AdaR1: From Long-CoT to Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization

Recently, long-thought reasoning models achieve strong performance on complex reasoning tasks, but often incur substantial inference overhead, making efficiency a critical concern. Our empirical analysis reveals that the benefit of using Long-CoT varies across problems: while some problems require elaborate reasoning, others show no improvement, or even degraded accuracy. This motivates adaptive reasoning strategies that tailor reasoning depth to the input. However, prior work primarily reduces redundancy within long reasoning paths, limiting exploration of more efficient strategies beyond the Long-CoT paradigm. To address this, we propose a novel two-stage framework for adaptive and efficient reasoning. First, we construct a hybrid reasoning model by merging long and short CoT models to enable diverse reasoning styles. Second, we apply bi-level preference training to guide the model to select suitable reasoning styles (group-level), and prefer concise and correct reasoning within each style group (instance-level). Experiments demonstrate that our method significantly reduces inference costs compared to other baseline approaches, while maintaining performance. Notably, on five mathematical datasets, the average length of reasoning is reduced by more than 50%, highlighting the potential of adaptive strategies to optimize reasoning efficiency in large language models. Our code is coming soon at https://github.com/StarDewXXX/AdaR1

EfficientHuman: Efficient Training and Reconstruction of Moving Human using Articulated 2D Gaussian

3D Gaussian Splatting (3DGS) has been recognized as a pioneering technique in scene reconstruction and novel view synthesis. Recent work on reconstructing the 3D human body using 3DGS attempts to leverage prior information on human pose to enhance rendering quality and improve training speed. However, it struggles to effectively fit dynamic surface planes due to multi-view inconsistency and redundant Gaussians. This inconsistency arises because Gaussian ellipsoids cannot accurately represent the surfaces of dynamic objects, which hinders the rapid reconstruction of the dynamic human body. Meanwhile, the prevalence of redundant Gaussians means that the training time of these works is still not ideal for quickly fitting a dynamic human body. To address these, we propose EfficientHuman, a model that quickly accomplishes the dynamic reconstruction of the human body using Articulated 2D Gaussian while ensuring high rendering quality. The key innovation involves encoding Gaussian splats as Articulated 2D Gaussian surfels in canonical space and then transforming them to pose space via Linear Blend Skinning (LBS) to achieve efficient pose transformations. Unlike 3D Gaussians, Articulated 2D Gaussian surfels can quickly conform to the dynamic human body while ensuring view-consistent geometries. Additionally, we introduce a pose calibration module and an LBS optimization module to achieve precise fitting of dynamic human poses, enhancing the model's performance. Extensive experiments on the ZJU-MoCap dataset demonstrate that EfficientHuman achieves rapid 3D dynamic human reconstruction in less than a minute on average, which is 20 seconds faster than the current state-of-the-art method, while also reducing the number of redundant Gaussians.

A comprehensive review of remote sensing in wetland classification and mapping

Wetlands constitute critical ecosystems that support both biodiversity and human well-being; however, they have experienced a significant decline since the 20th century. Back in the 1970s, researchers began to employ remote sensing technologies for wetland classification and mapping to elucidate the extent and variations of wetlands. Although some review articles summarized the development of this field, there is a lack of a thorough and in-depth understanding of wetland classification and mapping: (1) the scientific importance of wetlands, (2) major data, methods used in wetland classification and mapping, (3) driving factors of wetland changes, (4) current research paradigm and limitations, (5) challenges and opportunities in wetland classification and mapping under the context of technological innovation and global environmental change. In this review, we aim to provide a comprehensive perspective and new insights into wetland classification and mapping for readers to answer these questions. First, we conduct a meta-analysis of over 1,200 papers, encompassing wetland types, methods, sensor types, and study sites, examining prevailing trends in wetland classification and mapping. Next, we review and synthesize the wetland features and existing data and methods in wetland classification and mapping. We also summarize typical wetland mapping products and explore the intrinsic driving factors of wetland changes across multiple spatial and temporal scales. Finally, we discuss current limitations and propose future directions in response to global environmental change and technological innovation. This review consolidates our understanding of wetland remote sensing and offers scientific recommendations that foster transformative progress in wetland science.

