduction (PDR), a universal acceleration framework for vector-level distance-based robust aggregators, which performs robust aggregation by compressing gradients into a drastically smaller subspace via sparse random projection to efficiently compute reliability weights. This approach reduces the server computational complexity to an optimal $ \mathcal{O}(Mp) $, where $ M $ is the number of clients and $ p $ is the model dimension, matching the theoretical lower bound required merely to read the gradients. We establish convergence guarantees under standard FL assumptions in prior Byzantine-robust FL analyses. By leveraging the Subspace Embedding Theorem, we show that PDR achieves optimal convergence rates of $ \mathcal{O}(1/\sqrt{T}) $ for non-convex functions and $ \mathcal{O}(1/T) $ for strongly convex functions, where $ T $ denotes the number of iterations. Crucially, we mathematically demonstrate that this massive acceleration comes almost for free, merely inflating the inherent Byzantine error floor by a bounded, tunable factor of $ \frac{1+ε}{1-ε} $. Experimental results on benchmark datasets confirm that integrating PDR with existing aggregators yields orders of magnitude speedups in time efficiency while maintaining highly competitive convergence performance.

cs.LG↗

arXiv2026

Tensor gradient flow with quasi-entropy for smectic liquid crystals and discretizations keeping coupled physical constraints

A gradient flow for the concentration and a $2\times 2$ tensor is constructed to describe smectic liquid crystals. The free energy consists of the entropy term and interaction term involving squared second order spatial derivatives. The entropy term incorporates the concentration in the quasi-entropy originally proposed for the tensor only, which is a strictly convex and lower semicontinuous function imposing coupled constraints between the concentration and the tensor. An evolution equation for the boundary normal derivative of the concentration is proposed in addition to the equations for the concentration and the tensor, giving an energy dissipation system. Numerical schemes are designed with emphases on using the entropy term to keep the coupled constraints, and the discretization of the boundary normal derivatives satisfying summation by parts. Existence, uniqueness, energy dissipation and error estimates are established. Numerical results indicate the efficiency and robustness of the scheme. Configurations of defects different from other layer structures are observed.

math.NA↗