Multi-Modal Large Models Based Beam Prediction: An Example Empowered by DeepSeek

Beam prediction is an effective approach to reduce training overhead in massive multiple-input multiple-output (MIMO) systems. However, existing beam prediction models still exhibit limited generalization ability in diverse scenarios, which remains a critical challenge. In this paper, we propose MLM-BP, a beam prediction framework based on the multi-modal large model released by DeepSeek, with full consideration of multi-modal environmental information. Specifically, the distribution of scatterers that impact the optimal beam is captured by the sensing devices. Then positions are tokenized to generate text-based representations, and multi-view images are processed by an image encoder, which is fine-tuned with low-rank adaptation (LoRA), to extract environmental embeddings. Finally, these embeddings are fed into the large model, and an output projection module is designed to determine the optimal beam index. Simulation results show that MLM-BP achieves 98.1% Top-1 accuracy on the simulation dataset. Additionally, it demonstrates few-shot generalization on a real-world dataset, achieving 72.7% Top-1 accuracy and 92.4% Top-3 accuracy with only 30% of the dataset, outperforming the existing small models by over 15%.

Case-Based Reasoning Enhances the Predictive Power of LLMs in Drug-Drug Interaction

Drug-drug interaction (DDI) prediction is critical for treatment safety. While large language models (LLMs) show promise in pharmaceutical tasks, their effectiveness in DDI prediction remains challenging. Inspired by the well-established clinical practice where physicians routinely reference similar historical cases to guide their decisions through case-based reasoning (CBR), we propose CBR-DDI, a novel framework that distills pharmacological principles from historical cases to improve LLM reasoning for DDI tasks. CBR-DDI constructs a knowledge repository by leveraging LLMs to extract pharmacological insights and graph neural networks (GNNs) to model drug associations. A hybrid retrieval mechanism and dual-layer knowledge-enhanced prompting allow LLMs to effectively retrieve and reuse relevant cases. We further introduce a representative sampling strategy for dynamic case refinement. Extensive experiments demonstrate that CBR-DDI achieves state-of-the-art performance, with a significant 28.7% accuracy improvement over both popular LLMs and CBR baseline, while maintaining high interpretability and flexibility.

A Unified RCS Modeling of Typical Targets for 3GPP ISAC Channel Standardization and Experimental Analysis

Accurate radar cross section (RCS) modeling is crucial for characterizing target scattering and improving the precision of Integrated Sensing and Communication (ISAC) channel modeling. Existing RCS models are typically designed for specific target types, leading to increased complexity and lack of generalization. This makes it difficult to standardize RCS models for 3GPP ISAC channels, which need to account for multiple typical target types simultaneously. Furthermore, 3GPP models must support both system-level and link-level simulations, requiring the integration of large-scale and small-scale scattering characteristics. To address these challenges, this paper proposes a unified RCS modeling framework that consolidates these two aspects. The model decomposes RCS into three components: (1) a large-scale power factor representing overall scattering strength, (2) a small-scale angular-dependent component describing directional scattering, and (3) a random component accounting for variations across target instances. We validate the model through mono-static RCS measurements for UAV, human, and vehicle targets across five frequency bands. The results demonstrate that the proposed model can effectively capture RCS variations for different target types. Finally, the model is incorporated into an ISAC channel simulation platform to assess the impact of target RCS characteristics on path loss, delay spread, and angular spread, providing valuable insights for future ISAC system design.

Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting

Multi-task forecasting has become the standard approach for time-series forecasting (TSF). However, we show that it suffers from an Expressiveness Bottleneck, where predictions at different time steps share the same representation, leading to unavoidable errors even with optimal representations. To address this issue, we propose a two-stage framework: first, pre-train a foundation model for one-step-ahead prediction; then, adapt it using step-specific LoRA modules.This design enables the foundation model to handle any number of forecast steps while avoiding the expressiveness bottleneck. We further introduce the Mixture-of-LoRA (MoLA) model, which employs adaptively weighted LoRA experts to achieve partial parameter sharing across steps. This approach enhances both efficiency and forecasting performance by exploiting interdependencies between forecast steps. Experiments show that MoLA significantly improves model expressiveness and outperforms state-of-the-art time-series forecasting methods. Code is available at https://anonymous.4open.science/r/MoLA-BC92.

