System-Level Comparison of Multimodal and In-Band mmWave Sensing for Beam Prediction in 6G ISAC

Integrated sensing and communication (ISAC) can reduce beam-training overhead in mmWave vehicle-to-infrastructure (V2I) links by enabling in-band sensing-based beam prediction, while exteroceptive sensors can further enhance the prediction accuracy. This work develop a system-level framework that evaluates camera, LiDAR, radar, GPS, and in-band mmWave power, both individually and in multimodal fusion using the DeepSense-6G Scenario-33 dataset. A latency-aware neural network composed of lightweight convolutional (CNN) and multilayer-perceptron (MLP) encoders predict a 64-beam index. We assess performance using Top-k accuracy alongside spectral-efficiency (SE) gap, signal-to-noise-ratio (SNR) gap, rate loss, and end-to-end latency. Results show that the mmWave power vector is a strong standalone predictor, and fusing exteroceptive sensors with it preserves high performance: mmWave alone and mmWave+LiDAR/GPS/Radar achieve 98% Top-5 accuracy, while mmWave+camera achieves 94% Top-5 accuracy. The proposed framework establishes calibrated baselines for 6G ISAC-assisted beam prediction in V2I systems.

CNSight: Evaluation of Clinical Note Segmentation Tools

Clinical notes are often stored in unstructured or semi-structured formats after extraction from electronic medical record (EMR) systems, which complicates their use for secondary analysis and downstream clinical applications. Reliable identification of section boundaries is a key step toward structuring these notes, as sections such as history of present illness, medications, and discharge instructions each provide distinct clinical contexts. In this work, we evaluate rule-based baselines, domain-specific transformer models, and large language models for clinical note segmentation using a curated dataset of 1,000 notes from MIMIC-IV. Our experiments show that large API-based models achieve the best overall performance, with GPT-5-mini reaching a best average F1 of 72.4 across sentence-level and freetext segmentation. Lightweight baselines remain competitive on structured sentence-level tasks but falter on unstructured freetext. Our results provide guidance for method selection and lay the groundwork for downstream tasks such as information extraction, cohort identification, and automated summarization.

Feature-Centric Unsupervised Node Representation Learning Without Homophily Assumption

Unsupervised node representation learning aims to obtain meaningful node embeddings without relying on node labels. To achieve this, graph convolution, which aggregates information from neighboring nodes, is commonly employed to encode node features and graph topology. However, excessive reliance on graph convolution can be suboptimal-especially in non-homophilic graphs-since it may yield unduly similar embeddings for nodes that differ in their features or topological properties. As a result, adjusting the degree of graph convolution usage has been actively explored in supervised learning settings, whereas such approaches remain underexplored in unsupervised scenarios. To tackle this, we propose FUEL, which adaptively learns the adequate degree of graph convolution usage by aiming to enhance intra-class similarity and inter-class separability in the embedding space. Since classes are unknown, FUEL leverages node features to identify node clusters and treats these clusters as proxies for classes. Through extensive experiments using 15 baseline methods and 14 benchmark datasets, we demonstrate the effectiveness of FUEL in downstream tasks, achieving state-of-the-art performance across graphs with diverse levels of homophily.

ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation

Recently, large language models (LLMs) have been widely used as recommender systems, owing to their strong reasoning capability and their effectiveness in handling cold-start items. To better adapt LLMs for recommendation, retrieval-augmented generation (RAG) has been incorporated. Most existing RAG methods are user-based, retrieving purchase patterns of users similar to the target user and providing them to the LLM. In this work, we propose ItemRAG, an item-based RAG method for LLM-based recommendation that retrieves relevant items (rather than users) from item-item co-purchase histories. ItemRAG helps LLMs capture co-purchase patterns among items, which are beneficial for recommendations. Especially, our retrieval strategy incorporates semantically similar items to better handle cold-start items and uses co-purchase frequencies to improve the relevance of the retrieved items. Through extensive experiments, we demonstrate that ItemRAG consistently (1) improves the zero-shot LLM-based recommender by up to 43% in Hit-Ratio-1 and (2) outperforms user-based RAG baselines under both standard and cold-start item recommendation settings.

Blocker-Aware Beamforming and Dynamic Power Allocation for Multicarrier ISAC-NOMA Systems

This paper proposes a blocker-aware multicarrier integrated sensing and communication (ISAC)-non orthogonal multiple access (NOMA) system, leveraging hybrid beamforming and dynamic power allocation to enhance spectrum efficiency in 6G networks. Recognizing the performance degradation caused by environmental blockers, the system introduces a joint waveform design that ensures robust operation under varying channel conditions. A channel switching mechanism is deployed to reroute communication through alternative non-line-of-sight paths when the primary line-of-sight links are obstructed. Moreover, a dynamic power allocation strategy enforces a minimum rate constraint for the weak NOMA user, ensuring consistent quality of service. Extensive simulations over multiple blockage scenarios and signal to noise (SNR) conditions validate the effectiveness of the proposed solution. Notably, under severe blockage, the system achieves up to a 400% sensing rate enhancement at 15 dB SNR, with only a 20% reduction in communication rate. These results corroborate the system's ability to adapt and optimize joint sensing-communication performance in practical deployment environments.

