arXiv subjects

Yuan Guo

Publications and source records attributed to Yuan Guo.

At least 37 records · Page 2Linked to original sources

arXiv2026

Environment-Aware Near-Field Channel Estimation Leveraging CKM and ISAC

This paper proposes an environment-aware near-field channel estimation framework for integrated sensing and communication (ISAC) systems equipped with extremely large-scale antenna arrays (ELAAs). The proposed framework jointly exploits channel knowledge maps (CKMs) and ISAC to obtain a priori information on static and dynamic environmental features for facilitating channel estimation. In particular, we propose a novel CKM representation, termed the virtual object map (VOM), which stores the locations of virtual environment objects (EOs) to characterize the dominant multipath components (MPCs) induced by static physical EOs. In addition, we design a sensing-assisted channel training protocol, in which the ISAC-enabled base station (BS) transmits downlink pilots while simultaneously collecting monostatic echoes for sensing dynamic targets in the environment, and the user equipment (UE) feeds back a quantized version of its received pilot observation. Based on the VOM prior and the sensed dynamic information, the BS jointly estimates the coefficients of the static and dynamic MPCs to recover the near-field channel. Numerical results demonstrate that the proposed joint VOM- and sensing-aided channel estimation scheme significantly outperforms conventional schemes without VOM-based priors and/or dynamic sensing in terms of both channel estimation accuracy and achievable rate.

cs.IT↗

arXiv2026

UI-Venus-1.5 Technical Report

GUI agents have emerged as a powerful paradigm for automating interactions in digital environments, yet achieving both broad generality and consistently strong task performance remains challenging. In this report, we present UI-Venus-1.5, a unified, end-to-end GUI Agent designed for robust real-world applications. The proposed model family comprises two dense variants (2B and 8B) and one mixture-of-experts variant (30B-A3B) to meet various downstream application scenarios. Compared to our previous version, UI-Venus-1.5 introduces three key technical advances: (1) a comprehensive Mid-Training stage leveraging 10 billion tokens across 30+ datasets to establish foundational GUI semantics; (2) Online Reinforcement Learning with full-trajectory rollouts, aligning training objectives with long-horizon, dynamic navigation in large-scale environments; and (3) a single unified GUI Agent constructed via Model Merging, which synthesizes domain-specific models (grounding, web, and mobile) into one cohesive checkpoint. Extensive evaluations demonstrate that UI-Venus-1.5 establishes new state-of-the-art performance on benchmarks such as ScreenSpot-Pro (69.6%), VenusBench-GD (75.0%), and AndroidWorld (77.6%), significantly outperforming previous strong baselines. In addition, UI-Venus-1.5 demonstrates robust navigation capabilities across a variety of Chinese mobile apps, effectively executing user instructions in real-world scenarios. Code: https://github.com/inclusionAI/UI-Venus; Model: https://huggingface.co/collections/inclusionAI/ui-venus

cs.CV↗

arXiv2026

Near-Field Multi-Cell ISCAP with Extremely Large-Scale Antenna Array

This paper investigates a coordinated multi-cell integrated sensing, communication, and powering (ISCAP) system operating in the electromagnetic near field, where each base station (BS) employs an extremely large-scale antenna array (ELAA) to simultaneously support downlink communication, wireless power transfer (WPT), and environmental sensing. Three categories of communication users (CUs) with different interference cancellation capabilities are considered, and sensing is enabled through a distributed multiple-input multiple-output (MIMO) radar architecture. To address the resulting design challenges, a robust optimization framework is proposed by optimizing the beamforming strategy to maximize the worst-case detection probability over a prescribed sensing region, subject to per-user signal-to-interference-plus-noise ratio (SINR) constraints and energy harvesting requirements at energy receivers (ERs), while explicitly capturing the uncertainty in ER locations. By leveraging semidefinite relaxation (SDR), the original non-convex problem is reformulated as a convex semidefinite program with a provably tight relaxation. Furthermore, a low-complexity maximum ratio transmission (MRT)-based suboptimal scheme is developed, yielding a closed-form solution in the asymptotic regime as the number of antenna elements approaches infinity. Extensive numerical results reveal the fundamental trade-offs among sensing accuracy, communication reliability, and WPT efficiency.