Disentangling Instruction Influence in Diffusion Transformers for Parallel Multi-Instruction-Guided Image Editing

Instruction-guided image editing enables users to specify modifications using natural language, offering more flexibility and control. Among existing frameworks, Diffusion Transformers (DiTs) outperform U-Net-based diffusion models in scalability and performance. However, while real-world scenarios often require concurrent execution of multiple instructions, step-by-step editing suffers from accumulated errors and degraded quality, and integrating multiple instructions with a single prompt usually results in incomplete edits due to instruction conflicts. We propose Instruction Influence Disentanglement (IID), a novel framework enabling parallel execution of multiple instructions in a single denoising process, designed for DiT-based models. By analyzing self-attention mechanisms in DiTs, we identify distinctive attention patterns in multi-instruction settings and derive instruction-specific attention masks to disentangle each instruction's influence. These masks guide the editing process to ensure localized modifications while preserving consistency in non-edited regions. Extensive experiments on open-source and custom datasets demonstrate that IID reduces diffusion steps while improving fidelity and instruction completion compared to existing baselines. The codes will be publicly released upon the acceptance of the paper.

A Theoretical Analysis of Analogy-Based Evolutionary Transfer Optimization

Evolutionary transfer optimization (ETO) has been gaining popularity in research over the years due to its outstanding knowledge transfer ability to address various challenges in optimization. However, a pressing issue in this field is that the invention of new ETO algorithms has far outpaced the development of fundamental theories needed to clearly understand the key factors contributing to the success of these algorithms for effective generalization. In response to this challenge, this study aims to establish theoretical foundations for analogy-based ETO, specifically to support various algorithms that frequently reference a key concept known as similarity. First, we introduce analogical reasoning and link its subprocesses to three key issues in ETO. Then, we develop theories for analogy-based knowledge transfer, rooted in the principles that underlie the subprocesses. Afterwards, we present two theorems related to the performance gain of analogy-based knowledge transfer, namely unconditionally nonnegative performance gain and conditionally positive performance gain, to theoretically demonstrate the effectiveness of various analogy-based ETO methods. Last but not least, we offer a novel insight into analogy-based ETO that interprets its conditional superiority over traditional evolutionary optimization through the lens of the no free lunch theorem for optimization.

Robust Safety Critical Control Under Multiple State and Input Constraints: Volume Control Barrier Function Method

In this paper, the safety-critical control problem for uncertain systems under multiple control barrier function (CBF) constraints and input constraints is investigated. A novel framework is proposed to generate a safety filter that minimizes changes to reference inputs when safety risks arise, ensuring a balance between safety and performance. A nonlinear disturbance observer (DOB) based on the robust integral of the sign of the error (RISE) is used to estimate system uncertainties, ensuring that the estimation error converges to zero exponentially. This error bound is integrated into the safety-critical controller to reduce conservativeness while ensuring safety. To further address the challenges arising from multiple CBF and input constraints, a novel Volume CBF (VCBF) is proposed by analyzing the feasible space of the quadratic programming (QP) problem. % ensuring solution feasibility by keeping the volume as a positive value. To ensure that the feasible space does not vanish under disturbances, a DOB-VCBF-based method is introduced, ensuring system safety while maintaining the feasibility of the resulting QP. Subsequently, several groups of simulation and experimental results are provided to validate the effectiveness of the proposed controller.

MSCMHMST: A traffic flow prediction model based on Transformer

This study proposes a hybrid model based on Transformers, named MSCMHMST, aimed at addressing key challenges in traffic flow prediction. Traditional single-method approaches show limitations in traffic prediction tasks, whereas hybrid methods, by integrating the strengths of different models, can provide more accurate and robust predictions. The MSCMHMST model introduces a multi-head, multi-scale attention mechanism, allowing the model to parallel process different parts of the data and learn its intrinsic representations from multiple perspectives, thereby enhancing the model's ability to handle complex situations. This mechanism enables the model to capture features at various scales effectively, understanding both short-term changes and long-term trends. Verified through experiments on the PeMS04/08 dataset with specific experimental settings, the MSCMHMST model demonstrated excellent robustness and accuracy in long, medium, and short-term traffic flow predictions. The results indicate that this model has significant potential, offering a new and effective solution for the field of traffic flow prediction.

