arXiv subjects

Xianbin Wang

Publications and source records attributed to Xianbin Wang.

At least 19 recordsLinked to original sources

arXiv2026

Movable Antenna-Assisted Secret Key Generation over Temporally Correlated Channels

Physical-layer key generation (PLKG) exploits reciprocal channel dynamics to generate symmetric keys, addressing the key distribution problem in wireless communications. However, strong temporal correlation in slow-fading environments limits fresh randomness and results in low key generation rates. Movable antennas (MAs) introduce spatial degrees of freedom through antenna repositioning, providing a new approach to addressing the limitation. This paper investigates MA-assisted PLKG over temporally correlated channels. A first-order Gauss-Markov process is utilized to characterize the temporal correlation of multipath gains. We characterize the full-sequence mutual information (MI) over temporally correlated channels. For fixed antenna positions and beamforming, we establish a finite-horizon approximation error bound that vanishes as both effective signal-to-noise ratios (SNRs) increase. We derive a closed-form expression for the one-step conditional MI and adopt it as a tractable key-information metric. A nonconvex problem is formulated to jointly optimize antenna positions and per-slot beamforming under transmit-power and minimum-spacing constraints. We develop an alternating optimization algorithm, combining an exact principal-eigenvector beamforming solution with feasible SCA-based projected gradient position updates. Simulation results demonstrate that the MA-assisted scheme outperforms the fixed-position antenna baseline. At a high temporal correlation coefficient, position optimization achieves approximately $51\%$ higher rates than fixed antennas. Joint position and beamforming optimization provides a further $106\%$ gain over position optimization alone, demonstrating more pronounced gains under strong temporal correlation.

cs.IT↗

arXiv2026

JABER: Sequential Quality Control for Human-AI Data Annotation under Shared Bias

High-quality annotations are essential for training and evaluating artificial intelligence (AI) systems. Quality control (QC) often acquires additional human or AI judgments before accepting a label. Nevertheless, shared biases can cause correlated judgments, leading conventional methods to mistake repeated agreement for independent evidence of correctness. To address this challenge, we propose JABER (Joint Alias-Preserving Belief for Evidence Routing), a bias-aware sequential QC framework that maintains a joint Bayesian posterior over label correctness and shared biases to guide evidence acquisition and termination decisions. Building on this posterior, we develop a finite-horizon algorithm that selects evidence-acquisition and termination actions to minimize expected QC loss under resource constraints. Our theoretical analysis shows that conditional-independence updating can become arbitrarily confident in an incorrect label under persistent shared bias. We further identify when repeated inspections cannot distinguish a correct label from an incorrect label supported by shared bias, when obtaining a new label can resolve this ambiguity, and why modeling the dependence between label correctness and shared bias can be necessary for QC decisions. Our experiments show that JABER reduces expected QC loss by an average of 4.65% compared with a method maintaining separate label correctness and bias probabilities when shared bias makes repeated inspections unreliable. Further, JABER's finite-horizon planning, which evaluates outcomes of successive QC actions, reduces expected QC loss by an average of 10.05% compared with a one-step lookahead method. In real-API experiments on FEVER, JABER achieves zero labeling errors while reducing QC costs by 2.67% and 16.99% compared with marginal-based belief modeling and one-step lookahead methods, respectively.

cs.AI↗

arXiv2026

STEPS: Semantic Contract-Guided Scheduling for LLM-Assisted Natural Language-Driven Edge AI Services

Edge user/service scheduling governs where and how AI services are executed under limited communication and computing resources. Existing edge scheduling frameworks typically assume numerically specified service requirements, such as latency bounds and energy budgets. However, users often express ambiguous and context-dependent expectations in natural language, creating a gap between user intent and scheduling decisions. We propose semantic contract-guided edge potential scheduling (STEPS), a natural language-driven framework that introduces semantic contracts as executable interfaces between user-side semantics and edge-side decisions. A large language model (LLM)-assisted semantic parser extracts service requirements and raw confidence scores from natural language requests. Confidence calibration and interpretable mapping rules convert these outputs into preference weights, fulfillment bounds, and semantic uncertainty. STEPS formulates intended per-slot edge scheduling as an exact potential game for execution-node selection, computing-resource provisioning, and bandwidth allocation, followed by capacity-feasible resource realization. Fulfillment-driven feedback signals enable adaptive scheduling under evolving service and network conditions. We establish the existence of pure-strategy Nash equilibrium, prove finite-step convergence of uncapped strict best-response dynamics, and characterize the boundedness of projected adaptive-control parameters. Experiments under synthetic and Melbourne-based hybrid settings demonstrate improved contract fulfillment and reduced service loss relative to several adapted benchmarks. Post-drift evaluations further show that feedback adaptation improves fulfillment and admission coverage under the tested conditions.

