Recent research has devoted considerable effort to verifying the intermediate reasoning steps of chain-of-thought (CoT) trajectories using process reward models (PRMs) and other verifier models.However, training a PRM typically requires human annotators to assign reward scores to each reasoning step, which is both costly and time-consuming.Existing automated approaches, such as Monte Carlo (MC) estimation, also demand substantial computational resources due to repeated LLM rollouts.To overcome these limitations, we propose contrastive pointwise mutual information (CPMI), a novel automatic reward labeling method that leverages the model's internal probability to infer step-level supervision while significantly reducing the computational burden of annotating dataset.CPMI quantifies how much a reasoning step increases the mutual information between the step and the correct target answer relative to hard-negative alternatives.This contrastive signal serves as a proxy for the step's contribution to the final solution and yields a reliable reward.The experimental results show that CPMI-based labeling reduces dataset construction time by 84% and token generation by 98% compared to MC estimation, while achieving higher accuracy on process-level evaluations and mathematical reasoning benchmarks.The code is available at https://github.com/nakyungLee20/CPMI.

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 wave-form 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.

Handover (HO) is essential for ensuring seamless connectivity but poses significant challenges, such as HO interruption time, frequent HO, and HO failures, especially in dense urban environments with millimeter wave small-cell deployments. To address these challenges, this paper proposes a novel HO procedure that leverages a digital twin of the radio access network (RAN Twin), which is compatible with the 3GPP New Radio standard, Release 18. The proposed RAN Twin integrates multimodal sensing data and historical network information to generate an optimal list of candidate cells and share user equipment context data. This enables early synchronization with the target HO cell, minimizing interruption time. Simulation results demonstrate that the proposed approach outperforms the baseline 3GPP HO in terms of latency and HO rate, underscoring its potential to enhance overall network performance.

In recent years, the connected automotive industry has grown explosively. Various connected car services are emerging, such as remote vehicle diagnostics, driver's health monitoring, infotainment, and vehicle safety management. As a result, the number and type of vehicle data are increasing tremendously day by day. However, existing connected automotive solutions have a limitation in that each company manages its own closed data silos. This restricts connected car services from using data sources in various domains. Hence, we propose the GS1 Connected Car, an integrated vehicle information platform, and its ecosystem. We suggest GS1-based automotive data standards for not only in-vehicle data but also all the automotive-related data generated during the lifecycle of vehicles. We provide standardized data collection to EPCIS, the discovery of global automotive services using ONS, IoT Mash-up service between an in-car dashboard platform and IoT devices, video infotainment called GS1 video, and automotive lifecycle management application. We have implemented our platform in a real car by developing an Android-based vehicle dashboard, including service discovery, Mash-up services, and GS1 Video. Also, a mobile application for lifecycle management and Amazon skills for collecting driver's information are developed. Our demonstration and case study show the feasibility of the proposed platform, widening the scope of future connected car services.