Sparse Alignment Enhanced Latent Diffusion Transformer for Zero-Shot Speech Synthesis

While recent zero-shot text-to-speech (TTS) models have significantly improved speech quality and expressiveness, mainstream systems still suffer from issues related to speech-text alignment modeling: 1) models without explicit speech-text alignment modeling exhibit less robustness, especially for hard sentences in practical applications; 2) predefined alignment-based models suffer from naturalness constraints of forced alignments. This paper introduces \textit{S-DiT}, a TTS system featuring an innovative sparse alignment algorithm that guides the latent diffusion transformer (DiT). Specifically, we provide sparse alignment boundaries to S-DiT to reduce the difficulty of alignment learning without limiting the search space, thereby achieving high naturalness. Moreover, we employ a multi-condition classifier-free guidance strategy for accent intensity adjustment and adopt the piecewise rectified flow technique to accelerate the generation process. Experiments demonstrate that S-DiT achieves state-of-the-art zero-shot TTS speech quality and supports highly flexible control over accent intensity. Notably, our system can generate high-quality one-minute speech with only 8 sampling steps. Audio samples are available at https://sditdemo.github.io/sditdemo/.

CAML: Collaborative Auxiliary Modality Learning for Multi-Agent Systems

Multi-modality learning has become a crucial technique for improving the performance of machine learning applications across domains such as autonomous driving, robotics, and perception systems. While existing frameworks such as Auxiliary Modality Learning (AML) effectively utilize multiple data sources during training and enable inference with reduced modalities, they primarily operate in a single-agent context. This limitation is particularly critical in dynamic environments, such as connected autonomous vehicles (CAV), where incomplete data coverage can lead to decision-making blind spots. To address these challenges, we propose Collaborative Auxiliary Modality Learning ($\textbf{CAML}$), a novel multi-agent multi-modality framework that enables agents to collaborate and share multimodal data during training while allowing inference with reduced modalities per agent during testing. We systematically analyze the effectiveness of $\textbf{CAML}$ from the perspective of uncertainty reduction and data coverage, providing theoretical insights into its advantages over AML. Experimental results in collaborative decision-making for CAV in accident-prone scenarios demonstrate that \ours~achieves up to a ${\bf 58.13}\%$ improvement in accident detection. Additionally, we validate $\textbf{CAML}$ on real-world aerial-ground robot data for collaborative semantic segmentation, achieving up to a ${\bf 10.61}\%$ improvement in mIoU.

AUKT: Adaptive Uncertainty-Guided Knowledge Transfer with Conformal Prediction

Knowledge transfer between teacher and student models has proven effective across various machine learning applications. However, challenges arise when the teacher's predictions are noisy, or the data domain during student training shifts from the teacher's pretraining data. In such scenarios, blindly relying on the teacher's predictions can lead to suboptimal knowledge transfer. To address these challenges, we propose a novel and universal framework, Adaptive Uncertainty-guided Knowledge Transfer ($\textbf{AUKT}$), which leverages Conformal Prediction (CP) to dynamically adjust the student's reliance on the teacher's guidance based on the teacher's prediction uncertainty. CP is a distribution-free, model-agnostic approach that provides reliable prediction sets with statistical coverage guarantees and minimal computational overhead. This adaptive mechanism mitigates the risk of learning undesirable or incorrect knowledge. We validate the proposed framework across diverse applications, including image classification, imitation-guided reinforcement learning, and autonomous driving. Experimental results consistently demonstrate that our approach improves performance, robustness and transferability, offering a promising direction for enhanced knowledge transfer in real-world applications.