Despite the remarkable success of Large Language Models (LLMs), evaluating their outputs' quality regarding preference remains a critical challenge. While existing works usually leverage a strong LLM as the judge for comparing LLMs' response pairwisely, such a single-evaluator approach is vulnerable to cyclic preference, i.e., output A is better than B, B than C, but C is better than A, causing contradictory evaluation results. To address this, we introduce PGED (Preference Graph Ensemble and Denoise), a novel approach that leverages multiple model-based evaluators to construct preference graphs, and then ensembles and denoises these graphs for acyclic, non-contradictory evaluation results. We provide theoretical guarantees for our framework, demonstrating its efficacy in recovering the ground truth preference structure. Extensive experiments on ten benchmarks demonstrate PGED 's superiority in three applications: 1) model ranking for evaluation, 2) response selection for test-time scaling, and 3) data selection for model fine-tuning. Notably, PGED combines small LLM evaluators (e.g., Llama3-8B, Mistral-7B, Qwen2-7B) to outperform strong ones (e.g., Qwen2-72B), showcasing its effectiveness in enhancing evaluation reliability and improving model performance.

Learning from preference-based feedback has become an effective approach for aligning LLMs across diverse tasks.However, highquality human-annotated preference data remains expensive and scarce.Existing methods address this challenge through either selfrewarding, which scales by using purely AIgenerated labels but risks unreliability, or active learning, which ensures quality through oracle annotation but cannot fully leverage unlabeled data.In this paper, we present COACT, a novel framework that synergistically combines selfrewarding and active learning through strategic human-AI collaboration.COACT leverages self-consistency to identify both reliable selflabeled data and samples that are requiring oracle verification.Additionally, oracle feedback guides the model to generate new instructions within its solvable capability.Evaluated on three reasoning benchmarks across two model families, COACT achieves average improvements of +13.25% on GSM8K, +8.19% on MATH, and +13.16% on WebInstruct, consistently outperforming all baselines.1

Motivated by the remarkable progress of large language models (LLMs) in objective tasks like mathematics and coding, there is growing interest in their potential to simulate human behavior—a capability with profound implications for transforming social science research and customer-centric business insights. However, LLMs often lack a nuanced understanding of human cognition and behavior, limiting their effectiveness in social simulation and personalized applications. We posit that this limitation stems from a fundamental misalignment: standard LLM pretraining on vast, uncontextualized web data does not capture the continuous, situated context of an individual's decisions, thoughts, and behaviors over time. To bridge this gap, we introduce HumanLLM, a foundation model designed for personalized understanding and simulation of individuals. We first construct the Cognitive Genome Dataset, a large-scale corpus curated from real-world user data on platforms like Reddit, Twitter, Blogger, and Amazon. Through a rigorous, multi-stage pipeline involving data filtering, synthesis, and quality control, we automatically extract over 5.5 million user logs to distill rich profiles, behaviors, and thinking patterns. We then formulate diverse learning tasks and perform supervised fine-tuning to empower the model to predict a wide range of individualized human behaviors, thoughts, and experiences. Comprehensive evaluations demonstrate that HumanLLM achieves superior performance in predicting user actions and inner thoughts, more accurately mimics user writing styles and preferences, and generates more authentic user profiles compared to base models. Furthermore, HumanLLM shows significant gains on out-of-domain social intelligence benchmarks, indicating enhanced generalization. This work paves the way for more human-centric AI systems by advancing research in social simulation, developing personalized companions, enabling marketing intelligence through simulated customer feedback, and powering more realistic user simulation for recommender systems.

Concept Bottleneck Models (CBMs) have garnered increasing attention due to their ability to provide concept-based explanations for black-box deep learning models while achieving high final prediction accuracy using human-like concepts. However, the training of current CBMs is heavily dependent on the precision and richness of the annotated concepts in the dataset. These concept labels are typically provided by experts, which can be costly and require significant resources and effort. Additionally, concept saliency maps frequently misalign with input saliency maps, causing concept predictions to correspond to irrelevant input features - an issue related to annotation alignment. To address these limitations, we propose a new framework called SSCBM (Semi-supervised Concept Bottleneck Model). Our SSCBM is suitable for practical situations where annotated data is scarce. By leveraging joint training on both labeled and unlabeled data and aligning the unlabeled data at the concept level, we effectively solve these issues. We proposed a strategy to generate pseudo labels and an alignment loss. Experiments demonstrate that our SSCBM is both effective and efficient. With only 10% labeled data, our model's concept and task accuracy on average across four datasets is only 2.44% and 3.93% lower, respectively, compared to the best baseline in the fully supervised learning setting.

Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a human-understandable concept layer. However, most previous studies focused on cases where the data, including concepts, are clean. In many scenarios, we always need to remove/insert some training data or new concepts from trained CBMs due to different reasons, such as privacy concerns, data mislabelling, spurious concepts, and concept annotation errors. Thus, the challenge of deriving efficient editable CBMs without retraining from scratch persists, particularly in large-scale applications. To address these challenges, we propose Editable Concept Bottleneck Models (ECBMs). Specifically, ECBMs support three different levels of data removal: concept-label-level, concept-level, and data-level. ECBMs enjoy mathematically rigorous closed-form approximations derived from influence functions that obviate the need for re-training. Experimental results demonstrate the efficiency and effectiveness of our ECBMs, affirming their adaptability within the realm of CBMs.

3D point cloud segmentation aims to assign semantic labels to individual points in a scene for fine-grained spatial understanding. Existing methods typically adopt data augmentation to alleviate the burden of large-scale annotation. However, most augmentation strategies only focus on local transformations or semantic recomposition, lacking the consideration of global structural dependencies within scenes. To address this limitation, we propose a graph-guided data augmentation framework with dual-level constraints for realistic 3D scene synthesis. Our method learns object relationship statistics from real-world data to construct guiding graphs for scene generation. Local-level constraints enforce geometric plausibility and semantic consistency between objects, while global-level constraints maintain the topological structure of the scene by aligning the generated layout with the guiding graph. Extensive experiments on indoor and outdoor datasets demonstrate that our framework generates diverse and high-quality augmented scenes, leading to consistent improvements in point cloud segmentation performance across various models. Code is available at: https://github.com/alexander7xu/DualLevelAug

Large language models (LLMs) have shown impressive capabilities across tasks such as mathematics, coding, and reasoning, yet their learning ability, which is crucial for adapting to dynamic environments and acquiring new knowledge, remains underexplored. In this work, we address this gap by introducing a framework inspired by cognitive psychology and education. Specifically, we decompose general learning ability into three distinct, complementary dimensions: *Learning from Instructor* (acquiring knowledge via explicit guidance), *Learning from Concept* (internalizing abstract structures and generalizing to new contexts), and *Learning from Experience* (adapting through accumulated exploration and feedback). We conduct a comprehensive empirical study across the three learning dimensions and identify several insightful findings, such as (i) interaction improves learning; (ii) conceptual understanding is scale-emergent and benefits larger models; and (iii) LLMs are effective few-shot learners but not many-shot learners. Based on our framework and empirical findings, we introduce a benchmark that provides a unified and realistic evaluation of LLMs' general learning abilities across three learning cognition dimensions. It enables diagnostic insights and supports evaluation and development of more adaptive and human-like models.

In recent years, blockchain-based data trading platforms have gained great popularity in many fields because blockchain is decentralized, transparent, antitamper, and traceable. However, data trading for medical images still has problems such as privacy protection and copyright protection, so we propose a blockchain-based data trading scheme for medical images with copyright and privacy protection. Our scheme utilizes smart contracts in the blockchain to achieve automation and intelligence of data trading. We also combine digital watermarking and image encryption to further solve copyright, secure transmission, and privacy issues during medical image transactions. We conduct relevant experiments and verify the feasibility of our proposed scheme.

