Entropy

Entropy is a tactical turn-based RPG inspired by classic JRPGs. Assemble your party of mercenaries and forge your own path towards an expedition into hell to stop the demonic incursion. Choices you make along the way will determine the fate of your world.

Role differentiation as ignition of a collective information...

Informational active matter shows how measurement-informed decisions produce collective order, so far in systems that reach consensus. We design collective information engines structured by...

Non-equilibrium active noise enhances generative memory in diffusion models

Generative diffusion models have emerged as powerful tools for sampling high-dimensional distributions, yet they typically rely on white gaussian noise and noise schedules to destroy and reconstruct information. Here, we demonstrate that driving the generative process out of equilibrium using active, temporally correlated noise sources fundamentally alters the information thermodynamics of the system. We show that coupling the data to an active non-Markovian bath creates a `memory effect' where high-level semantic information (such as class identity or molecular metastability) is stored in the temporal correlations of auxiliary degrees of freedom. Using Fisher information analysis, we prove that this active mechanism significantly retards the rate of information decay compared to passive Brownian motion. Crucially, this memory effect facilitates an earlier and more robust symmetry breaking (speciation) during the reverse generative process, allowing the system to resolve multi-scale structures, reminiscent of metastable states in molecular configurations that are washed out in the typical noising processes. Our results suggest that non-equilibrium protocols, inspired by active matter physics, offer a thermodynamically distinct and potentially advantageous pathway for recovering high-dimensional energy landscapes using generative diffusion.

All you need is links

When Tim Berners Lee talks about how he designed the web, he uses an analogy from physics, describing his process as a quest to find “fundamental laws” which can generate a desired system:

All you need is links

When Tim Berners Lee talks about how he designed the web, he uses an analogy from physics, describing his process as a quest to find “fundamental laws” which can generate a desired system:

All you need is links

When Tim Berners Lee talks about how he designed the web, he uses an analogy from physics, describing his process as a quest to find “fundamental laws” which can generate a desired system:

All you need is links

When Tim Berners Lee talks about how he designed the web, he uses an analogy from physics, describing his process as a quest to find “fundamental laws” which can generate a desired system:

Uncapped Look-Ahead and the Information Horizon

I write a lot about how bad output randomness is for games, but today I want to write about a problem common in many deterministic games – specifically ones that lack hidden information. Why …

Unified framework for information integration based on information geometry

Significance Measuring the degree of causal influences among multiple elements of a system is a fundamental problem in physics and biology. We propose a unified framework for quantifying any combination of causal relationships between elements in a hierarchical manner based on information geometry. Our measure of integration, called geometrical integrated information, quantifies the strength of multiple causal influences among elements by projecting the probability distribution of a system onto a constrained manifold. This measure overcomes mathematical problems of existing measures and enables an intuitive understanding of the relationships between integrated information and other measures of causal influence such as transfer entropy. Inspired by the integration of neural activity in consciousness studies, our measure should have general utility in analyzing complex systems. , Assessment of causal influences is a ubiquitous and important subject across diverse research fields. Drawn from consciousness studies, integrated information is a measure that defines integration as the degree of causal influences among elements. Whereas pairwise causal influences between elements can be quantified with existing methods, quantifying multiple influences among many elements poses two major mathematical difficulties. First, overestimation occurs due to interdependence among influences if each influence is separately quantified in a part-based manner and then simply summed over. Second, it is difficult to isolate causal influences while avoiding noncausal confounding influences. To resolve these difficulties, we propose a theoretical framework based on information geometry for the quantification of multiple causal influences with a holistic approach. We derive a measure of integrated information, which is geometrically interpreted as the divergence between the actual probability distribution of a system and an approximated probability distribution where causal influences among elements are statistically disconnected. This framework provides intuitive geometric interpretations harmonizing various information theoretic measures in a unified manner, including mutual information, transfer entropy, stochastic interaction, and integrated information, each of which is characterized by how causal influences are disconnected. In addition to the mathematical assessment of consciousness, our framework should help to analyze causal relationships in complex systems in a complete and hierarchical manner.

A Beginner's Guide To Information Architecture in UX [2025]

What is information architecture? Why is it so important, and how does it relate to UX design? Find out why IA is so crucial in this expert guide.