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 […]
From Noise to Narrative: Tracing the Origins of Hallucinations in Transformers
arXiv:2509.06938v2 Announce Type: replace-cross Abstract: As generative AI systems become competent and democratized in science, business, and government, deeper insight into their failure modes now poses an acute need. The occasional volatility in their behavior, such as the propensity of transformer models to hallucinate, impedes trust and adoption of emerging AI solutions in high-stakes areas. […]
Generative AI and Power Imbalances in Global Education: Frameworks for Bias Mitigation
arXiv:2406.02966v4 Announce Type: replace-cross Abstract: This study examines how Generative Artificial Intelligence reproduces global power hierarchies in education and proposes a framework to address resulting inequities. Using a critical qualitative design, the study conducted zero-shot prompt testing with two leading systems, ChatGPT-4 Turbo and Gemini 1.5, and collected real-time outputs from Global North and South […]
Lost in Translation and Noise: A Deep Dive into the Failure Modes of VLMs on Real-World Tables
arXiv:2511.17238v1 Announce Type: cross Abstract: The impressive performance of VLMs is largely measured on benchmarks that fail to capture the complexities of real-world scenarios. Existing datasets for tabular QA, such as WikiTableQuestions and FinQA, are overwhelmingly monolingual (English) and present tables in a digitally perfect, clean format. This creates a significant gap between research and […]
Defending the Edge: Representative-Attention Defense against Backdoor Attacks in Federated Learning
arXiv:2505.10297v2 Announce Type: replace-cross Abstract: Federated learning (FL) remains highly vulnerable to adaptive backdoor attacks that preserve stealth by closely imitating benign update statistics. Existing defenses predominantly rely on anomaly detection in parameter or gradient space, overlooking behavioral constraints that backdoor attacks must satisfy to ensure reliable trigger activation. These anomaly-centric methods fail against adaptive […]
Approximating a gene regulatory network from non-sequential data
arXiv:2401.11858v3 Announce Type: replace Abstract: Given non-sequential snapshots from instances of a dynamical system, we design a compressed sensing based algorithm that reconstructs the dynamical system. On the theoretical side, we show that: (1) successful reconstruction is possible under the assumption that we can construct an approximate clock from a subset of the coordinates of […]