Empirical Study on Near-Field and Spatial Non-Stationarity Modeling for THz XL-MIMO Channel in Indoor Scenario

Terahertz (THz) extremely large-scale MIMO (XL-MIMO) is considered a key enabling technology for 6G and beyond due to its advantages such as wide bandwidth and high beam gain. As the frequency and array size increase, users are more likely to fall within the near-field (NF) region, where the far-field plane-wave assumption no longer holds. This also introduces spatial non-stationarity (SnS), as different antenna elements observe distinct multipath characteristics. Therefore, this paper proposes a THz XL-MIMO channel model that accounts for both NF propagation and SnS, validated using channel measurement data. In this work, we first conduct THz XL-MIMO channel measurements at 100 GHz and 132 GHz using 301- and 531-element ULAs in indoor environments, revealing pronounced NF effects characterized by nonlinear inter-element phase variations, as well as element-dependent delay and angle shifts. Moreover, the SnS phenomenon is observed, arising not only from blockage but also from inconsistent reflection or scattering. Based on these observations, a hybrid NF channel modeling approach combining the scatterer-excited point-source model and the specular reflection model is proposed to capture nonlinear phase variation. For SnS modeling, amplitude attenuation factors (AAFs) are introduced to characterize the continuous variation of path power across the array. By analyzing the statistical distribution and spatial autocorrelation properties of AAFs, a statistical rank-matching-based method is proposed for their generation. Finally, the model is validated using measured data. Evaluation across metrics such as entropy capacity, condition number, spatial correlation, channel gain, Rician K-factor, and RMS delay spread confirms that the proposed model closely aligns with measurements and effectively characterizes the essential features of THz XL-MIMO channels.

A Unified Deterministic Channel Model for Multi-Type RIS with Reflective, Transmissive, and Polarization Operations

Reconfigurable Intelligent Surface (RIS) technologies have been considered as a promising enabler for 6G, enabling advantageous control of electromagnetic (EM) propagation. RIS can be categorized into multiple types based on their reflective/transmissive modes and polarization control capabilities, all of which are expected to be widely deployed in practical environments. A reliable RIS channel model is essential for the design and development of RIS communication systems. While deterministic modeling approaches such as ray-tracing (RT) offer significant benefits, a unified model that accommodates all RIS types is still lacking. This paper addresses this gap by developing a high-precision deterministic channel model based on RT, supporting multiple RIS types: reflective, transmissive, hybrid, and three polarization operation modes. To achieve this, a unified EM response model for the aforementioned RIS types is developed. The reflection and transmission coefficients of RIS elements are derived using a tensor-based equivalent impedance approach, followed by calculating the scattered fields of the RIS to establish an EM response model. The performance of different RIS types is compared through simulations in typical scenarios. During this process, passive and lossless constraints on the reflection and transmission coefficients are incorporated to ensure fairness in the performance evaluation. Simulation results validate the framework's accuracy in characterizing the RIS channel, and specific cases tailored for dual-polarization independent control and polarization rotating RISs are highlighted as insights for their future deployment. This work can be helpful for the evaluation and optimization of RIS-enabled wireless communication systems.

Voila: Voice-Language Foundation Models for Real-Time Autonomous Interaction and Voice Role-Play

A voice AI agent that blends seamlessly into daily life would interact with humans in an autonomous, real-time, and emotionally expressive manner. Rather than merely reacting to commands, it would continuously listen, reason, and respond proactively, fostering fluid, dynamic, and emotionally resonant interactions. We introduce Voila, a family of large voice-language foundation models that make a step towards this vision. Voila moves beyond traditional pipeline systems by adopting a new end-to-end architecture that enables full-duplex, low-latency conversations while preserving rich vocal nuances such as tone, rhythm, and emotion. It achieves a response latency of just 195 milliseconds, surpassing the average human response time. Its hierarchical multi-scale Transformer integrates the reasoning capabilities of large language models (LLMs) with powerful acoustic modeling, enabling natural, persona-aware voice generation -- where users can simply write text instructions to define the speaker's identity, tone, and other characteristics. Moreover, Voila supports over one million pre-built voices and efficient customization of new ones from brief audio samples as short as 10 seconds. Beyond spoken dialogue, Voila is designed as a unified model for a wide range of voice-based applications, including automatic speech recognition (ASR), Text-to-Speech (TTS), and, with minimal adaptation, multilingual speech translation. Voila is fully open-sourced to support open research and accelerate progress toward next-generation human-machine interactions.

