Method teaches generative AI models to locate personalized objects
After being trained with this technique, vision-language models can better identify a unique item in a new scene.
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Arbitrary Entropy Policy Optimization: Entropy Is Controllable in Reinforcement Fine-tuning
arXiv:2510.08141v3 Announce Type: replace Abstract: Reinforcement fine-tuning (RFT) is essential for enhancing the reasoning capabilities of large language models (LLM), yet the widely adopted Group Relative Policy Optimization (GRPO) suffers from entropy collapse, where entropy monotonically decreases, exploration vanishes, and…
Semantically Guided Action Anticipation
arXiv:2411.15557v4 Announce Type: replace-cross Abstract: Unsupervised domain adaptation remains a critical challenge in enabling the knowledge transfer of models across unseen domains. Existing methods struggle to balance the need for domain-invariant representations with preserving domain-specific features, which is often due…
Socially inspired Adaptive Coalition and Client Selection in Federated Learning
arXiv:2506.02897v2 Announce Type: replace Abstract: Federated Learning (FL) enables privacy-preserving collaborative model training, but its effectiveness is often limited by client data heterogeneity. We introduce a client-selection algorithm that (i) dynamically forms nonoverlapping coalitions of clients based on asymptotic agreement…
Normalised clustering accuracy: An asymmetric external cluster validity measure
arXiv:2209.02935v5 Announce Type: replace Abstract: There is no, nor will there ever be, single best clustering algorithm. Nevertheless, we would still like to be able to distinguish between methods that work well on certain task types and those that systematically…
BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery
arXiv:2501.01540v2 Announce Type: replace Abstract: Understanding the world and explaining it with scientific theories is a central aspiration of artificial intelligence research. Proposing theories, designing experiments to test them, and then revising them based on data are fundamental to scientific…
Balancing Performance and Reject Inclusion: A Novel Confident Inlier Extrapolation Framework for Credit Scoring
arXiv:2510.12967v1 Announce Type: new Abstract: Reject Inference (RI) methods aim to address sample bias by inferring missing repayment data for rejected credit applicants. Traditional approaches often assume that the behavior of rejected clients can be extrapolated from accepted clients, despite…
Tensor Logic: The Language of AI
arXiv:2510.12269v2 Announce Type: replace-cross Abstract: Progress in AI is hindered by the lack of a programming language with all the requisite features. Libraries like PyTorch and TensorFlow provide automatic differentiation and efficient GPU implementation, but are additions to Python, which…
An Investigation of Memorization Risk in Healthcare Foundation Models
arXiv:2510.12950v1 Announce Type: new Abstract: Foundation models trained on large-scale de-identified electronic health records (EHRs) hold promise for clinical applications. However, their capacity to memorize patient information raises important privacy concerns. In this work, we introduce a suite of black-box…
A Multimodal XAI Framework for Trustworthy CNNs and Bias Detection in Deep Representation Learning
arXiv:2510.12957v1 Announce Type: new Abstract: Standard benchmark datasets, such as MNIST, often fail to expose latent biases and multimodal feature complexities, limiting the trustworthiness of deep neural networks in high-stakes applications. We propose a novel multimodal Explainable AI (XAI) framework…