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May 30, 2025
Large language models (LLMs) trained via reinforcement learning with verifiable reward (RLVR) have achieved breakthroughs on tasks with explicit, automatable verification, such as software programming and mathematical problems. Extending RLVR to electronic design automation (EDA), especially automatically generating hardware description languages (HDLs) like Verilog from natural-language (NL) specifications, however, poses three key challenges: the lack of automated and accurate verification environments, the scarcity of high-quality NL-code pairs, and the prohibitive computation cost of RLVR. To this end, we introduce CodeV-R1, an RLVR framework for training Verilog generation LLMs. First, we develop a rule-based testbench generator that performs robust equivalence checking against golden references. Second, we propose a round-trip data synthesis method that pairs open-source Verilog snippets with LLM-generated NL descriptions, verifies code-NL-code consistency via the generated testbench, and filters out inequivalent examples to yield a high-quality dataset. Third, we employ a two-stage "distill-then-RL" training pipeline: distillation for the cold start of reasoning abilities, followed by adaptive DAPO, our novel RLVR algorithm that can reduce training cost by adaptively adjusting sampling rate. The resulting model, CodeV-R1-7B, achieves 68.6% and 72.9% pass@1 on VerilogEval v2 and RTLLM v1.1, respectively, surpassing prior state-of-the-art by 12~20%, while matching or even exceeding the performance of 671B DeepSeek-R1. We will release our model, training pipeline, and dataset to facilitate research in EDA and LLM communities.
Polarforming Design with Phase Shifter Based Polarization Reconfigurable Antennas
May 28, 2025In this paper, we propose a new form of polarization reconfigurable antennas (PRAs) that can form linear, circular, and general elliptical polarizations assisted by phase shifters (PSs). With PRAs, polarforming is achieved, which enables the antenna to shape its polarization into a desired state for aligning with that of the received electromagnetic (EM) wave or reconfiguring that of the transmit EM wave. To demonstrate the benefits of polarforming, we investigate a PRA-aided single-input single-output (SISO) communication system equipped with tunable PSs for polarization adaptation. We characterize the achievable signal-to-noise ratio (SNR) at the receiver as a function of the phase shifts of PS-based PRAs. Moreover, we develop an alternating optimization approach to maximize the SNR by optimizing the phase shifts at both the transmitter and receiver. Finally, comprehensive simulation results are presented, which not only validate the effectiveness of polarforming in mitigating the channel depolarization effects, but also demonstrate its substantial performance improvement over conventional systems.
Wireless Communication for Low-Altitude Economy with UAV Swarm Enabled Two-Level Movable Antenna System
May 28, 2025Unmanned aerial vehicle (UAV) is regarded as a key enabling platform for low-altitude economy, due to its advantages such as 3D maneuverability, flexible deployment, and LoS air-to-air/ground communication links. In particular, the intrinsic high mobility renders UAV especially suitable for operating as a movable antenna (MA) from the sky. In this paper, by exploiting the flexible mobility of UAV swarm and antenna position adjustment of MA, we propose a novel UAV swarm enabled two-level MA system, where UAVs not only individually deploy a local MA array, but also form a larger-scale MA system with their individual MA arrays via swarm coordination. We formulate a general optimization problem to maximize the minimum achievable rate over all ground UEs, by jointly optimizing the 3D UAV swarm placement positions, their individual MAs' positions, and receive beamforming for different UEs. We first consider the special case where each UAV has only one antenna, under different scenarios of one single UE, two UEs, and arbitrary number of UEs. In particular, for the two-UE case, we derive the optimal UAV swarm placement positions in closed-form that achieves IUI-free communication, where the UAV swarm forms a uniform sparse array (USA) satisfying collision avoidance constraint. While for the general case with arbitrary number of UEs, we propose an efficient alternating optimization algorithm to solve the formulated non-convex optimization problem. Then, we extend the results to the case where each UAV is equipped with multiple antennas. Numerical results verify that the proposed low-altitude UAV swarm enabled MA system significantly outperforms various benchmark schemes, thanks to the exploitation of two-level mobility to create more favorable channel conditions for multi-UE communications.
