AINeutralarXiv – CS AI · Jun 236/10
🧠Researchers present ThermoLLM, a Large Language Model-based framework for multi-zone HVAC control that integrates thermodynamic physics and spatial building semantics through a knowledge graph. The system outperforms standard baselines and competing LLM approaches by reasoning about zone coupling and thermal interactions, achieving superior energy-comfort trade-offs in building simulations.
AINeutralarXiv – CS AI · Jun 236/10
🧠Researchers challenge three foundational assumptions in reinforcement learning—treating environments as Markov processes, learning as policy optimization, and agents as scalar reward maximizers—proposing instead a framework grounded in evolutionary dynamics and thermodynamic theories of agency. The work suggests reconceptualizing agent learning as adaptation rather than optimization, with goals extending beyond simple reward signals.
AINeutralarXiv – CS AI · Jun 196/10
🧠Researchers propose a thermodynamic framework for measuring intelligence based on a system's ability to amplify rare but valid futures through recursive self-simulation. The model suggests intelligence is quantifiable on a universal scale and proves that recursive self-simulation is necessary and nearly sufficient for achieving high thermodynamic intelligence across systems from passive matter to large language models.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers propose the Hierarchical Emergence Framework (HEF), a mathematical model explaining why independently evolving complex systems converge toward similar structures despite different starting conditions. Testing on transformer networks shows reproducible phase transition signatures during grokking, with all models converging to identical accuracy levels regardless of initialization parameters.
AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers introduce Physics-Informed Deep Learning (PIDL), a unified neural framework that enforces both differential equations and thermodynamic constraints simultaneously across different physical domains. The framework demonstrates exceptional data efficiency and zero Second Law violations in both thermodynamic and financial modeling applications.
AINeutralarXiv – CS AI · May 285/10
🧠Researchers present an improved PULSE method for efficiently estimating thermodynamic properties of chemically disordered compounds using AI-driven partition function sampling. The approach significantly reduces computational costs compared to traditional Monte Carlo methods while maintaining high accuracy, as demonstrated through 2D Ising model validation.
AINeutralarXiv – CS AI · Mar 276/10
🧠Researchers present a unified theoretical framework for understanding generative diffusion models by connecting information theory, dynamics, and thermodynamics. The study reveals that diffusion generation operates as controlled noise-induced symmetry breaking, where the score function regulates information flow from noise to structured data.
AIBullisharXiv – CS AI · Mar 37/106
🧠Researchers introduce MultiPUFFIN, a multimodal AI foundation model that predicts molecular properties for drug discovery and materials science. The model combines multiple data types and thermodynamic principles to achieve superior performance while using 2000x fewer training molecules than existing models like ChemBERTa-2.
AINeutralarXiv – CS AI · Mar 164/10
🧠Researchers propose a new geometric framework for reinforcement learning that applies thermodynamics principles to formalize curriculum learning. The approach interprets reward parameters as coordinates on a task manifold, where optimal learning curricula correspond to geodesics that minimize excess thermodynamic work.