cs.NI↗

arXiv2025

VenusBench-GD: A Comprehensive Multi-Platform GUI Benchmark for Diverse Grounding Tasks

GUI grounding is a critical component in building capable GUI agents. However, existing grounding benchmarks suffer from significant limitations: they either provide insufficient data volume and narrow domain coverage, or focus excessively on a single platform and require highly specialized domain knowledge. In this work, we present VenusBench-GD, a comprehensive, bilingual benchmark for GUI grounding that spans multiple platforms, enabling hierarchical evaluation for real-word applications. VenusBench-GD contributes as follows: (i) we introduce a large-scale, cross-platform benchmark with extensive coverage of applications, diverse UI elements, and rich annotated data, (ii) we establish a high-quality data construction pipeline for grounding tasks, achieving higher annotation accuracy than existing benchmarks, and (iii) we extend the scope of element grounding by proposing a hierarchical task taxonomy that divides grounding into basic and advanced categories, encompassing six distinct subtasks designed to evaluate models from complementary perspectives. Our experimental findings reveal critical insights: general-purpose multimodal models now match or even surpass specialized GUI models on basic grounding tasks. In contrast, advanced tasks, still favor GUI-specialized models, though they exhibit significant overfitting and poor robustness. These results underscore the necessity of comprehensive, multi-tiered evaluation frameworks.

cs.CV↗

arXiv2025

Beamforming for Transmissive RIS Transceiver Enabled Simultaneous Wireless Information and Power Transfer Systems

This paper investigates a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-empowered simultaneous wireless information and power transfer (SWIPT) system with multiple information decoding (ID) and energy harvesting (EH) users. Under the considered system model, we formulate an optimization problem that maximizes the sum-rate of all ID users via the design of the TRIS transceiver's active beamforming. The design is constrained by per-antenna power limits at the TRIS transceiver and by the minimum harvested energy demand of all EH users. Due to the non-convexity of the objective function and the energy harvesting constraint, the sum-rate problem is difficult to tackle. To solve this challenging optimization problem, by leveraging the weighted minimum mean squared error (WMMSE) framework and the majorization-minimization (MM) method, we propose a second-order cone programming (SOCP)-based algorithm. Per-element power constraints introduce a large number of constraints, making the problem considerably more difficult. By applying the alternating direction method of multipliers (ADMM) method, we successfully develop an analytical, computationally efficient, and highly parallelizable algorithm to address this challenge. Numerical results are provided to validate the convergence and effectiveness of the proposed algorithms. Furthermore, the low-complexity algorithm significantly reduces computational complexity without performance degradation.

eess.SP↗

arXiv2025

Fair Rate Maximization for Multi-User Multi-Cell MISO Communication Systems via Novel Transmissive RIS Transceiver

This paper explores a multi-cell multiple-input single-output (MISO) downlink communication system enabled by a unique transmissive reconfigurable intelligent surface (TRIS) transceiver configuration. Within this system framework, we formulate an optimization problem for the purpose of maximizing the minimum rate of users for each cell via designing the transmit beamforming of the TRIS transceiver, subject to the power constraints of each TRIS transceiver unit. Since the objective function is non-differentiable, the max-min rate problem is difficult to solve. In order to tackle this challenging optimization problem, an efficient low-complexity optimization algorithm is developed. Specifically, the log-form rate function is transformed into a tractable form by employing the fractional programming (FP) methodology. Next, the max-min objective function can be approximated using a differentiable function derived from smooth approximation theory. Moreover, by applying the majorization-minimization (MM) technique and examining the optimality conditions, a solution is proposed that updates all variables analytically without relying on any numerical solvers. Numerical results are presented to demonstrate the convergence and effectiveness of the proposed low-complexity algorithm. Additionally, the algorithm can significantly reduce the computational complexity without performance loss. Furthermore, the simulation results illustrate the clear superiority of the deployment of the TRIS transceiver over the benchmark schemes.