Polarforming for Wireless Networks: Opportunities and Challenges
May 27, 2025Polarforming emerges as a promising technique for manipulating the polarization of electromagnetic (EM) waves by shaping the polarization of an antenna into a desired state. By dynamically adjusting antenna polarization, polarforming enables real-time polarization matching or mismatching with received EM waves, thereby leveraging polarization degrees of freedom (DoFs) to enhance wireless communication performance. In this article, we first present an overview of the fundamental principles and design approaches underlying the polarforming technique. We then analyze the key advantages of polarforming, including hardware cost reduction, depolarization mitigation, channel adaptation, signal power enhancement, and interference suppression. Furthermore, we explore promising applications of polarforming for next-generation wireless networks. Numerical case studies demonstrate the substantial performance gains of polarforming over conventional fixed-polarization antenna (FPA) systems, along with a discussion of implementation challenges to motivate future research.
Spectral-inspired Neural Operator for Data-efficient PDE Simulation in Physics-agnostic Regimes
May 27, 2025Partial differential equations (PDEs) govern the spatiotemporal evolution of various physical systems. Classical numerical solvers, while accurate, require fine discretization and full knowledge of the governing PDEs, limiting their applicability when the physics is unknown or fast inference is required. Data-driven neural PDE solvers alleviate these constraints by learning from data but demand large training datasets and perform poorly in data-scarce regimes. Physics-aware methods mitigate data requirements by incorporating physical knowledge yet rely on known PDE terms or local numerical schemes, restricting their ability to handle unknown or globally coupled systems. In this work, we propose the Spectral-inspired Neural Operator (SINO), a novel framework that learns PDE operators from limited trajectories (as few as 2-5), without any known PDE terms. SINO operates in the frequency domain and introduces a Frequency-to-Vector module to learn spectral representations analogous to derivative multipliers. To model nonlinear physical interactions, we design a nonlinear operator block that includes a $\Pi$-Block with low-pass filtering to prevent aliasing. Finally, we introduce an operator distillation technique to distill the trained model for efficient inference. SINO achieves state-of-the-art results across multiple PDE benchmarks, demonstrating strong discretization invariance and robust generalization to out-of-distribution initial conditions. To our knowledge, SINO is the first physics-aware method capable of accurately simulating globally coupled systems (e.g., the Navier-Stokes equations) from limited data without any explicit PDE terms.
Evaluating Robustness of Large Audio Language Models to Audio Injection: An Empirical Study
May 26, 2025Large Audio-Language Models (LALMs) are increasingly deployed in real-world applications, yet their robustness against malicious audio injection attacks remains underexplored. This study systematically evaluates five leading LALMs across four attack scenarios: Audio Interference Attack, Instruction Following Attack, Context Injection Attack, and Judgment Hijacking Attack. Using metrics like Defense Success Rate, Context Robustness Score, and Judgment Robustness Index, their vulnerabilities and resilience were quantitatively assessed. Experimental results reveal significant performance disparities among models; no single model consistently outperforms others across all attack types. The position of malicious content critically influences attack effectiveness, particularly when placed at the beginning of sequences. A negative correlation between instruction-following capability and robustness suggests models adhering strictly to instructions may be more susceptible, contrasting with greater resistance by safety-aligned models. Additionally, system prompts show mixed effectiveness, indicating the need for tailored strategies. This work introduces a benchmark framework and highlights the importance of integrating robustness into training pipelines. Findings emphasize developing multi-modal defenses and architectural designs that decouple capability from susceptibility for secure LALMs deployment.
