Reinforcement learning has emerged as a paradigm for post-training large language models, boosting their reasoning capabilities. Such approaches compute an advantage value for each sample, reflecting better or worse performance than expected, thereby yielding both positive and negative signals for training. However, existing approaches often mix them indiscriminately, especially in the early stages, leading to ambiguous guidance and limited gains. To address this issue, we propose CAPO (Curriculum Advantage Policy Optimization), an adaptive curriculum mechanism based on advantage signals. The proposed mechanism bootstraps imitation learning with positive-only advantage samples to establish robust foundations, and subsequently introduces negative signals to cultivate discriminative capabilities, thereby improving generalization across complex scenarios. Compatible with diverse optimization methods including GRPO, PPO, RLOO, and Reinforce++, our method consistently achieves stable and significant improvements in mathematical reasoning tasks, and further generalizes effectively to multimodal Graphical User Interface (GUI) reasoning scenarios, establishing itself as a versatile and robust optimization paradigm.

WCET (Worst-Case Execution Time) estimation on multicore architecture is particularly challenging mainly due to the complex accesses over cache shared by multiple cores. Existing analysis identifies possible contentions between parallel tasks by leveraging the partial order of the tasks or their program regions. Unfortunately, they overestimate the number of cache misses caused by a remote block access without considering the actual cache state and the number of accesses. This paper reports a new analysis for inter-core cache contention. Based on the order of program regions in a task, we first identify memory references that could be affected if a remote access occurs in a region. Afterwards, a fine-grained contention analysis is constructed that computes the number of cache misses based on the access quantity of local and remote blocks. We demonstrate that the overall inter-core cache interference of a task can be obtained via dynamic programming. Experiments show that compared to existing methods, the proposed analysis reduces inter-core cache interference and WCET estimations by$\mathbf{5 2. 3 1 \%}$and$\mathbf{8. 9 4 \%}$on average, without significantly increasing computation overhead.

Directed Acyclic Graph (DAG) models are extensively utilized across fields such as automotive, wireless communication, and deep learning, to capture the inherent functional dependencies. Topology of DAG has a significant impact on the performance of scheduling and resource management algorithms applied to it. Hence, it is imperative to generate all DAG topologies within the parameter ranges pertinent to an application domain, for impartial evaluation of such algorithms. Unfortunately, the existing DAG generators that are capable of offering full topology coverage have limited scalability and controllable parameters. This work reports open-source FT-DAG, an efficient and formally verified full-topology DAG generator that is able to control all major parameters, including the longest length, shortest length, width, jump layer, jump level, in-degree, out-degree, shape value as well as the number of nodes and edges. Experiments show that when the number of nodes is larger than 20, FT-DAG provides at least two orders of magnitude speedup compared to the state of the art and more orders to other generators. FT-DAG scales to 100 nodes in a typical industrial case study within hours.

With recent advances on cellular technologies pushing the boundary of cellular performance, cellular reliability has become a key concern of their adoption and deployment. To fully understand cellular reliability, we work with a major Android phone vendor, Xiaomi, to conduct a long-term (2020-2024) and large-scale (involving 123M users) measurement study in China, with coarse-grained general statistics and fine-grained sampling diagnostics. Our measurement reveals contrasting evolution trends of cellular failures in different stages of the data connection: in the past five years, failures after connection establishment decrease remarkably (by 29%), while failures during connection setup exhibit a sharp increase (by 38%). Our analysis illustrates that the contrast stems from the joint impact of multiple stakeholders, including ISPs’ increasing deployment of 5G base stations, 5G infrastructure upgrade from NSA (Non-Standalone) to SA (Standalone) mode, software defects coming from Android’s adaptation to new cellular technologies, and so forth. Our work provides actionable insights for improving cellular reliability at scale. More importantly, we have built on our insights to develop enhancements that effectively address cellular reliability issues with remarkable real-world impact—our optimizations have reduced 38% cellular connection failures for 5G phones and 31% failure recovery time across all phones.

Summary This paper uses a bionic lower limb rehabilitation mechanism combined with a running frame structure to simulate human gait movement for lower limb rehabilitation training. The vibration excited by the vertical vibration module causes the muscle to oscillate, and the mechanical vibration excites the neuromuscular system to obtain corresponding rehabilitation functions. The robot system is modeled in three dimensions, and the static analysis and modal analysis of the running frame are carried out. The mechanism and application of vertical vibration in the key technology are clarified, and the vibration element in the vertical vibration device is analyzed. The vibration theory is deduced, and the vibration displacement figure is drawn using simulation software. The related cooperative vibration spring has also been analyzed with single‐degree‐of‐freedom damping vibration, and the spring's center of mass movement, momentum, and position changes over time are illustrated. The design of the robot system solves the current situation of single movement of the lower limb rehabilitation robot and unsatisfactory rehabilitation effect, laying a foundation for the practical application of the subsequent lower limb rehabilitation robot system.