arXiv subjects

Ruotong Zhao

Publications and source records attributed to Ruotong Zhao.

11 recordsLinked to original sources

arXiv2026

Pinching Antenna-Assisted ISAC with Waveguide Mode Selection

Conventional pinching antenna (PA)-assisted integrated sensing and communication (ISAC) architectures typically assume static receiver locations or predetermined receive waveguides, thereby underutilizing the inherent spatial degrees of freedom. This paper proposes a novel mode-selectable PA-assisted ISAC framework to maximize the post-combining sensing signal-to-noise ratio while satisfying multi-user quality-of-service constraints by jointly optimizing the waveguide mode selection, transmit beamforming, and transmit/receive PA positions. To tackle the resulting mixed-integer nonconvex optimization problem, we develop a low-complexity block-coordinate descent algorithm that leverages a penalty-based majorization-minimization method to achieve high-quality suboptimal solutions. Numerical results demonstrate that the proposed design significantly outperforms both traditional PA and fixed-antenna benchmarks by synergistically harnessing spatial adaptability and modal reconfigurability. In particular, the mode-selectable design enables the coordinated optimization of transmit/receive operations and sensing-communication resource allocation, thereby maintaining sensing robustness under stringent communication requirements.

eess.SP↗

arXiv2026

Energy-Efficient Resource Allocation for Six-Dimensional Movable Antenna Systems

This paper investigates the energy-efficiency (EE) maximization problem for a multiuser wireless network equipped with six-dimensional movable antennas (6DMAs), where the three-dimensional (3D) positions and orientations of the antennas are jointly optimized to fully exploit the additional spatial degrees of freedom offered by dynamic channel reconfiguration. However, the practical operation of 6DMAs incurs non-negligible mechanical energy consumption. Moreover, orientation-dependent phase variations, together with the strong coupling among antenna positions, rotation angles, transmit beamforming, and time allocation, render the resulting problem highly non-convex and analytically challenging. To address this issue, we develop a block coordinate descent (BCD) optimization framework that integrates Dinkelbach's transformation with the majorization-minimization (MM) approach to efficiently obtain a high quality suboptimal solution with guaranteed convergence. Simulation results unveil that the proposed design achieves significant EE improvements over conventional benchmarks, thereby highlighting the critical importance of accounting for practical mechanical energy costs in 6DMA enabled systems. Furthermore, our results reveal a fundamental trade-off between throughput enhancement and mechanical overhead: although larger antenna reconfigurations can improve channel conditions, their EE gains gradually diminish due to the increased mechanical energy consumption.

eess.SP↗

arXiv2026

LitReview Arena: Evaluating Literature Review Agents with Battle-Style Peer Review Platform

Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspects of research utility depend on expert judgment rather than reference-overlap metrics. We introduce LitReview Arena, a battle-style evaluation platform with a structured protocol tailored to literature review quality: domain experts with AI paper-writing experience compare anonymized drafts, are matched to topics within their expertise, and provide dimension-wise outcomes over five literature-review-specific criteria. From this protocol, we collect approximately 3k expert judgments, each containing five dimension-wise outcomes, and show that even the strongest current systems win only 23.0% of decisive matches against human drafts on overall utility, while agentic LLMs such as Sonar Deep Research substantially outperform base language models by over 60%. We further find that existing LLM-as-a-judge methods are substantially misaligned with human experts (Spearman's rho=0.467), especially on synthesis-heavy criteria such as paper structure and research suggestions. Using the collected preference data, we provide an expert-calibrated evaluator, LitJudge, which improves alignment to Spearman's rho=0.78, comparable to inter-expert consistency; code and data are publicly available at https://github.com/VanellopeAsher/LitReview-Arena.

cs.AI↗

arXiv2026

AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery

Artificial intelligence research increasingly depends on prolonged cycles of reproduction, debugging, and iterative refinement to achieve State-Of-The-Art (SOTA) performance, creating a growing need for systems that can accelerate the full pipeline of empirical model optimization. In this work, we introduce AutoSOTA, an end-to-end automated research system that advances the latest SOTA models published in top-tier AI papers to reproducible and empirically improved new SOTA models. We formulate this problem through three tightly coupled stages: resource preparation and goal setting; experiment evaluation; and reflection and ideation. To tackle this problem, AutoSOTA adopts a multi-agent architecture with eight specialized agents that collaboratively ground papers to code and dependencies, initialize and repair execution environments, track long-horizon experiments, generate and schedule optimization ideas, and supervise validity to avoid spurious gains. We evaluate AutoSOTA on recent research papers collected from eight top-tier AI conferences under filters for code availability and execution cost. Across these papers, AutoSOTA achieves strong end-to-end performance in both automated replication and subsequent optimization. Specifically, it successfully discovers 105 new SOTA models that surpass the original reported methods, averaging approximately five hours per paper. Case studies spanning LLM, NLP, computer vision, time series, and optimization further show that the system can move beyond routine hyperparameter tuning to identify architectural innovation, algorithmic redesigns, and workflow-level improvements. These results suggest that end-to-end research automation can serve not only as a performance optimizer, but also as a new form of research infrastructure that reduces repetitive experimental burden and helps redirect human attention toward higher-level scientific creativity.

