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…
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…
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…
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,…
(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,…
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…
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…
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…
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…
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,…
