Although debiased large language models (LLMs) excel at handling known or low-bias prompts, they often fail on unfamiliar and high-bias prompts.We demonstrate via out-ofdistribution (OOD) detection that these highbias prompts cause a distribution shift, degrading static model performance.To enable realtime correction, we propose CAP-TTA, a testtime adaptation framework.CAP-TTA triggers context-aware LoRA updates only when a bias-risk score exceeds a set threshold.By utilizing an offline precomputed diagonal preconditioner, it ensures fast and stable optimization.Across multiple benchmarks and human evaluations, CAP-TTA effectively reduces toxicity/bias score with significantly lower latency than standard optimization methods (e.g., AdamW or SGD).Furthermore, it prevents catastrophic forgetting, and substantially improves narrative fluency over state-of-the-art baselines without compromising debiasing performance.

The rateless code is widely used as a forward error correction solution. This paper proposes a novel rateless coding scheme with a Gradually Incremental Degree (GID) which is based on Robust Soliton Distribution used in Luby Transform (LT) code. With the feedback information, the proposed scheme significantly reduces the average degree of encoding symbols, as well as maintains the performance of the rateless code. Moreover, a degree distribution of the proposed rateless coding scheme is addressed to reduce the computational complexity. Simulation results show that the proposed rateless coding scheme with GID achieves a lower average degree than that of the standard LT code under the same coding overhead.

When a fault-tolerant robot fails, its fault responsive system detects and identifies the failure. During the recovery process, reconfiguration of the system isolates the fault, and a new system model and a suitable controller attempt to completely compensate for the faulty condition without interrupting the robot's operation. In this paper, the authors address the recovery process for fault-tolerant serial robots when they experience actuator failure. For this purpose, the authors consider three controllers based on PID feedback, sliding control, and parameter adaptation methods. It is shown that the sliding control implemented with a boundary layer reduces the system errors efficiently when the errors are large, and the controller behaves like an ordinary PID feedback as the errors get smaller. Additionally, when failures cause uncertainty in system parameters, inclusion of parameter identification capability in the controller design is suggested. Although the work is valid for a general robot, simulation results are presented on a four-axis robot.>