cs.NI↗

arXiv2026

Concurrent Task Orchestration: Hierarchical Trusted Collaborator Group Formation for Constrained Resource Allocation

Effective collaboration among resource-constrained mobile intelligent machines, such as Unmanned Aerial Vehicles (UAVs), is essential for the completion of complex tasks. However, concurrent task demands, limited distributed resources, and dynamic collaborator availabilities make it challenging to satisfy task-specific completion goals in diverse collaborative systems. To address these challenges simultaneously, we propose a new framework of trusted collaborator group formation and the supporting resource allocation strategy for concurrent task orchestration. Specifically, a collaborative task is decomposed into subtasks subject to dynamic resource availability and inter-subtask dependencies. A task utility model is developed for collaborator group evaluation support subtask allocation according to task completion goals and unified value-risk analysis. Based on this model, concurrent task orchestration is formulated as a long-term utility maximization problem under $α$-fairness. To solve the problem efficiently, we develop a hierarchical matching strategy that first clusters tasks and collaborators into coarse-grained sets and subsequently performs fine-grained collaborator group formation and task allocation within matched sets. Task-specific trust is predicted through spatial-temporal Graph Neural Networks (GNNs) and embedded into a fairness-aware Deep Reinforcement Learning (DRL) framework to continuously update the matching policies. Simulation results demonstrate that the proposed strategy improves concurrent task completion performance by 22% over representative baselines under resource constraints.

cs.NI↗

arXiv2026

Efficient Perturbation-Based SC Decoding for Long Polar Codes

Soft information perturbations can significantly improve the successive cancellation (SC) decoding performance of polar codes. This is an effective method for realizing low-latency yet high performance for long polar codes, since each perturbation-based SC (PSC) decoding can be performed in parallel. However, little theoretical insight has been revealed so far regarding its work rationale. This paper provides the theoretical analysis of the soft information perturbation. It reveals that PSC decoding improves the SC decoding performance through correcting the erroneous bit estimations, especially the first erroneous bit (FEB). It further validates that perturbing the soft information does not degrade the error-correction capability of SC decoding. Such revelations lead to the design of optimal perturbations that maximizes the FEB correction probability, resulting in an enhanced PSC decoding performance. Based on this, a novel hybrid PSC (HPSC) decoding is proposed. By introducing the random and biased perturbations, the HPSC decoding can better divert from the initial erroneous estimation and converge to the intended one. Our simulation results demonstrate that with the optimal perturbation, the HPSC decoding can achieve a similar performance as the cyclic redundancy check (CRC) aided SC list (CA-SCL) decoding, while avoiding path sorting and expansion.

cs.IT↗

arXiv2026

ELSA: Efficient LLM-Centric Split Aggregation for Privacy-Aware Hierarchical Federated Learning over the Network Edge

Training large language models (LLMs) in resource-constrained edge networks poses fundamental challenges in computation, communication, data heterogeneity, and privacy. To address these challenges, we propose ELSA (Efficient LLM-Centric Split Aggregation), a distributed fine-tuning framework that integrates split learning (SL) with hierarchical federated learning (HFL) to coordinate computation and model aggregation across heterogeneous clients, edge servers, and the cloud. ELSA introduces three key innovations. First, it employs a task-agnostic, behavior-aware client clustering mechanism that constructs semantic fingerprints from public probe inputs using symmetric Kullback-Leibler (KL) divergence, complemented by prediction-consistency-based trust scoring and latency-aware edge assignment to account for data heterogeneity, client reliability, and communication constraints. Second, it adopts a resource-aware dynamic model partitioning strategy that distributes the LLM across clients and edge servers in three segments, while reserving the cloud for adapter aggregation. This design coordinates computation across heterogeneous nodes while balancing on-device computational demands and global convergence behavior. Third, it integrates computational sketches with semantic subspace orthogonal perturbation (SS-OP) to reduce communication overhead and mitigate privacy leakage during model exchanges. Extensive experiments across diverse NLP tasks demonstrate that ELSA outperforms state-of-the-art baselines in model performance, convergence behavior, and robustness. Further, on-device profiling on NVIDIA RTX 4090 GPUs and Jetson AGX Orin edge devices characterizes the memory footprint, energy consumption, and execution latency of key training stages, providing empirical evidence of ELSA's practical feasibility.

