Tag
This opinion paper argues that large language models are a degenerate special case of world models, not a separate paradigm, and proposes a continuous spectrum from next-token prediction to latent-space architectures like JEPA, examining the data and architecture challenges along this path.
This paper proposes a thermodynamic measure of intelligence, defining intelligence as the ability to make rare but valid futures more likely. It introduces a metric called 'rare-valid lift' that quantifies how much more often a system produces unlikely but acceptable outcomes compared to a passive baseline.
This paper proposes a thermodynamic measure of intelligence defined as 'rare-valid lift' and argues that recursive self-simulation is necessary and nearly sufficient for high thermodynamic intelligence, making intelligence measurable on a universal scale.
This is a popular science article of over 25,000 characters, starting from the origin of entropy, reviewing the development of dissipative system theory, and exploring a three-level analysis of whether AI belongs to dissipative systems (hardware level, training level, static model).
This paper introduces a tree-based formal framework for modeling complementarity in multi-agent human-AI interactions, proving that complementarity is attainable in regression but obstructed in classification under natural conditions on local aggregation and loss functions.
This post explores the debate among top AI figures regarding whether LLMs alone can achieve AGI or if additional breakthroughs like world models are required.