eess.SP↗

arXiv2025

Cramér-Rao Bound Optimization for Fluid Antenna-Empowered Integrated Sensing and Uplink Communication System

Integrated sensing and communication (ISAC) is a promising solution for the future sixth-generation (6G) system. However, classical fixed-position antenna (FPA) ISAC systems fail to fully utilize spatial degrees of freedom (DoFs), resulting in limited gains for both radar sensing and communication functionalities. This challenge can be addressed by the emerging novel fluid antenna (FA) technology, which can pursue better channel conditions and improve sensing and communication performances. In this paper, we aim to minimize the Cramér-Rao bound (CRB) for estimating the target's angle while guaranteeing communication performance. This involves jointly optimizing active beamforming, power allocation, receiving filters, and FA position configurations, which is a highly non-convex problem. To tackle this difficulty, we propose an efficient iterative solution that analytically optimizes all variables without relying on numerical solvers, i.e., CVX. Specifically, by leveraging cutting-edge majorization-minimization (MM) and penalty-dual-decomposition (PDD) methods, we develop a low-complexity algorithm to solve the beamformer configuration problem containing the fractional and quartic terms. Numerical simulation results demonstrate the effectiveness and efficiency of our proposed algorithm, highlighting significant performance improvements achieved by employing FA in the ISAC system.

eess.SP↗

arXiv2025

Resource Allocation for Transmissive RIS Transceiver Enabled SWIPT Systems

A novel transmissive reconfigurable intelligent surface (TRIS) transceiver-empowered simultaneous wireless information and power transfer (SWIPT) framework is proposed. The sum-rate of the information decoding (ID) users is maximized by optimizing the TRIS transceiver's beamforming, subject to the energy harvesting (EH) users' quality-of-harvest and the per-antenna power constraints. To solve this non-convex problem, we develop an efficient optimization algorithm. First, the original problem is reformulated as a semi-definite programming (SDP) problem. The resulting SDP problem is then addressed using successive convex approximation (SCA) combined with a penalty-based method. Numerical results demonstrate the effectiveness of the algorithm.

eess.SP↗

arXiv2025

Movable Antenna Enhanced Networked Integrated Sensing and Communication System

Integrated sensing and communication (ISAC) is a key technology for future 6G networks. Most existing studies focus on monostatic and/or bistatic setups with limited coverage and capabilities. Networked ISAC systems with distributed base stations (BSs) can overcome these limitations. Moreover, movable antenna (MA) architectures offer improved ISAC performance over fixed-position antennas (FPAs) by enabling adaptable antenna movement. In this paper, we utilize the MA to promote communication capability with guaranteed sensing performance via jointly designing beamforming, power allocation, receiving filters and position configuration of transmit/receive MA towards maximizing the sum rate for both downlink (DL) and uplink (UL) users. The optimization problem is highly difficult due to the unique channel model derived from the position coefficient of the MA. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) method, we develop an efficient solution that optimizes all variables via convex optimization techniques. Extensive simulation results verify the effectiveness of our proposed algorithms and demonstrate the substantial performance promotion by deploying the MA framework in the networked ISAC system.