Data trading has become a pivotal element in modern business models, facilitating data exchange, however, it may give rise to potential disputes. Existing data trading models resort to centralized arbitration to resolve disputes, potentially compromising the transparency and reliability of the trading. Recent research has proposed the use of decentralized arbitration to overcome these challenges. However, practical and viable decentralized arbitration models are currently lacking. Existing decentralized arbitration solutions for data trading primarily focus on tackling specific arbitration issues without providing a comprehensive mechanism. Therefore, we have developed a comprehensive data trading model, BlockArb, featuring a decentralized arbitration mechanism. We present the details of BlockArb, addressing the challenges of the decentralized arbitration system. We conducted a performance analysis of this model, revealing the feasibility of this approach in numerous scenarios. We highlight the superiority of our proposed scheme by comparing it with existing arbitration methods employed in data trading. Our experimental results unequivocally validate the effectiveness and reliability of our model.

In the past few years, there has been an increasing focus on deep learning-based watermarking techniques. Many existing methods do not impose constraints to guide the embedding of watermarking, which leads to random embedding positions and makes watermarks vulnerable to detection and attack. In this paper, an adaptive robust watermarking technique is proposed as a solution to this issue. The proposed method employs a new embedding-guided end-to-end architecture, introducing the Embedding Guide component that utilizes attention mechanism and edge information to embed the secret message into regions that are visually insensitive and inconspicuous. This component enables adaptive embedding of the secret message in each cover image, resulting in high-quality watermarked images with improved imperceptibility. To enhance robustness, this study integrates the Efficient Channel Attention (ECA) block into both the message preprocessor and decoder, facilitating more effective secret message embedding and extraction. Furthermore, UNet++ is applied to improve performance against combined noise. The experimental findings demonstrate that the suggested algorithm surpasses current approaches.

Traditional methods of pre-training, fine-tuning, and ensembling often overlook essential relational data and task interconnections. To address this gap, our study presents a novel approach to harnessing this relational information via a relational graph-based model. We introduce Relational grAph Model ensemBLE model, abbreviated as RAMBLE. This model distinguishes itself by performing class label inference simultaneously across all data nodes and task nodes, employing the relational graph in a transductive manner. This fine-grained approach allows us to better comprehend and model the intricate interplay between data and tasks. Furthermore, we incorporate a novel variational information bottleneck-guided scheme for embedding fusion and aggregation. This innovative technique facilitates the creation of an informative fusion embedding, honing in on embeddings beneficial for the intended task while simultaneously filtering out potential noise-laden embeddings. Our theoretical analysis, grounded in information theory, confirms that the use of relational information for embedding fusion allows us to achieve higher upper and lower bounds on our target task's accuracy. We thoroughly assess our proposed model across eight diverse datasets, and the experimental results demonstrate the model's effective utilization of relational knowledge derived from all pre-trained models, thereby enhancing its performance on our target tasks.

Pre-training datasets are critical for building state-of-the-art machine learning models, motivating rigorous study on their impact on downstream tasks. In this work, we study the impact of the trade-off between the intra-class diversity (the number of samples per class) and the inter-class diversity (the number of classes) of a supervised pre-training dataset. Empirically, we found that with the size of the pre-training dataset fixed, the best downstream performance comes with a balance on the intra-/inter-class diversity. To understand the underlying mechanism, we show theoretically that the downstream performance depends monotonically on both types of diversity. Notably, our theory reveals that the optimal class-to-sample ratio (#classes / #samples per class) is invariant to the size of the pre-training dataset, which motivates an application of predicting the optimal number of pre-training classes. We demonstrate the effectiveness of this application by an improvement of around 2 points on the downstream tasks when using ImageNet as the pre-training dataset.

The technologies of vital signs detection have been proven of great use while it is still limited by several challenges. One of the major challenges in vital signs detection is strong interferences, such as multiple targets in continuous wave radar system and random body movement (RBM), which significantly degrade the accuracy of the measurement. In this paper, a 77GHz linear frequency modulated continuous-wave (LFMCW) radar system is investigated to mitigate multiple-targets interferences. Furthermore, a novel regional hidden Markov model (RHMM) is proposed to acquire accurate estimates of the respiration rate (RR) and heart rate (HR) by exploiting the underlying slow-variant characteristics of these vital signs in the RBM environment. Experiments demonstrate the error rates of the proposed method are less than 9% for RR and less than 3% for HR in the multi-targets RBM environment.