Mobility-Aware Localization in mmWave Channel: Adaptive Hybrid Filtering Approach

Precise user localization and tracking enhances energy-efficient and ultra-reliable low latency applications in the next generation wireless networks. In addition to computational complexity and data association challenges with Kalman-filter localization techniques, estimation errors tend to grow as the user's trajectory speed increases. By exploiting mmWave signals for joint sensing and communication, our approach dispenses with additional sensors adopted in most techniques while retaining high resolution spatial cues. We present a hybrid mobility-aware adaptive framework that selects the Extended Kalman filter at pedestrian speed and the Unscented Kalman filter at vehicular speed. The scheme mitigates data-association problem and estimation errors through adaptive noise scaling, chi-square gating, Rauch-Tung-Striebel smoothing. Evaluations using Absolute Trajectory Error, Relative Pose Error, Normalized Estimated Error Squared and Root Mean Square Error metrics demonstrate roughly 30-60% improvement in their respective regimes indicating a clear advantage over existing approaches tailored to either indoor or static settings.

A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation

In e-commerce, where users face a vast array of possible item choices, recommender systems are vital for helping them discover suitable items they might otherwise overlook. While many recommender systems primarily rely on a user's purchase history, recent multi-behavior recommender systems incorporate various auxiliary user behaviors, such as item clicks and cart additions, to enhance recommendations. Despite their overall performance gains, their effectiveness varies considerably between visited items (i.e., those a user has interacted with through auxiliary behaviors) and unvisited items (i.e., those with which the user has had no such interactions). Specifically, our analysis reveals that (1) existing multi-behavior recommender systems exhibit a significant gap in recommendation quality between the two item types (visited and unvisited items) and (2) achieving strong performance on both types with a single model architecture remains challenging. To tackle these issues, we propose a novel multi-behavior recommender system, MEMBER. It employs a mixture-of-experts framework, with experts designed to recommend the two item types, respectively. Each expert is trained using a self-supervised method specialized for its design goal. In our comprehensive experiments, we show the effectiveness of MEMBER across both item types, achieving up to 65.46% performance gain over the best competitor in terms of Hit Ratio@20.

On the Effect of Uncertainty on Layer-wise Inference Dynamics

Understanding how large language models (LLMs) internally represent and process their predictions is central to detecting uncertainty and preventing hallucinations. While several studies have shown that models encode uncertainty in their hidden states, it is underexplored how this affects the way they process such hidden states. In this work, we demonstrate that the dynamics of output token probabilities across layers for certain and uncertain outputs are largely aligned, revealing that uncertainty does not seem to affect inference dynamics. Specifically, we use the Tuned Lens, a variant of the Logit Lens, to analyze the layer-wise probability trajectories of final prediction tokens across 11 datasets and 5 models. Using incorrect predictions as those with higher epistemic uncertainty, our results show aligned trajectories for certain and uncertain predictions that both observe abrupt increases in confidence at similar layers. We balance this finding by showing evidence that more competent models may learn to process uncertainty differently. Our findings challenge the feasibility of leveraging simplistic methods for detecting uncertainty at inference. More broadly, our work demonstrates how interpretability methods may be used to investigate the way uncertainty affects inference.

'Hello, World!': Making GNNs Talk with LLMs

While graph neural networks (GNNs) have shown remarkable performance across diverse graph-related tasks, their high-dimensional hidden representations render them black boxes. In this work, we propose Graph Lingual Network (GLN), a GNN built on large language models (LLMs), with hidden representations in the form of human-readable text. Through careful prompt design, GLN incorporates not only the message passing module of GNNs but also advanced GNN techniques, including graph attention and initial residual connection. The comprehensibility of GLN's hidden representations enables an intuitive analysis of how node representations change (1) across layers and (2) under advanced GNN techniques, shedding light on the inner workings of GNNs. Furthermore, we demonstrate that GLN achieves strong zero-shot performance on node classification and link prediction, outperforming existing LLM-based baseline methods.

A Domain-Agnostic Scalable AI Safety Ensuring Framework

Ensuring the safety of AI systems has recently emerged as a critical priority for real-world deployment, particularly in physical AI applications. Current approaches to AI safety typically address predefined domain-specific safety conditions, limiting their ability to generalize across contexts. We propose a novel AI safety framework that ensures AI systems comply with any user-defined constraint, with any desired probability, and across various domains. In this framework, we combine an AI component (e.g., neural network) with an optimization problem to produce responses that minimize objectives while satisfying user-defined constraints with probabilities exceeding user-defined thresholds. For credibility assessment of the AI component, we propose internal test data, a supplementary set of safety-labeled data, and a conservative testing methodology that provides statistical validity of using internal test data. We also present an approximation method of a loss function and how to compute its gradient for training. We mathematically prove that probabilistic constraint satisfaction is guaranteed under specific, mild conditions and prove a scaling law between safety and the number of internal test data. We demonstrate our framework's effectiveness through experiments in diverse domains: demand prediction for production decision, safe reinforcement learning within the SafetyGym simulator, and guarding AI chatbot outputs. Through these experiments, we demonstrate that our method guarantees safety for user-specified constraints, outperforms for up to several order of magnitudes existing methods in low safety threshold regions, and scales effectively with respect to the size of internal test data.