Graph Data Modeling: Molecules, Proteins, & Chemical Processes arXiv:2508.19356v1 Announce Type: new Abstract: Graphs are central to the chemical sciences, providing a natural language to describe molecules, proteins, reactions, and industrial processes. They capture interactions and structures that underpin materials,…
Atrial Fibrillation Prediction Using a Lightweight Temporal Convolutional and Selective State Space Architecture arXiv:2508.19361v1 Announce Type: new Abstract: Atrial fibrillation (AF) is the most common arrhythmia, increasing the risk of stroke, heart failure, and other cardiovascular complications. While AF detection…
Memorization in Graph Neural Networks arXiv:2508.19352v1 Announce Type: new Abstract: Deep neural networks (DNNs) have been shown to memorize their training data, yet similar analyses for graph neural networks (GNNs) remain largely under-explored. We introduce NCMemo (Node Classification Memorization), the…
Efficient Multi-Source Knowledge Transfer by Model Merging arXiv:2508.19353v1 Announce Type: new Abstract: While transfer learning is an advantageous strategy, it overlooks the opportunity to leverage knowledge from numerous available models online. Addressing this multi-source transfer learning problem is a promising…
Re:Frame — Retrieving Experience From Associative Memory arXiv:2508.19344v1 Announce Type: new Abstract: Offline reinforcement learning (RL) often deals with suboptimal data when collecting large expert datasets is unavailable or impractical. This limitation makes it difficult for agents to generalize and…
(DEMO) Deep Reinforcement Learning Based Resource Allocation in Distributed IoT Systems arXiv:2508.19318v1 Announce Type: new Abstract: Deep Reinforcement Learning (DRL) has emerged as an efficient approach to resource allocation due to its strong capability in handling complex decision-making tasks. However,…
POT: Inducing Overthinking in LLMs via Black-Box Iterative Optimization arXiv:2508.19277v1 Announce Type: new Abstract: Recent advances in Chain-of-Thought (CoT) prompting have substantially enhanced the reasoning capabilities of large language models (LLMs), enabling sophisticated problem-solving through explicit multi-step reasoning traces. However,…
An Empirical Risk Minimization Approach for Offline Inverse RL and Dynamic Discrete Choice Model arXiv:2502.14131v5 Announce Type: replace Abstract: We study the problem of estimating Dynamic Discrete Choice (DDC) models, also known as offline Maximum Entropy-Regularized Inverse Reinforcement Learning (offline…
Grounding the Ungrounded: A Spectral-Graph Framework for Quantifying Hallucinations in multimodal LLMs arXiv:2508.19366v1 Announce Type: new Abstract: Hallucinations in large language models (LLMs) remain a fundamental obstacle to trustworthy AI, particularly in high-stakes multimodal domains such as medicine, law, and…
Unfolding AlphaFold's Bayesian Roots in Probability Kinematics arXiv:2505.19763v2 Announce Type: replace Abstract: We present a novel theoretical interpretation of AlphaFold1 that reveals the potential of generalized Bayesian updating for probabilistic deep learning. The seminal breakthrough of AlphaFold1 in protein structure…
