[2606.20231] Thermodynamic Measure of Intelligence

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Computer Science > Artificial Intelligence

arXiv:2606.20231 (cs)

[Submitted on 18 Jun 2026]

Title:Thermodynamic Measure of Intelligence

Authors:Ishanu Chattopadhyay<br>View a PDF of the paper titled Thermodynamic Measure of Intelligence, by Ishanu Chattopadhyay

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Abstract:Can intelligence be measured? We propose that intelligence can be defined as the lawful amplification of rare but valid futures: a system increases the probability of outcomes that would be unlikely under passive dynamics but remain admissible under the constraints of the domain. We start with the premise that an intelligent system must model the world and its own place within it. Because the system is part of the world it models, this leads naturally to recursive self-simulation: the system represents futures in which its own actions are part of the trajectory. Our central results give a necessity statement and a conditional near-sufficiency statement connecting this architecture to a precise thermodynamic measure of lawful amplification of rare-valid futures: high rare-valid lift is impossible unless the internal simulation identifies rare-valid futures with high fidelity; conversely, when rare-valid fidelity is high and the simulation contains an effective policy, the achievable lift approaches the actuation-limited optimum. Thus recursive self-simulation is not merely a plausible feature of intelligence but, under the stated assumptions, is necessary and nearly sufficient for high thermodynamic intelligence. The resulting framework makes intelligence measurable on a universal scale, from passive matter and feedback controllers, large language models, and humans as text generators to Maxwell-demon-like information engines.

Subjects:

Artificial Intelligence (cs.AI); Statistical Mechanics (cond-mat.stat-mech); Information Theory (cs.IT); Mathematical Physics (math-ph); Adaptation and Self-Organizing Systems (nlin.AO)

Cite as:<br>arXiv:2606.20231 [cs.AI]

(or<br>arXiv:2606.20231v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.20231

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Ishanu Chattopadhyay [view email]<br>[v1]<br>Thu, 18 Jun 2026 13:41:35 UTC (3,062 KB)

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