cs.LG↗

arXiv2026

Predictive Rolling-Horizon Optimization for Commitment-Aware Model-Parallel Inference under Spatio-Temporal Edge Dynamics

Model-parallel inference over dynamic edge systems requires scheduling decisions that account for not only instantaneous resources but also future resource contention and reliable service commitments. Existing edge-inference designs, however, predominantly optimize performance metrics based on current or short-term system states, without explicitly coupling current assignments with future commitment fulfillment. To address this issue, we propose PROMISE, a predictive rolling-horizon framework for commitment-aware model-parallel inference under spatio-temporal edge dynamics. PROMISE jointly models stochastic task generation, mobility-induced communication variations, privacy-aware model partitioning, and load-dependent edge computing capability. We introduce committed completion time (CCT) as an endogenous service decision and formulate joint SD--ES mapping and CCT determination to balance commitment fulfillment and service reward. To address cross-timeslot coupling, PROMISE estimates future task arrivals and computational workloads over an adaptive horizon and embeds them into certainty-equivalent ES-state rollout. A progress-aware surrogate then evaluates feasible current-stage mappings, while the corresponding CCTs are analytically recovered from predicted completion times. Only the first-stage decisions are committed, and the optimization is repeated with newly observed states in a receding-horizon manner. Numerical experiments demonstrate robust scheduling performance under diverse system scales and workload dynamics, while Raspberry-Pi-based experiments validate the practical feasibility and key system characteristics of model-parallel edge inference.

cs.DC↗

arXiv2026

FUSION: Forecast-Embedded Agent Scheduling with Service Incentive Optimization over Distributed Air-Ground Edge Networks

This paper introduces a forecasting-driven, incentive-aware service provisioning framework for distributed air--ground integrated networks with human--machine coexistence. Agent pairs (APs), each comprising a vehicle and its carried uncrewed aerial vehicles (UAVs), are proactively dispatched to overloaded hotspots to augment the computing capacity of edge servers (ESs). This design introduces four coupled challenges: uncertain spatio-temporal workloads, coupling between vehicular mobility and UAV capacity, forecast-driven contracting risks, and heterogeneous quality-of-service (QoS) requirements of human users (HUs) and machine users (MUs). To address these challenges, we propose FUSION, a two-stage framework with offline service preparation and online task scheduling. In the offline stage, a liquid neural network forecasts multi-step ES demand, an enhanced ant colony optimization scheme constructs AP service routes, and an auction-based mechanism establishes ES--AP contracts. In the online stage, we formulate congestion-aware scheduling as an exact-potential game among service demanders (SDs) and develop a potential-guided best-response dynamics algorithm. For a fixed online state, the algorithm converges to an $\varepsilon$-Nash equilibrium (NE) under a positive improvement threshold and to a pure-strategy NE when the threshold is zero. Within the considered contracting model, we theoretically establish that the offline mechanism satisfies individual rationality, near-truthfulness, and weak budget balance. Experiments on synthetic data and real-world load traces show that FUSION achieves higher social welfare while maintaining interaction delay and signaling energy overheads comparable to the considered benchmarks.

cs.NI↗

arXiv2026

QAOA-Based Pilot Assignment for Cell-Free Massive MIMO Systems

Pilot contamination is a major challenge in cell-free massive multiple-input multiple-output (MIMO) systems, where the limited number of orthogonal pilots makes pilot reuse unavoidable. Since the pilot assignment solution space grows exponentially with the number of users, finding the global optimum becomes computationally challenging on classical computers. Recent advances in quantum computing provide a promising approach for solving such large-scale combinatorial optimization problems. In this paper, we reformulate the assignment problem as a quantum-compatible optimization problem, enabling it to be directly solved by the Quantum Approximate Optimization Algorithm (QAOA). Specifically, the pilot contamination objective and assignment constraints are incorporated into the QAOA formulation. This allows the quantum search to focus on valid pilot assignments with low contamination cost. Simulation results show that the proposed method achieves performance close to exhaustive search, demonstrating the potential of quantum-assisted optimization for pilot assignment in future wireless systems.

