arXiv:2510.13302v2 Announce Type: replace-cross Abstract: Computational stylometry analyzes writing style through quantitative patterns in text, supporting applications from forensic tasks such as identity linking and plagiarism detection to literary attribution in the humanities. Supervised and contrastive approaches rely on data with spurious correlations and often confuse style with topic. Despite their natural use in AI-generated […]
Evaluating AI-Driven Automated Map Digitization in QGIS
arXiv:2504.18777v3 Announce Type: replace Abstract: Map digitization is an important process that converts maps into digital formats that can be used for further analysis. This process typically requires a deep human involvement because of the need for interpretation and decision-making when translating complex features. With the advancement of artificial intelligence, there is an alternative to […]
Efficient Reinforcement Learning for Large Language Models with Intrinsic Exploration
arXiv:2511.00794v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has improved the reasoning ability of large language models, yet training remains costly because many rollouts contribute little to optimization, considering the amount of computation required. This study investigates how simply leveraging intrinsic data properties, almost free benefit during training, can improve data efficiency […]
A model for mosquito-borne epidemic outbreaks with information-dependent protective behaviour
arXiv:2511.16802v1 Announce Type: new Abstract: We study a model for a mosquito-borne epidemic outbreak in which humans can adopt protective behaviour against vector bites depending on information on the past and present prevalence. Assuming that mosquitoes can also feed on other non-competent hosts (i.e. hosts that cannot infect others), we first review some results from […]
MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment
arXiv:2509.14001v4 Announce Type: replace-cross Abstract: Personalized object detection aims to adapt a general-purpose detector to recognize user-specific instances from only a few examples. Lightweight models often struggle in this setting due to their weak semantic priors, while large vision-language models (VLMs) offer strong object-level understanding but are too computationally demanding for real-time or on-device applications. […]
Masked-and-Reordered Self-Supervision for Reinforcement Learning from Verifiable Rewards
arXiv:2511.17473v1 Announce Type: cross Abstract: Test-time scaling has been shown to substantially improve large language models’ (LLMs) mathematical reasoning. However, for a large portion of mathematical corpora, especially theorem proving, RLVR’s scalability is limited: intermediate reasoning is crucial, while final answers are difficult to directly and reliably verify. Meanwhile, token-level SFT often degenerates into rote […]
Supervised Contrastive Learning for Few-Shot AI-Generated Image Detection and Attribution
arXiv:2511.16541v2 Announce Type: replace-cross Abstract: The rapid advancement of generative artificial intelligence has enabled the creation of synthetic images that are increasingly indistinguishable from authentic content, posing significant challenges for digital media integrity. This problem is compounded by the accelerated release cycle of novel generative models, which renders traditional detection approaches (reliant on periodic retraining) […]
The Coding Limits of Robust Watermarking for Generative Models
arXiv:2509.10577v2 Announce Type: replace-cross Abstract: We ask a basic question about cryptographic watermarking for generative models: to what extent can a watermark remain reliable when an adversary is allowed to corrupt the encoded signal? To study this question, we introduce a minimal coding abstraction that we call a zero-bit tamper-detection code. This is a secret-key […]
InTAct: Interval-based Task Activation Consolidation for Continual Learning
arXiv:2511.17439v1 Announce Type: cross Abstract: Continual learning aims to enable neural networks to acquire new knowledge without forgetting previously learned information. While recent prompt-based methods perform strongly in class-incremental settings, they remain vulnerable under domain shifts, where the input distribution changes but the label space remains fixed. This exposes a persistent problem known as representation […]
Value of Information-Enhanced Exploration in Bootstrapped DQN
arXiv:2511.02969v2 Announce Type: replace-cross Abstract: Efficient exploration in deep reinforcement learning remains a fundamental challenge, especially in environments characterized by high-dimensional states and sparse rewards. Traditional exploration strategies that rely on random local policy noise, such as $epsilon$-greedy and Boltzmann exploration methods, often struggle to efficiently balance exploration and exploitation. In this paper, we integrate […]