eess.SP↗

arXiv2025

UI-Venus Technical Report: Building High-performance UI Agents with RFT

We present UI-Venus, a native UI agent that takes only screenshots as input based on a multimodal large language model. UI-Venus achieves SOTA performance on both UI grounding and navigation tasks using only several hundred thousand high-quality training samples through reinforcement finetune (RFT) based on Qwen2.5-VL. Specifically, the 7B and 72B variants of UI-Venus obtain 94.1% / 50.8% and 95.3% / 61.9% on the standard grounding benchmarks, i.e., Screenspot-V2 / Pro, surpassing the previous SOTA baselines including open-source GTA1 and closed-source UI-TARS-1.5. To show UI-Venus's summary and planing ability, we also evaluate it on the AndroidWorld, an online UI navigation arena, on which our 7B and 72B variants achieve 49.1% and 65.9% success rate, also beating existing models. To achieve this, we introduce carefully designed reward functions for both UI grounding and navigation tasks and corresponding efficient data cleaning strategies. To further boost navigation performance, we propose Self-Evolving Trajectory History Alignment & Sparse Action Enhancement that refine historical reasoning traces and balances the distribution of sparse but critical actions, leading to more coherent planning and better generalization in complex UI tasks. Our contributions include the publish of SOTA open-source UI agents, comprehensive data cleaning protocols and a novel self-evolving framework for improving navigation performance, which encourage further research and development in the community. Code is available at https://github.com/inclusionAI/UI-Venus.

cs.CV↗

arXiv2025

Max-Min Rate Optimization for Multigroup Multicast MISO Systems Via Novel Transmissive RIS Transceiver

This paper investigates a novel transmissive reconfigurable intelligent surface (RIS) transceiver architectureenabled multigroup multicast downlink communication system. Under this setup, an optimization problem is formulated to maximize the minimum rate of users across all groups, subject to the maximum available power of each RIS unit. Due to the nondifferentiable nature of the objective function, the max-min rate problem is challenging to solve. To tackle this difficult problem, we develop an iterative solution by leveraging the successive convex approximation (SCA) and the penalty function method. However, the above approach has high computational complexity and may lead to compromised performance. To overcome these drawbacks, we design an efficient second-order cone programming (SOCP)-based method using the weighted minimum mean squared error (WMMSE) framework to reduce computational complexity. Furthermore, to further reduce the computational complexity, we also propose a low-complexity and solver-free algorithm that analytically updates all variables by combining the smooth approximation theory and the majorization-minimization (MM) method. Numerical results are provided to verify the convergence and effectiveness of our proposed three algorithms. It is also demonstrated that the SOCP-based method outperforms the penalty-based algorithm in terms of both the achieved min rate and the computational complexity. In contrast, the lowcomplexity design achieves significantly lower complexity with only slightly degraded performance.

eess.SP↗

arXiv2025

Atomic-to-Compositional Generalization for Mobile Agents with A New Benchmark and Scheduling System

Autonomous agents powered by multimodal large language models have been developed to facilitate task execution on mobile devices. However, prior work has predominantly focused on atomic tasks -- such as shot-chain execution tasks and single-screen grounding tasks -- while overlooking the generalization to compositional tasks, which are indispensable for real-world applications. This work introduces UI-NEXUS, a comprehensive benchmark designed to evaluate mobile agents on three categories of compositional operations: Simple Concatenation, Context Transition, and Deep Dive. UI-NEXUS supports interactive evaluation in 20 fully controllable local utility app environments, as well as 30 online Chinese and English service apps. It comprises 100 interactive task templates with an average optimal step count of 14.05. Experimental results across a range of mobile agents with agentic workflow or agent-as-a-model show that UI-NEXUS presents significant challenges. Specifically, existing agents generally struggle to balance performance and efficiency, exhibiting representative failure modes such as under-execution, over-execution, and attention drift, causing visible atomic-to-compositional generalization gap. Inspired by these findings, we propose AGENT-NEXUS, a lightweight and efficient scheduling system to tackle compositional mobile tasks. AGENT-NEXUS extrapolates the abilities of existing mobile agents by dynamically decomposing long-horizon tasks to a series of self-contained atomic subtasks. AGENT-NEXUS achieves 24% to 40% task success rate improvement for existing mobile agents on compositional operation tasks within the UI-NEXUS benchmark without significantly sacrificing inference overhead. The demo video, dataset, and code are available on the project page at https://ui-nexus.github.io.