quant-ph↗

arXiv2026

Long-Term Behavioral Evaluation for Trusted Collaborator Selection via Bidirectional Mamba

Effective selection of trustworthy collaborators is crucial to ensuring the successful completion of collaborative tasks, which requires accurate assessments of both long-term device behavior and short-term collaborative dynamics. Consistent device behavior patterns, which are learned from historical collaborations, can be used to predict their reliability in future collaborations. However, accurately assessing device behavior based on historical collaborations remains challenging. First, behavior assessment from limited historical collaborations captures only instantaneous past behavior, failing to represent the devices' true behavior. Second, due to the temporal dependencies of device behavior, a unidirectional evaluation that relies only on earlier collaborations loses the opportunity to learn from subsequent collaborations. Addressing these challenges requires evaluating device behavior based on long-term collaborations while considering both forward and backward temporal dependencies. To this end, this work proposes a bidirectional Mamba-enabled model (BM) for long-term behavioral evaluation. For each short time slot, a graph is constructed among devices based on historical collaborations, and device behavioral features within the slot are then aggregated accordingly. Subsequently, a bidirectional Mamba model integrates these short-term representations across all time intervals, producing a stable and reliable long-term behavior evaluation for each device. Experimental results demonstrate that BM achieves higher evaluation accuracy than baseline methods, thereby enabling the selection of collaborators that maximize the value of task completion.

cs.LG↗

arXiv2026

Multi-View Trust Evaluation for Collaborator Selection via Evidential Deep Learning

Selection of trustworthy collaborators in distributed systems is critical for efficient task completion, necessitating the inference of trustworthiness from their past collaboration experience. However, as a collaborator serves distinct devices across diverse scenarios in past collaborations, its trust-related data, observed from different device-specific views, is inherently multi-source, heterogeneous, and uneven in quality. Consequently, achieving accurate trust evaluations for collaborator selection remains a major challenge. To tackle these issues, we propose a novel multi-view evidential learning (MVE) based trust evaluation method. First, to accommodate the multi-source heterogeneity of observed trust-related data, we model each task owner who has interacted with a potential collaborator as an independent observational view, enabling the evaluation of the collaborator's view-specific trust. Second, to address the dynamic evolution of trust under changing conditions, we leverage the powerful long-sequence modeling capability of the Mamba model to capture the deep temporal patterns of a collaborator's trust state within each view. Furthermore, to quantify the certainty levels of view-specific trust assessments, we incorporate an evidential deep learning mechanism in MVE, which outputs trust evaluation results while quantifying the subjective uncertainty underlying them. Finally, we employ a dynamic evidential fusion strategy to adaptively integrate the multi-view evidence based on their respective quantified uncertainties, thereby yielding a final trust evaluation for the collaborator. Extensive experiments demonstrate that the proposed MVE method outperforms baselines in both trust evaluation accuracy and task success rate.

eess.SY↗

arXiv2026

TrustFormer: Cross-Temporal and Cross- Dimensional Transformer for Task-Specific Multi-Dimensional Trust Evaluation

In dynamic collaborative systems, the selection of reliable collaborators is critical to ensuring effective task execution. Existing trust evaluation methods often rely on unidimensional or scalar representations, which fail to faithfully capture a collaborator's true trustworthiness, thereby motivating a shift toward multi-dimensional trust modeling. However, due to the asynchrony of collected trust-related data across different dimensions, as well as the complex intra- and inter-dimensional dependencies embedded within these data, multi-dimensional trust evaluation remains challenging. To address these challenges, we propose TrustFormer, a task-specific multi-dimensional trust evaluation framework. Specifically, TrustFormer leverages task identifiers and device-generated timestamps to synchronize heterogeneous trust-related data across historical collaborations. It further employs cross-temporal and cross-dimensional attention mechanisms to jointly model temporal dynamics and inter-dimensional correlations, thereby effectively learning the multi-dimensional trust evolution of potential collaborators from historical performance data. In addition, according to the multi-dimensional resource requirements of tasks, potential collaborators' multi-dimensional resource trust is evaluated. Finally, by synthesizing these multi-dimensional trust profiles, the framework enables the optimal collaborator selection. Experimental results demonstrate that TrustFormer outperforms existing methods by yielding a 40.8% improvement in trust evaluation accuracy and enabling more reliable collaborator selection.