EvdCLIP: Improving Vision-Language Retrieval with Entity Visual Descriptions from Large Language Models
May 24, 2025Vision-language retrieval (VLR) has attracted significant attention in both academia and industry, which involves using text (or images) as queries to retrieve corresponding images (or text). However, existing methods often neglect the rich visual semantics knowledge of entities, thus leading to incorrect retrieval results. To address this problem, we propose the Entity Visual Description enhanced CLIP (EvdCLIP), designed to leverage the visual knowledge of entities to enrich queries. Specifically, since humans recognize entities through visual cues, we employ a large language model (LLM) to generate Entity Visual Descriptions (EVDs) as alignment cues to complement textual data. These EVDs are then integrated into raw queries to create visually-rich, EVD-enhanced queries. Furthermore, recognizing that EVD-enhanced queries may introduce noise or low-quality expansions, we develop a novel, trainable EVD-aware Rewriter (EaRW) for vision-language retrieval tasks. EaRW utilizes EVD knowledge and the generative capabilities of the language model to effectively rewrite queries. With our specialized training strategy, EaRW can generate high-quality and low-noise EVD-enhanced queries. Extensive quantitative and qualitative experiments on image-text retrieval benchmarks validate the superiority of EvdCLIP on vision-language retrieval tasks.
An Outlook on the Opportunities and Challenges of Multi-Agent AI Systems
May 23, 2025Multi-agent AI systems (MAS) offer a promising framework for distributed intelligence, enabling collaborative reasoning, planning, and decision-making across autonomous agents. This paper provides a systematic outlook on the current opportunities and challenges of MAS, drawing insights from recent advances in large language models (LLMs), federated optimization, and human-AI interaction. We formalize key concepts including agent topology, coordination protocols, and shared objectives, and identify major risks such as dependency, misalignment, and vulnerabilities arising from training data overlap. Through a biologically inspired simulation and comprehensive theoretical framing, we highlight critical pathways for developing robust, scalable, and secure MAS in real-world settings.
Training Step-Level Reasoning Verifiers with Formal Verification Tools
May 21, 2025Process Reward Models (PRMs), which provide step-by-step feedback on the reasoning generated by Large Language Models (LLMs), are receiving increasing attention. However, two key research gaps remain: collecting accurate step-level error labels for training typically requires costly human annotation, and existing PRMs are limited to math reasoning problems. In response to these gaps, this paper aims to address the challenges of automatic dataset creation and the generalization of PRMs to diverse reasoning tasks. To achieve this goal, we propose FoVer, an approach for training PRMs on step-level error labels automatically annotated by formal verification tools, such as Z3 for formal logic and Isabelle for theorem proof, which provide automatic and accurate verification for symbolic tasks. Using this approach, we synthesize a training dataset with error labels on LLM responses for formal logic and theorem proof tasks without human annotation. Although this data synthesis is feasible only for tasks compatible with formal verification, we observe that LLM-based PRMs trained on our dataset exhibit cross-task generalization, improving verification across diverse reasoning tasks. Specifically, PRMs trained with FoVer significantly outperform baseline PRMs based on the original LLMs and achieve competitive or superior results compared to state-of-the-art PRMs trained on labels annotated by humans or stronger models, as measured by step-level verification on ProcessBench and Best-of-K performance across 12 reasoning benchmarks, including MATH, AIME, ANLI, MMLU, and BBH. The datasets, models, and code are provided at https://github.com/psunlpgroup/FoVer.
Directional Sparsity Based Statistical Channel Estimation for 6D Movable Antenna Communications
May 21, 2025Six-dimensional movable antenna (6DMA) is an innovative and transformative technology to improve wireless network capacity by adjusting the 3D positions and 3D rotations of antennas/surfaces (sub-arrays) based on the channel spatial distribution. For optimization of the antenna positions and rotations, the acquisition of statistical channel state information (CSI) is essential for 6DMA systems. In this paper, we unveil for the first time a new \textbf{\textit{directional sparsity}} property of the 6DMA channels between the base station (BS) and the distributed users, where each user has significant channel gains only with a (small) subset of 6DMA position-rotation pairs, which can receive direct/reflected signals from the user. By exploiting this property, a covariance-based algorithm is proposed for estimating the statistical CSI in terms of the average channel power at a small number of 6DMA positions and rotations. Based on such limited channel power estimation, the average channel powers for all possible 6DMA positions and rotations in the BS movement region are reconstructed by further estimating the multi-path average power and direction-of-arrival (DOA) vectors of all users. Simulation results show that the proposed directional sparsity-based algorithm can achieve higher channel power estimation accuracy than existing benchmark schemes, while requiring a lower pilot overhead.