The proliferation of AI-Generated Content (AIGC), especially deepfake videos, poses a severe threat to social trust by enabling fraud, privacy violations and disinformation. Existing AI-generated video detection (AGVD) benchmarks focus on open-source model generated videos, yet commercial closed-source models produce more realistic, temporally coherent videos that are underexplored in detection research. To fill this gap, we present Chameleon, a commercial-grade dataset with 1,700 AI-generated videos from 600 real-world sources across three key domains (News, Speech, Recommendation), featuring high resolution, rich annotations and 3D consistency metrics for dynamic scene spatial coherence, shifting detection from face-centric forgery to holistic scene forensics. This benchmark assesses models on two core tasks: accurate AI video detection in real-world conditions and forensic backtracking of original sources. Experimental results reveal critical limitations of existing methods in detecting and backtracking high-fidelity, spatiotemporally consistent videos from commercial closed-source models, highlighting current methods’ flawed forensic reasoning and establishing Chameleon as a vital challenge for AIGC security research. The code and data are available at https://github.com/lxixim/Chameleon.

Cross-lingual Aspect-based Sentiment Analysis (XABSA) aims to extract aspect-level sentiments across multiple languages. This task typically relies on source language data to train models and transfer them to target languages, so it faces significant challenges such as data scarcity and language disparities. To this end, this study proposes a code-Mixed In-conteXt lEaRning (MIXER). We design four kinds of demonstration retrieval libraries to introduce Code-mixed In-Context Demonstrations (CICD), which use the code-mixed mechanism to integrate the features of the target language and enrich the target language's knowledge while retaining the source language's knowledge. Language-Specific tags (LST) are introduced to enhance the model's understanding of multilingual demonstrations. To validate the effectiveness of MIXER, we conduct extensive experiments on the SemEval-2016 dataset, comparing its performance against existing XABSA methods. The experimental results show that MIXER performs better than existing XABSA methods with average F1 scores on the Mistral and Llama3 improved by 1.59% and 1.44%, respectively, highlighting its potential for broader multilingual applications.

Understanding and modeling time remains a key challenge in today's video understanding systems. With language playing a central role in driving powerful generalization, foundational video-language models must inherently capture a sense of temporality. However, Video-text retrieval faces three challenges: Parameter efficiency bottlenecks (traditional prompt learning requires adjusting over 10 % of parameters, leading to a sharp increase in computational costs), limitations of static course strategies (manually defined difficulty thresholds cause pseudo-label biases in highly abstract actions), and insufficient adaptability to heterogeneous architectures (cross-modal hybrid expert frameworks rely on shared weights, restricting the flexible combination of heterogeneous models such as CLIP and ViT). To address these challenges, this paper proposes the Dynamic Curriculum Learning with Adaptive Prompt Mixture of Experts (DynaCLIP) framework. Through the collaborative optimization of the Adaptive Prompt Mixture of Experts (APMoE) module and dynamic curriculum learning, the parameter efficiency and dynamic adaptability are significantly improved. APMoE adopts orthogonal initialization expert pools and cross-modal routing networks. It freezes the pre-trained backbone networks (such as CLIP/BERT) on the premise of fine-tuning only less than 1 % of parameters (static expert pool + dynamic prompt generator), and generates instance-aware text encoder prompts by dynamically selecting Top-k experts and weighted fusion. Here, these instanceaware prompts are implemented as continuous soft prompts in the embedding space, rather than discrete natural language text. Meanwhile, a two-dimensional difficulty quantification system is constructed based on the verb abstraction level (VerbNet three-level classification) and BERT semantic similarity. Combined with the improved binary search strategy to balance high-confidence samples and diversity exploration, a cognitiveinspired course learning mechanism is formed. Experiments show that DynaCLIP achieves 7.4 % R@1 on the TEMPO dataset, 67.5 % accuracy in temporal inference. The code is available at: https://github.com/18162195164/DynaCLIP.

Nested Named Entity Recognition (NNER) focuses on addressing overlapped entity recognition. Compared to Flat Named Entity Recognition (FNER), annotated resources are scarce in the corpus for NNER. Data augmentation is an effective approach to address the insufficient annotated corpus. However, there is a significant lack of exploration in data augmentation methods for NNER. Due to the presence of nested entities in NNER, existing data augmentation methods cannot be directly applied to NNER tasks. Therefore, in this work, we focus on data augmentation for NNER and resort to more expressive structures, Composited-Nested-Label Classification (CNLC) in which constituents are combined by nested-word and nested-label, to model nested entities. The dataset is augmented using the Composited-Nested-Learning (CNL). In addition, we propose the Confidence Filtering Mechanism (CFM) for a more efficient selection of generated data. Experimental results demonstrate that this approach results in improvements in ACE2004 and ACE2005 and alleviates the impact of sample imbalance.