eess.SY↗

arXiv2026

Certifiable Semantic Agreement Among LLM Agents: What the Admissibility Instrument Decides

Can a committee of LLM agents reach agreement that is certifiable at the level of meaning, not only at the level of a label? We build a protocol to find out. H-CSC emits one of three typed outcomes per round -- semantic commit, verdict commit, or typed abort -- under a common 2f+1 distinct-signer certificate, and we use it to measure what such agreement costs and buys. The answer is conditional, and the condition is not the protocol. We prove a containment lemma: whenever the semantic core is large enough to make the committed verdict deterministically valid, the verdict margin already exceeds f, so at matched deterministic guarantees no coverage separation from certificate-wrapped majority is possible. Measurement agrees: the two rules commit the identical task set. What matters instead is the admissibility instrument. Against adversaries that preserve the verdict and corrupt only the reasoning, a 442 MB fine-tuned encoder reaches AUROC 0.621-0.744 at 0-8% TPR (5% honest FPR), while a training-free lexical predicate reaches 0.865-0.982 at 38-80% (50 tasks, 200 attacks, 400 honest) and is exactly deterministic, discharging an assumption the digest proofs rely on. Honest agents disperse further than attacks displace (90th-percentile honest angular distance 0.992 rad against a 0.65 radius), so on free-text rationales no embedding filter is viable -- a property of the output schema, which when tightened collapses dispersion twenty-fold. The committed digest is not inert: under non-disclosure a similarity-preserving sketch separates faithful from substituted derived statements at AUROC 0.741 (n=350), where a cryptographic hash and a verdict-only digest score exactly 0.500. We also report a tie-break capture vulnerability found in our own protocol, and its four-line fix. We claim no safety or coverage advantage over verdict-only certification; the lemma explains why there is none to claim.

cs.MA↗

arXiv2026

Flying over The Uncertain Nature (FORTUNE): Intelligent and Humanistic 3D Path Planning for Low-Altitude Collaboration

The proliferation of low-altitude intelligent agents is increasing the demand for timely and socially responsible collaborative sensing in dynamic urban environments. However, jointly addressing heterogeneous spatiotemporal demands, environmental uncertainty, and human-centered operational constraints remains challenging. This paper studies 3D multi-UAV path planning and task assignment under uncertain ground PoI demands. Unlike existing work assuming static and fully known PoIs, we model persistent, temporally predictable, and emergent demands within a unified framework. We further incorporate altitude-dependent societal and environmental costs, including noise exposure and public safety risks, to balance sensing performance with socially compliant operations. To solve the resulting large-scale mixed-integer nonlinear problem, we propose FORTUNE, a hierarchical offline-online framework. Offline, a Transformer predicts Type-II PoI activation windows, while an enhanced sparrow search algorithm generates coordinated flight plans through priority-aware decoding and danger-aware evolution. Online, a lightweight refinement module accommodates emerging Type-III PoIs while preserving global mission coherence. Experiments on real-world traffic data and synthetic scenarios show that FORTUNE consistently outperforms state-of-the-art methods in effectiveness, scalability, and practical applicability.

cs.RO↗

arXiv2026

Spatiotemporal Feature Alignment and Weighted Fusion in Collaborative Perception Enabled by Network Synchronization and Age of Information

Collaborative perception in Internet of Vehicles (IoV) aggregates multi-vehicle observations for broader scene coverage and improved decision-making. However, fusion quality degrades under spatiotemporal heterogeneity from unsynchronized clocks, communication delays, and motion variations across vehicles. Prior work mitigates these through spatial transformations or fixed time-offset corrections, overlooking time-varying clock drifts and delays that cause persistent feature misalignment. To address these challenges, we propose a spatiotemporal feature alignment and weighted fusion framework. Specifically, network synchronization is introduced to estimate inter-vehicle clock states and establish a common temporal reference, onto which local feature timestamps can be mapped. Based on this, we define delivery-time Age of Information (AoI) to measure the expected age of a shared feature when it becomes available for fusion, by accounting for its generation time and the Vehicle-to-Everything (V2X) communication delay. The proposed spatiotemporal feature alignment then compensates asynchronous neighbor features toward the fusion time, rather than directly aggregating delayed features. Since different spatial regions contribute unequally to perception, we further perform Region-of-Interest (RoI)-level weighted fusion, where the fusion weights are determined by delivery-time AoI, synchronization reliability, and content complementarity. As a result, timely, reliable, and complementary regions are emphasized, while stale, uncertain, or redundant regions are down-weighted. Simulation results further demonstrate consistent accuracy improvements over representative baselines under clock drift, varying communication conditions, temporal misalignment levels, and vehicle numbers.

