Multimodal deepfake technologies have emerged rapidly in recent years, with wide application prospects in various fields. The conventional single-training paradigm with inherent limited generalization illustrates inadequate for addressing the continuous evolution of multimodal deepfakes. However, fine-tuning a model with new deepfake data faces past forgery patterns loss and the significant domain shift in diverse novel multimodal deepfake technologies. To address these issues, we propose a novel Quantum State Analytic Incremental Adaptability Learning method (Qsaint) for multimodal deepfake detection. To stabilize prior deepfake memory, Qsaint recursively learns detection-label mapping relations for the new deepfakes artifact with a closed-form solution, preserving the distribution memory from the historical deepfake domains without accessing previous videos. During incremental learning stages, we propose a deepfake quantum state adaptability module inspired by quantum information science. It adapts to the new forgery states and aligns them with the historical deepfake knowledge through cooling and evolution operations, eliminating deepfake domain shift issues. Comprehensive experiments demonstrate that Qsaint significantly mitigates the memory interference of historical deepfakes, effectively balancing the adaptability for new forgery tasks with the memorization of known deepfake patterns.

Time series forecasting faces significant challenges due to non-stationary components that obscure underlying patterns. While Transformer-based models are effective at capturing stationary components, they struggle with non-stationary dynamics and multivariate dependencies. In this paper, we proposeFreqEvo, a lightweight Frequency Domain Feature Enhancement module for time series forecasting.FreqEvoprogressively filters frequency components from high to low amplitude, ensuring the preservation of informative features while reducing noise. By integrating recursive Fourier-based residual modeling and cross-domain attention,FreqEvoeffectively refines low-amplitude frequency features and stabilizes the embeddings, outperforming traditional low-pass filtering and random frequency selection methods in capturing both short-term and long-term dependencies. Experimental results on benchmark datasets demonstrate thatFreqEvooutperforms state-of-the-art (SOTA) models and serves as a plug-and-play module to enhance existing Long-Term Sequence Forecasting (LSTF) models.

Deepfake technology induces substantial societal challenges, establishing deepfake detection as an important area of research. However, existing research mainly relies on target deepfake datasets, which limits its generalizability across out-of-distribution tasks to some extent. Also, it often emphasizes visual modalities while neglecting the complementary information of the auditory data. Their autoregressive-based strategies also introduce long-term information interference, further constraining the detection performance. Consequently, the potential to exploit complementary relations between visual and auditory modalities and to leverage strongly correlated short-range information remains underexplored for the detection task. To address these challenges, this paper introduces Self-BiSterm, a novel self-supervised learning framework for deepfake detection. First, we propose a bidirectional synchronization distribution modeling mechanism, which calculates inconsistent distributions for video-to-audio and audio-to-video scenarios. This mechanism effectively measures audio-visual inconsistencies, improving the model's generalization performance in practical applications. Second, to mitigate the issue of long-term information distortion, we develop a short-term temporal dependency module to estimate the adjacent local receptive fields. This module facilitates the estimation of subsequent distributions by capturing short-term temporal dependencies with high precision. The effectiveness of the proposed Self-BiSterm framework is validated on various benchmarks, demonstrating superior performance compared to existing methods.

Self-improving alignment leveraging large language models (LLMs) to automatically generate synthetic preference data has garnered significant attention as a means of reducing reliance on human labelers. These methods typically employ the LLM-as-a-judge mechanism, where the LLM generates responses and then employs itself to judge which response best aligns with the given prompt for curating the binary self-preferred dataset. However, these methods encounter two major challenges: (1) LLM-as-a-judge often produces error-prone evaluations, resulting in low-quality preference annotation, and (2) their optimization strategies often overlook the strength of preferences within binary pairs, leading to overfitting. This paper proposes a novel method, Preference-Strength-aware Optimization (PSO), to address these issues. Specifically, PSO frames the preference annotation process as a judgment token prediction task using the generative preference model to produce reliable judgments. The predicted judgment token indicates the preferred response and its corresponding probability reflects the disparity between responses, referred to as preference strength. Based on this strength, we introduce a new preference-strength-aware loss to adaptively reweight the impact of different response pairs on optimization, concentrating the model's learning on high-quality response pairs. Our experiments demonstrate that PSO significantly improves performance in preference benchmarks, achieving stronger alignment with human preferences, reducing verbose responses, and mitigating overfitting. Furthermore, PSO exhibits robust generalization and sample efficiency, offering a scalable and promising solution for LLM alignment without relying on human-annotated preferences.

Large Language Model (LLM) agents frequently produce sub-optimal actions when tackling complex, multi-step decision-making tasks. Employing self-critique to identify flaws and suggest enhancements is an effective strategy for refining actions. Although trajectory-level critique is commonly employed, it often fails to identify flawed steps accurately. In this paper, we introduce SLSC-MCTS, a method that integrates Monte Carlo Tree Search with Step-Level Self-Critique to enhance LLM agents during both testing and self-training phases. During decision tree expansion with SLSC-MCTS, the LLM agent initially generates an action, receives environmental feedback, and subsequently generates further actions via self-critique and refinement. Through multiple episodes of SLSC-MCTS, LLM agents can effectively utilize step-level critiques while disregarding ineffective ones based on node values, thereby incorporating the critiques more robustly. Additionally, our method further empowers LLM agents in a self-training manner, collecting training data from the constructed decision tree to iteratively fine-tune the LLM agents. The self-training data gathered via SLSC-MCTS is diverse and high-quality, which further enhances the reasoning, critiquing, and refining abilities of LLM agents. Experimental results demonstrate that SLSC-MCTS significantly improves LLM agents during testing, surpassing state-of-the-art baselines and achieving shorter task completion trajectories across information retrieval benchmarks such as WebShop and HotPotQA. After three iterations of self-training, LLM agents established by Llama-3.1-8B-Instruct show substantial improvement, even surpassing human experts in WebShop.

Radar object detection refers to identify objects from radar data, and the topic has received increasing interest during the last years, due to the appealing property of radar imaging and evident applications. However, the detection performance heavily relied on semantic information extraction, which is a great challenge in practical settings. Moreover, although remarkable progress has been made, most previous attempts are restrained from the essentially limited property of the employed single modality. Inspired by the recent success of cross-modality deep learning, we propose a novel cross-modality deep learning framework for the radar object detection task using the Squeeze-and-Excitation network, aiming to provide more powerful feature representation. Moreover, a novel noisy detection approach is also explored in our study, to increase the model's ability to handle with noise. Finally, a novel weighted location fusion strategy is introduced in our framework, to improve the detection performance further. To empirically investigate the effectiveness of the proposed framework, we conduct extensive experiments on the 2021 ICMR ROD challenge. The obtained results suggest that our framework outperforms related approaches. Our method ranks as the 3rd place on the final leaderboard, with an average precision (AP) percentage of 76.1. Models and codes are available at https://github.com/sunpengliang/modelConfusion.