AIBullisharXiv – CS AI · Jun 97/10
🧠Researchers introduce AMix-1, a 1.7-billion parameter protein foundation model that uses Bayesian Flow Networks to advance computational protein design and engineering. The model demonstrates predictable scaling laws, in-context learning capabilities, and test-time scaling algorithms that enable the design of protein variants with up to 50x improved activity, establishing a framework for lab-in-the-loop protein engineering.
AINeutralarXiv – CS AI · Jun 57/10
🧠Researchers introduce PERSUASIONTRACE, a framework for studying how large language models persuade humans across multi-turn conversations by tracking belief changes in real-time rather than just measuring pre/post outcomes. The study reveals that humans cluster into predictable persuasion patterns and that a Bayesian-network simulator better replicates authentic human belief dynamics than vanilla LLMs, with implications for both AI safety and persuasion research methodology.
AIBullisharXiv – CS AI · Jun 256/10
🧠Researchers present xAARA, an AI system that enhances stroke rehabilitation assessment by analyzing multi-view video to provide ARAT scores with calibrated uncertainty and clinical explanations. The system achieved 94.2% task accuracy while reducing predictive uncertainty by 96.1% compared to single clinicians, with four independent clinicians validating its potential for clinical deployment.
AINeutralarXiv – CS AI · Jun 106/10
🧠KG-SoftMAP is a novel machine learning method that improves Bayesian network structure learning from sparse discrete data by integrating imperfect domain knowledge as weighted soft priors. The approach combines expert-curated or LLM-extracted knowledge graphs with statistical scoring, demonstrating superior structure recovery on synthetic benchmarks and practical utility on real educational datasets.
AINeutralarXiv – CS AI · Jun 105/10
🧠Researchers propose a Bayesian Network-based Decision Support System (DSS) to help infrastructure operators select appropriate security tools across heterogeneous open-source networks. The framework addresses the growing complexity of managing interconnected systems by automating the matching of high-level security requirements to suitable mechanisms.
AINeutralarXiv – CS AI · Jun 86/10
🧠Researchers propose the Glassbox Framework, a new AI architecture that replaces post-hoc explainability with ante-hoc probabilistic mediation using Bayesian networks as transparent reasoning layers for large language models. This approach aims to make AI systems fundamentally accountable in high-stakes domains like healthcare, law, and public administration by encoding domain knowledge and causal assumptions before inference occurs.
AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers extended modular dynamic Bayesian networks (MDBNs) to model non-Markovian queuing systems by approximating non-exponential distributions with phase-type distributions. This advancement enables causal metamodeling for complex systems previously limited to Markovian analysis, achieving orders-of-magnitude speedup in inference compared to direct simulation.
AINeutralarXiv – CS AI · May 285/10
🧠Researchers analyze how Markov logic networks (MLNs) behave as domain size increases, demonstrating that probability distributions determined by MLNs diverge significantly from uniform distributions. The work provides asymptotic characterization for single-relation languages and proves fundamental differences exist between MLNs and lifted Bayesian networks in their distributional properties.
AINeutralarXiv – CS AI · May 125/10
🧠Researchers introduce Free Energy Manifold (FEM), a score-based conditional energy model designed to improve probabilistic inference in hybrid Bayesian networks containing both discrete and continuous variables. The work identifies and addresses a critical failure mode called the mode-bridge artifact, where standard energy models create artificially low-energy paths between separated probability modes, leading to overconfident predictions in regions not seen during training.
AIBullisharXiv – CS AI · Mar 37/106
🧠Researchers introduce General Proximal Flow Networks (GPFNs), a generalization of Bayesian Flow Networks that allows for arbitrary divergence functions instead of fixed Kullback-Leibler divergence. The framework enables iterative generative modeling with improved generation quality when divergence functions are adapted to underlying data geometry.
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AINeutralarXiv – CS AI · Mar 34/107
🧠A research study compares econometric methods versus causal machine learning algorithms for analyzing time-series data to inform policy decisions, using UK COVID-19 policies as a case study. The research evaluates four econometric methods against eleven causal ML algorithms, finding that econometric methods provide clearer temporal structure rules while causal ML algorithms explore broader graph structures to capture more causal relationships.