cs.CL↗

arXiv2025

Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks

This paper proposes a Curriculum-Transfer-Learning based physics-informed neural network (CTL-PINN) for long-term simulation of physical and mechanical behaviors. The main innovation of CTL-PINN lies in decomposing long-term problems into a sequence of short-term subproblems. Initially, the standard PINN is employed to solve the first sub-problem. As the simulation progresses, subsequent time-domain problems are addressed using a curriculum learning approach that integrates information from previous steps. Furthermore, transfer learning techniques are incorporated, allowing the model to effectively utilize prior training data and solve sequential time domain transfer problems. CTL-PINN combines the strengths of curriculum learning and transfer learning, overcoming the limitations of standard PINNs, such as local optimization issues, and addressing the inaccuracies over extended time domains encountered in CL-PINN and the low computational efficiency of TL-PINN. The efficacy and robustness of CTL-PINN are demonstrated through applications to nonlinear wave propagation, Kirchhoff plate dynamic response, and the hydrodynamic model of the Three Gorges Reservoir Area, showcasing its superior capability in addressing long-term computational challenges.

cs.LG↗

arXiv2024

Anisotropic Diffusion Probabilistic Model for Imbalanced Image Classification

Real-world data often has a long-tailed distribution, where the scarcity of tail samples significantly limits the model's generalization ability. Denoising Diffusion Probabilistic Models (DDPM) are generative models based on stochastic differential equation theory and have demonstrated impressive performance in image classification tasks. However, existing diffusion probabilistic models do not perform satisfactorily in classifying tail classes. In this work, we propose the Anisotropic Diffusion Probabilistic Model (ADPM) for imbalanced image classification problems. We utilize the data distribution to control the diffusion speed of different class samples during the forward process, effectively improving the classification accuracy of the denoiser in the reverse process. Specifically, we provide a theoretical strategy for selecting noise levels for different categories in the diffusion process based on error analysis theory to address the imbalanced classification problem. Furthermore, we integrate global and local image prior in the forward process to enhance the model's discriminative ability in the spatial dimension, while incorporate semantic-level contextual information in the reverse process to boost the model's discriminative power and robustness. Through comparisons with state-of-the-art methods on four medical benchmark datasets, we validate the effectiveness of the proposed method in handling long-tail data. Our results confirm that the anisotropic diffusion model significantly improves the classification accuracy of rare classes while maintaining the accuracy of head classes. On the skin lesion datasets, PAD-UFES and HAM10000, the F1-scores of our method improved by 4% and 3%, respectively compared to the original diffusion probabilistic model.

cs.CV↗

arXiv2024

Can Large Language Models Logically Predict Myocardial Infarction? Evaluation based on UK Biobank Cohort

Background: Large language models (LLMs) have seen extraordinary advances with applications in clinical decision support. However, high-quality evidence is urgently needed on the potential and limitation of LLMs in providing accurate clinical decisions based on real-world medical data. Objective: To evaluate quantitatively whether universal state-of-the-art LLMs (ChatGPT and GPT-4) can predict the incidence risk of myocardial infarction (MI) with logical inference, and to further make comparison between various models to assess the performance of LLMs comprehensively. Methods: In this retrospective cohort study, 482,310 participants recruited from 2006 to 2010 were initially included in UK Biobank database and later on resampled into a final cohort of 690 participants. For each participant, tabular data of the risk factors of MI were transformed into standardized textual descriptions for ChatGPT recognition. Responses were generated by asking ChatGPT to select a score ranging from 0 to 10 representing the risk. Chain of Thought (CoT) questioning was used to evaluate whether LLMs make prediction logically. The predictive performance of ChatGPT was compared with published medical indices, traditional machine learning models and other large language models. Conclusions: Current LLMs are not ready to be applied in clinical medicine fields. Future medical LLMs are suggested to be expert in medical domain knowledge to understand both natural languages and quantified medical data, and further make logical inferences.

