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 […]
How LLMs Learn to Reason: A Complex Network Perspective
arXiv:2509.23629v2 Announce Type: replace Abstract: Training large language models with Reinforcement Learning with Verifiable Rewards (RLVR) exhibits a set of distinctive and puzzling behaviors that remain poorly understood, including a two-stage learning curve, a V-shaped response-length trajectory, and a pronounced vulnerability to catastrophic forgetting. In this work, we propose that these behaviors are emergent collective […]
A Reinforcement Learning-Based Telematic Routing Protocol for the Internet of Underwater Things
arXiv:2506.00133v2 Announce Type: replace-cross Abstract: The Internet of Underwater Things (IoUT) has a lot of problems, like low bandwidth, high latency, mobility, and not enough energy. Routing protocols that were made for land-based networks, like RPL, don’t work well in these underwater settings. This paper talks about RL-RPL-UA, a new routing protocol that uses reinforcement […]
Model Inversion Attack Against Deep Hashing
arXiv:2511.12233v2 Announce Type: replace-cross Abstract: Deep hashing improves retrieval efficiency through compact binary codes, yet it introduces severe and often overlooked privacy risks. The ability to reconstruct original training data from hash codes could lead to serious threats such as biometric forgery and privacy breaches. However, model inversion attacks specifically targeting deep hashing models remain […]
MonoKAN: Certified Monotonic Kolmogorov-Arnold Network
arXiv:2409.11078v2 Announce Type: replace-cross Abstract: Artificial Neural Networks (ANNs) have significantly advanced various fields by effectively recognizing patterns and solving complex problems. Despite these advancements, their interpretability remains a critical challenge, especially in applications where transparency and accountability are essential. To address this, explainable AI (XAI) has made progress in demystifying ANNs, yet interpretability alone […]
RAG-BioQA Retrieval-Augmented Generation for Long-Form Biomedical Question Answering
arXiv:2510.01612v2 Announce Type: replace-cross Abstract: The exponential growth of biomedical literature creates significant challenges for accessing precise medical information. Current biomedical question-answering systems primarily focus on short-form answers, failing to provide the comprehensive explanations necessary for clinical decision-making. We present RAG-BioQA, a novel framework combining retrieval-augmented generation with domain-specific fine-tuning to produce evidence-based, long-form biomedical […]