This paper describes the system developed by the YNU-HPCC team for SemEval-2026 Task 1 (Humor Generation). The task aims to generate humorous texts from given news headlines or from two unrelated words. The core challenge lies in enabling Large Language Models (LLMs) to understand human humor and align with specific humorous styles. We investigated two approaches: fine-tuning with Proximal Policy Optimization (PPO) and in-context learning with LLMs. We also employed Qwen-Max to evaluate the quality of the generated texts. In the PPO experiments, we constructed a hybrid reward model to align with humor. For our final submission based on LLMs, we used multiple advanced LLMs, along with customized few-shot prompts and a small set of gold samples, to effectively guide the models in generating jokes that resonate with human humor. Experimental results show that our system achieves competitive performance, ranking 4th in the English track, 2nd in the Chinese track, and 2nd in the Spanish track.

High-resolution Multimodal Large Language Models (MLLMs) face prohibitive computational costs during inference due to the explosion of visual tokens. Existing acceleration strategies, such as token pruning or layer sparsity, suffer from severe "backbone dependency", performing well on Vicuna or Mistral architectures (e.g., LLaVA) but causing significant performance degradation when transferred to architectures like Qwen. To address this, we leverage truncated matrix entropy to uncover a universal three-stage inference lifecycle, decoupling visual redundancy into universal Intrinsic Visual Redundancy (IVR) and architecture-dependent Secondary Saturation Redundancy (SSR). Guided by this insight, we propose HalfV, a framework that first mitigates IVR via a unified pruning strategy and then adaptively handles SSR based on its specific manifestation. Experiments demonstrate that HalfV achieves superior efficiency-performance trade-offs across diverse backbones. Notably, on Qwen25-VL, it retains 96.8% performance at a 4.1× FLOPs speedup, significantly outperforming state-of-the-art baselines. Our code is available at https://github.com/civilizwa/HalfV.

The audio watermarking technique embeds messages into audio and accurately extracts messages from the watermarked audio. Traditional methods develop algorithms based on expert experience to embed watermarks into the time-domain or transform-domain of signals. With the development of deep neural networks, deep learning-based neural audio watermarking has emerged. Compared to traditional algorithms, neural audio watermarking achieves better robustness by considering various attacks during training. However, current neural watermarking methods suffer from low capacity and unsatisfactory imperceptibility. Additionally, the issue of watermark locating, which is extremely important and even more pronounced in neural audio water- marking, has not been adequately studied. In this paper, we design a dual-embedding wa- termarking model for efficient locating. We also consider the impact of the attack layer on the invertible neural network in robustness training, improving the model to enhance both its reasonableness and stability. Experiments show that the proposed model, IDEAW, can withstand various attacks with higher capacity and more efficient locating ability compared to existing methods.