Understanding how discussion dynamics shape team creativity has been limited by the difficulty of measuring process at scale.We introduce TRACE, a corpus of 309 group discussions from 103 teams (421 participants) across six creative problem-solving tasks.The dataset follows an input-process-output framework, integrating team composition (demographics, personalities), full discussion transcripts, and creativity outcomes.Using sentence embeddings and factor analysis, we identify four interpretable discussion dimensions:Coherence, Exploration, Convergence, and Participation.Analysis reveals a depth-breadth trade-off: coherent idea development inversely relates to semantic exploration.Larger teams explore more broadly but converge less effectively while team diversity shapes participation patterns more than discussion content.Novelty and usefulness in the creativity outcomes follow distinct pathways: Exploration and Convergence predict novelty, whereas Coherence predicts usefulness.These findings ground our understanding of how teams talk their way to creative solutions and provide guidance for designing multiagent systems.

Personalising large text-to-image diffusion models with only a handful of images unlocks various applications but exposes a fidelity–forgetting dilemma: models learn new concepts slowly and often erase semantically distant knowledge. We tackle this by injecting discriminative signals into the fine-tuning loop. Our framework, DiffDisReg, couples a Segmentation-Guided Attention Regularizer with a lightweight feature-level head and schedules both through a Semantic-Similarity regularization Scheduler that targets concepts most at risk of being forgotten. Experiments on DreamBooth benchmarks show that DiffDisReg reaches baseline geometric fidelity in under half the training steps while improving final image quality and reducing out-of-domain degradation. Ablation studies confirm the contribution of each component, and qualitative timelines reveal cleaner geometry and color from the earliest iterations. The code is available at: https://github.com/yj373/DiffDisReg.

Diffusion models have rapidly advanced the field of data synthesis as powerful generative models. However, efficiently evaluating their generative capability while reflecting human preferences remains challenging. In contrast, evaluation metrics for discriminative tasks are based on objective truth and can scale across various image domains with negligible cost. On the other hand, recent studies have expanded the application of diffusion models to discriminative tasks, highlighting a promising intersection between generative and discriminative capabilities. Inspired by these, we propose DiffDisEval, a novel benchmark method that assesses the generative ability of diffusion models through the lens of the discriminative segmentation task. DiffDisEval utilizes semantic masks generated by the denoising network in diffusion models, which fuses self-attention and cross-attention maps conditioned on carefully designed textual prompts using the BLIP model, enabling an unbiased evaluation of the model's ability to align images with texts. Additionally, based on DiffDisEval, we introduce an automatic dataset construction pipeline that can mitigate the constraints caused by the predefined label set of an existing image segmentation dataset and generate a dataset to fully characterize the segmentation capability of diffusion models. We formulate a general-purpose evaluation dataset, consisting of 1,000 images from four domains: natural images, medical scans, urban scenes, and aerial views. Evaluating prevalent diffusion models, including Stable Diffusion, Openjourney, and SDXL, reveals a strong correlation between the evaluation results and human preferences, demonstrating the efficacy of assessing generative models through discriminative tasks.

Operator eigenvalue problems play a critical role in various scientific fields and engineering applications, yet numerical methods are hindered by the curse of dimensionality. Recent deep learning methods provide an efficient approach to address this challenge by iteratively updating neural networks. These methods' performance relies heavily on the spectral distribution of the given operator: larger gaps between the operator's eigenvalues will improve precision, thus tailored spectral transformations that leverage the spectral distribution can enhance their performance. Based on this observation, we propose the **S**pectral **T**ransformation **Net**work (**STNet**). During each iteration, STNet uses approximate eigenvalues and eigenfunctions to perform spectral transformations on the original operator, turning it into an equivalent but easier problem. Specifically, we employ deflation projection to exclude the subspace corresponding to already solved eigenfunctions, thereby reducing the search space and avoiding converging to existing eigenfunctions. Additionally, our filter transform magnifies eigenvalues in the desired region and suppresses those outside, further improving performance. Extensive experiments demonstrate that STNet consistently outperforms existing learning-based methods, achieving state-of-the-art performance in accuracy.

In the digital era, technological advancements have enabled greater convenience, economic growth, and productivity, but also caused a significant increase in online scams, leading to both financial loss and emotional distress to individuals. It becomes critical to understand why individuals fall for these scams and identify effective protective measures. Central to the tactics used by scammers and security experts is gain-loss framing, a persuasive technique in communication that influences decision-making by altering how information is presented; it emphasises potation benefits (gain-framed) or losses (loss-framed) associated with the same core content. This study conducts a systematic test of how gain-loss framing is employed in scam tactics and warning messages and examines the role of risk perception in these processes. Results suggest that loss-based (versus reward-based) scams increase individuals’ perceived risk of not responding to scams, making them more susceptible to scams. Loss-framed (versus gain-framed) warnings are more effective in preventing people from responding to scams, particularly when individuals perceive moderate to high risks of responding to scams. These findings not only bridge a gap in our theoretical understanding of how gain-loss framing influences scam compliance and intervention processes but also inform the design of more targeted and effective anti-scam interventions.

Biomedical Named Entity Recognition (BioNER) aims at identifying biomedical entities such as genes, proteins, diseases, and chemical compounds in the given textual data. However, due to the issues of ethics, privacy, and high specialization of biomedical data, BioNER suffers from the more severe problem of lacking in quality labeled data than the general domain especially for the token-level. Facing the extremely limited labeled biomedical data, this work studies the problem of gazetteer-based BioNER, which aims at building a BioNER system from scratch. It needs to identify the entities in the given sentences when we have zero token-level annotations for training. Previous works usually use sequential labeling models to solve the NER or BioNER task and obtain weakly labeled data from gazetteers when we don't have full annotations. However, these labeled data are quite noisy since we need the labels for each token and the entity coverage of the gazetteers is limited. Here we propose to formulate the BioNER task as a Textual Entailment problem and solve the task via Textual Entailment with Dynamic Contrastive learning (TEDC). TEDC not only alleviates the noisy labeling issue, but also transfers the knowledge from pre-trained textual entailment models. Additionally, the dynamic contrastive learning framework contrasts the entities and non-entities in the same sentence and improves the model's discrimination ability. Experiments on two real-world biomedical datasets show that TEDC can achieve state-of-the-art performance for gazetteer-based BioNER.