Research and Experimental Validation for 3GPP ISAC Channel Modeling Standardization

Integrated Sensing and Communication (ISAC) is considered a key technology in 6G networks. An accurate sensing channel model is crucial for the design and sensing performance evaluation of ISAC systems. The widely used Geometry-Based Stochastic Model (GBSM), typically applied in standardized channel modeling, mainly focuses on the statistical fading characteristics of the channel. However, it fails to capture the characteristics of targets in ISAC systems, such as their positions and velocities, as well as the impact of the targets on the background. To address this issue, this paper proposes an extended GBSM (E-GBSM) sensing channel model that incorporates newly discovered channel characteristics into a unified modeling framework. In this framework, the sensing channel is divided into target and background channels. For the target channel, the model introduces a concatenated modeling approach, while for the background channel, a parameter called the power control factor is introduced to assess impact of the target on the background channel, making the modeling framework applicable to both mono-static and bi-static sensing modes. To validate the proposed model's effectiveness, measurements of target and background channels are conducted in both indoor and outdoor scenarios, covering various sensing targets such as metal plates, reconfigurable intelligent surfaces, human bodies, UAVs, and vehicles. The experimental results provide important theoretical support and empirical data for the standardization of ISAC channel modeling.

A Survey of New Mid-Band/FR3 for 6G: Channel Measurement, Characterization and Modeling in Outdoor Environment

The new mid-band (6-24 GHz) has attracted significant attention from both academia and industry, which is the spectrum with continuous bandwidth that combines the coverage benefits of low frequency with the capacity advantages of high frequency. Since outdoor environments represent the primary application scenario for mobile communications, this paper presents the first comprehensive review and summary of multi-scenario and multi-frequency channel characteristics based on extensive outdoor new mid-band channel measurement data, including UMa, UMi, and O2I. Specifically, a survey of the progress of the channel characteristics is presented, such as path loss, delay spread, angular spread, channel sparsity, capacity and near-field spatial non-stationary characteristics. Then, considering that satellite communication will be an important component of future communication systems, we examine the impact of clutter loss in air-ground communications. Our analysis of the frequency dependence of mid-band clutter loss suggests that its impact is not significant. Additionally, given that penetration loss is frequency-dependent, we summarize its variation within the FR3 band. Based on experimental results, comparisons with the standard model reveal that while the 3GPP TR 38.901 model remains a useful reference for penetration loss in wood and glass, it shows significant deviations for concrete and glass, indicating the need for further refinement. In summary, the findings of this survey provide both empirical data and theoretical support for the deployment of mid-band in future communication systems, as well as guidance for optimizing mid-band base station deployment in the outdoor environment. This survey offers the reference for improving standard models and advancing channel modeling.

Addressing the Curse of Scenario and Task Generalization in AI-6G: A Multi-Modal Paradigm

Existing works on machine learning (ML)-empowered wireless communication primarily focus on monolithic scenarios and single tasks. However, with the blooming growth of communication task classes coupled with various task requirements in future 6G systems, this working pattern is obviously unsustainable. Therefore, identifying a groundbreaking paradigm that enables a universal model to solve multiple tasks in the physical layer within diverse scenarios is crucial for future system evolution. This paper aims to fundamentally address the curse of ML model generalization across diverse scenarios and tasks by unleashing multi-modal feature integration capabilities in future systems. Given the universality of electromagnetic propagation theory, the communication process is determined by the scattering environment, which can be more comprehensively characterized by cross-modal perception, thus providing sufficient information for all communication tasks across varied environments. This fact motivates us to propose a transformative two-stage multi-modal pre-training and downstream task adaptation paradigm...