cs.CL↗

arXiv2026

Impact of chirality imbalance and nonlocal interactions on the QCD biased axionic domainwall interpretation of NANOGrav 15 year data

We investigate the influence of the chirality imbalance with local CP-breaking in hot QCD on the generation of a stochastic gravitational wave background (SGWB) sourced by the axion-like particle (ALP) domain-wall annihilation, induced by the QCD bias. Such a bias is quantified by the QCD topological susceptibility $\chi_t$, and its dependence on the $\theta$ angle and the chiral chemical potential $\mu_5$ is investigated at temperatures near the QCD scale within a nonlocal Nambu-Jona-Lasinio (NJL) model. We find that, besides the small-$\theta$ range, the axionic domain-wall interpretation of NANOGrav 15-year data on the nHz gravitational waves is still possible for a certain large-$\theta$ range if $\mu_5$ is large enough. We confirm that the peak of $|\chi_t|$ at the critical temperature $T_c$ for the CP restoration at $\theta=\pi$ exhibits a pronounced width compared to the local NJL result. Thus, for $\theta$ at and around $\pi$, the QCD bias near $T_c$ can also produce a GW signal strength compatible with the NANOGrav 15-year data.

hep-ph↗

arXiv2026

Robust and Secure Blockage-Aware Pinching Antenna-assisted Wireless Communication

In this work, we investigate a blockage-aware pinching antenna (PA) system designed for secure and robust wireless communication. The considered system comprises a base station equipped with multiple waveguides, each hosting multiple PAs, and serves multiple single-antenna legitimate users in the presence of multi-antenna eavesdroppers under imperfect channel state information (CSI). To safeguard confidential transmissions, artificial noise (AN) is deliberately injected to degrade the eavesdropping channels. Recognizing that conventional linear CSI error bounds become overly conservative for spatially distributed PA architectures, we develop new geometry aware uncertainty sets that jointly characterize eavesdropper position and array-orientation errors. Building upon these sets, we formulate a robust joint optimization problem that determines per waveguide beamforming and AN covariance, individual PA power ratio allocation, and PA positions to maximize the system sum rate subject to secrecy constraints. The highly nonconvex design problem is efficiently addressed via a low computational complexity iterative algorithm that capitalizes on block coordinate descent, penalty based methods, majorization minimization, the S procedure, and Lipschitz based surrogate functions. Simulation results demonstrate that the sum rate achieved by the proposed algorithm outperforms conventional fixed-antenna systems by 4.7 dB, offering substantially improved rate and secrecy performance. In particular, (i) adaptive PA positioning preserves LoS to legitimate users while effectively exploiting waveguide geometry to disrupt eavesdropper channels, and (ii) neglecting blockage effects in the PA system significantly impacts the system design, leading to performance degradation and inadequate secrecy guarantees.

cs.IT↗

arXiv2025

OmniScientist: Toward a Co-evolving Ecosystem of Human and AI Scientists

With the rapid development of Large Language Models (LLMs), AI agents have demonstrated increasing proficiency in scientific tasks, ranging from hypothesis generation and experimental design to manuscript writing. Such agent systems are commonly referred to as "AI Scientists." However, existing AI Scientists predominantly formulate scientific discovery as a standalone search or optimization problem, overlooking the fact that scientific research is inherently a social and collaborative endeavor. Real-world science relies on a complex scientific infrastructure composed of collaborative mechanisms, contribution attribution, peer review, and structured scientific knowledge networks. Due to the lack of modeling for these critical dimensions, current systems struggle to establish a genuine research ecosystem or interact deeply with the human scientific community. To bridge this gap, we introduce OmniScientist, a framework that explicitly encodes the underlying mechanisms of human research into the AI scientific workflow. OmniScientist not only achieves end-to-end automation across data foundation, literature review, research ideation, experiment automation, scientific writing, and peer review, but also provides comprehensive infrastructural support by simulating the human scientific system, comprising: (1) a structured knowledge system built upon citation networks and conceptual correlations; (2) a collaborative research protocol (OSP), which enables seamless multi-agent collaboration and human researcher participation; and (3) an open evaluation platform (ScienceArena) based on blind pairwise user voting and Elo rankings. This infrastructure empowers agents to not only comprehend and leverage human knowledge systems but also to collaborate and co-evolve, fostering a sustainable and scalable innovation ecosystem.