cs.AI↗

arXiv2024

BeHonest: Benchmarking Honesty in Large Language Models

Previous works on Large Language Models (LLMs) have mainly focused on evaluating their helpfulness or harmlessness. However, honesty, another crucial alignment criterion, has received relatively less attention. Dishonest behaviors in LLMs, such as spreading misinformation and defrauding users, present severe risks that intensify as these models approach superintelligent levels. Enhancing honesty in LLMs addresses critical limitations and helps uncover latent capabilities that are not readily expressed. This underscores the urgent need for reliable methods and benchmarks to effectively ensure and evaluate the honesty of LLMs. In this paper, we introduce BeHonest, a pioneering benchmark specifically designed to assess honesty in LLMs comprehensively. BeHonest evaluates three essential aspects of honesty: awareness of knowledge boundaries, avoidance of deceit, and consistency in responses. Building on this foundation, we designed 10 scenarios to evaluate and analyze 9 popular LLMs on the market, including both closed-source and open-source models from different model families with varied model sizes. Our findings indicate that there is still significant room for improvement in the honesty of LLMs. We encourage the AI community to prioritize honesty alignment in these models, which can harness their full potential to benefit society while preventing them from causing harm through deception or inconsistency. Our benchmark and code can be found at: \url{https://github.com/GAIR-NLP/BeHonest}.

cs.CL↗

arXiv2024

On User Association in Large-Scale Heterogeneous LEO Satellite Network

In this paper, we investigate the performance of large-scale heterogeneous low Earth orbit (LEO) satellite networks in the context of three association schemes. In contrast to existing studies, where single-tier LEO satellite-based network deployments are considered, the developed framework captures the heterogeneous nature of real-world satellite network deployments. More specifically, we propose an analytical framework to evaluate the performance of multi-tier LEO satellite-based networks, where the locations of LEO satellites are approximated as points of independent Poisson point processes, with different density, transmit power, and altitude. We propose three association schemes for the considered network topology based on: 1) the Euclidean distance, 2) the average received power, and 3) a random selection. By using stochastic geometry tools, analytical expressions for the association probability, the downlink coverage probability, as well as the spectral efficiency are derived for each association scheme, where the interference is considered. Moreover, we assess the achieved network performance under several different fading environments, including low, typical, and severe fading conditions, namely non-fading, shadowed-Rician and Rayleigh fading channels, respectively. Our results reveal the impact of fading channels on the coverage probability, and illustrate that the average power-based association scheme outperforms in terms of achieved coverage and spectral efficiency performance against the other two association policies. Furthermore, we highlight the impact of the proposed association schemes and the network topology on the optimal number of LEO satellites, providing guidance for the planning of multi-tier LEO satellite-based networks in order to enhance network performance.

cs.ET↗

arXiv2024

A Novel Temperature-based Model for SWIPT

In this letter, a novel communication paradigm for simultaneous wireless information and power transfer (SWIPT) is proposed, which leverages the thermal characteristics of electromagnetic signals. In particular, the proposed scheme exploits the inherent thermal dynamics of electromagnetic signals, enabling the seamless integration of information decoding and energy harvesting (EH). As a consequence, in contrast to conventional SWIPT techniques, the proposed model eliminates the need to divide the received signal into orthogonal components. By exploiting the thermal correlation between consecutive time slots, the communication channel is converted to a virtual multiple-input multiple-output (MIMO) channel with memory. We evaluate the achievable rate of the proposed temperature-modulated channel for uniform and exponential input distributions and assess its performance in terms of harvested energy through a non-linear harvesting model. Our numerical results reveal that the exponential distribution outperforms the uniform distribution in rate and harvested energy at low input power levels, while the uniform distribution achieves a better EH performance at high input power levels.

cs.IT↗