cs.NI↗

arXiv2026

SAGE: A Socially-Aware Generative Engine for Heterogeneous Multi-Agent Navigation

Safe and socially compliant navigation in open human-robot environments requires robots to reason about heterogeneous participants with different dynamics, autonomy levels, and social roles. Existing trajectory prediction and planning methods often rely on homogeneous interaction assumptions or enforce only geometric collision constraints, making it difficult to jointly model asymmetric interactions, coupled prediction-planning, and soft social norms. This paper proposes SAGE, a socially-aware generative engine for heterogeneous multi-agent navigation. SAGE represents robots and surrounding entities as a directed heterogeneous graph and employs a Heterogeneous Graph Transformer (HGT) to encode type-specific asymmetric interactions. Conditioned on the resulting context, a diffusion-based generative module jointly models future entity trajectories and robot trajectory plans. During inference, a training-free safety-social energy guidance mechanism refines sampled robot trajectories using differentiable collision, kinematic, task-progress, and role-conditioned social-compliance terms. Extensive experiments on real-world (ETH/UCY and SDD) and synthetic datasets verify the effectiveness of SAGE in improving safety and social compliance while maintaining task performance. The proposed guidance mechanism consistently reduces collision and social-violation rates, scales to teams of up to 20 robots, and enables explicit control of the safety-accuracy-task trade-off without retraining. These findings demonstrate the potential of SAGE as a scalable framework for socially-aware multi-agent navigation in complex environments.

cs.RO↗

arXiv2026

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation

Future intelligent transportation systems are envisioned to evolve toward a long-term mixed-autonomy paradigm, where human-driven vehicles (HVs) and autonomous vehicles (AVs) coexist within highly coupled traffic ecosystems. Such coexistence introduces pronounced heterogeneity, amplified uncertainty, and increasingly intricate interaction dynamics. In this context, it remains fundamentally challenging to simultaneously capture the heterogeneous behavioral distribution shifts arising from dynamic AV penetration, generate diverse yet executable trajectories under strong inter-vehicle coupling, and conduct reliable closed-loop safety and stability diagnostics for rare but high-impact events. To this end, we present Diffusion with Risk constraints, Imitation priors, and long-tail Feedback for mixed-autonomy Traffic generation (DRIFT), a mixed-autonomy traffic generation framework that unifies heterogeneity-aware conditional encoding, conditional diffusion-based executable trajectory generation, and progressive adversarial alignment enhanced by risk-aware long-tail feedback, thereby enabling traffic behaviors to be iteratively generated, filtered, selected, and validated within a closed-loop execution pipeline. In addition, a unified evaluation protocol is developed to jointly characterize safety, efficiency, and closed-loop stability across representative traffic scenarios and AV penetration regimes. Experimental results demonstrate that DRIFT achieves a strong safety-efficiency trade-off in closed-loop mixed-autonomy benchmarks, while further revealing the critical influence of candidate executability, online selection, and long-tail feedback on executable traffic evolution.

cs.DC↗

arXiv2026

SLM, LLM or Agentic AI? Toward Intelligent UAV-Enabled WPT Systems in Low-Altitude Economy Networks

Unmanned Aerial Vehicles (UAVs) have become key enabling platforms for low-altitude economic networks, yet achieving efficient and adaptive optimization under resource-constrained and dynamic environments remains challenging. This paper investigates language models for UAV-enabled Wireless Power Transfer (WPT) systems. First, a lightweight Small Language Model (SLM)-based solution is developed using a pre-trained BERT backbone, enhanced UAV embeddings and contextual features, a geometry-aware path decoder, and ensemble inference to achieve low complexity, low latency, and high energy efficiency. Second, an Agentic AI-based framework is designed to exploit the reasoning and interactive capabilities of Large Language Models (LLMs). It integrates four collaborative agents-Initializer, Actor, Critic, and Reflector-to form a closed loop of generation, optimization, evaluation, and reflection for iterative UAV path and energy optimization. Finally, simulations compare the SLM-, LLM-, and Agentic AI-based approaches.

cs.IT↗