cs.CY↗

arXiv2025

Resource Allocation for Multi-waveguide Pinching Antenna-assisted Broadcast Networks

In this paper, we investigate the resource allocation for multi-dielectric waveguide-assisted broadcast systems, where each waveguide employs multiple pinching antennas (PAs), aiming to maximize the minimum achievable rate among multiple users. To capture realistic propagation effects, we propose a novel generalized frequency-dependent power attenuation model for dielectric waveguides PA systems. We jointly optimize waveguide beamforming, PA power ratio allocation, and antenna positions via a block coordinate descent scheme that capitalizes on majorization minimization and penalty methods, circumventing the inherent non-convexity of the formulated optimization problem and obtaining a computationally efficient sub-optimal solution. Simulation results demonstrate that our proposed framework substantially outperforms both conventional antenna systems and single PA per waveguide configurations, clearly illustrating the intricate trade-offs between waveguide propagation loss, path loss, and resource allocation among multiple PAs.

cs.IT↗

arXiv2025

CrimeMind: Simulating Urban Crime with Multi-Modal LLM Agents

Modeling urban crime is an important yet challenging task that requires understanding the subtle visual, social, and cultural cues embedded in urban environments. Previous work has mainly focused on rule-based agent-based modeling (ABM) and deep learning methods. ABMs offer interpretability of internal mechanisms but exhibit limited predictive accuracy. In contrast, deep learning methods are often effective in prediction but are less interpretable and require extensive training data. Moreover, both lines of work lack the cognitive flexibility to adapt to changing environments. Leveraging the capabilities of large language models (LLMs), we propose CrimeMind, a novel LLM-driven ABM framework for simulating urban crime within a multi-modal urban context. A key innovation of our design is the integration of the Routine Activity Theory (RAT) into the agentic workflow of CrimeMind, enabling it to process rich multi-modal urban features and reason about criminal behavior. However, RAT requires LLM agents to infer subtle cues in evaluating environmental safety as part of assessing guardianship, which can be challenging for LLMs. To address this, we collect a small-scale human-annotated dataset and align CrimeMind's perception with human judgment via a training-free textual gradient method. Experiments across four major U.S. cities demonstrate that CrimeMind outperforms both traditional ABMs and deep learning baselines in crime hotspot prediction and spatial distribution accuracy, achieving up to a 24% improvement over the strongest baseline. Furthermore, we conduct counterfactual simulations of external incidents and policy interventions and it successfully captures the expected changes in crime patterns, demonstrating its ability to reflect counterfactual scenarios. Overall, CrimeMind enables fine-grained modeling of individual behaviors and facilitates evaluation of real-world interventions.

cs.AI↗

arXiv2025

Sum-Rate Maximization for Pinching Antenna-assisted NOMA Systems with Multiple Dielectric Waveguides

This paper investigates the resource allocation design for a pinching antenna (PA)-assisted multiuser multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system featuring multiple dielectric waveguides. To enhance model accuracy, we propose a novel frequency-dependent power attenuation model for the dielectric waveguides in PA-assisted systems. By jointly optimizing the precoder vector and the PA placement, we aim to maximize the system's sum-rate while accounting for the power attenuation across the dielectric waveguides. The design is formulated as a non-convex optimization problem. To effectively address the problem at hand, we introduce an alternating optimization-based algorithm to obtain a suboptimal solution in polynomial time. Our results demonstrate that the proposed PA-assisted system not only significantly outperforms the conventional system but also surpasses a naive PA-assisted system that disregards power attenuation. The performance gain compared to the naive PA-assisted system becomes more pronounced at high carrier frequencies, emphasizing the importance of considering power attenuation in system design.

eess.SP↗

arXiv2024

Securing V2I Backscattering from Eavesdropper

As our cities become more intelligent and more connected with new technologies like 6G, improving communication between vehicles and infrastructure is essential while reducing energy consumption. This study proposes a secure framework for vehicle-to-infrastructure (V2I) backscattering near an eavesdropping vehicle to maximize the sum secrecy rate of V2I backscatter communication over multiple coherence slots. This sustainable framework aims to jointly optimize the reflection coefficients at the backscattering vehicle, carrier emitter power, and artificial noise at the infrastructure, along with the target vehicle's linear trajectory in the presence of an eavesdropping vehicle in the parallel lane. To achieve this optimization, we separated the problem into three parts: backscattering coefficient, power allocation, and trajectory design problems. We respectively adopted parallel computing, fractional programming, and finding all the candidates for the global optimal solution to obtain the global optimal solution for these three problems. Our simulations verified the fast convergence of our alternating optimization algorithm and showed that our proposed secure V2I backscattering outperforms the existing benchmark by over 4.7 times in terms of secrecy rate for 50 slots. Overall, this fundamental research on V2I backscattering provided insights to improve vehicular communication's connectivity, efficiency, and security.

eess.SP↗