• Computer Science > Artificial Intelligence [Submitted on 3 Feb 2026] Title:Contextuality from Single-State Representations: An Information-Theoretic Principle for Adaptive Intelligence View PDF HTML (experimental)Abstract:Adaptive systems often operate across multiple contexts while reusing a fixed internal state space due to constraints on memory, representation, or physical resources. • Such single-state reuse is ubiquitous in natural and artificial intelligence, yet its fundamental representational consequences remain poorly understood. • We show that contextuality is not a peculiarity of quantum mechanics, but an inevitable consequence of single-state reuse in classical probabilistic representations. • Modeling contexts as interventions acting on a shared internal state, we prove that any classical model reproducing contextual outcome statistics must incur an irreducible information-theoretic cost: dependence on context cannot be mediated solely through the internal state. • We provide a minimal constructive example that explicitly realizes this cost and clarifies its operational meaning. • We further explain how nonclassical probabilistic frameworks avoid this obstruction by relaxing the assumption of a single global joint probability space, without invoking quantum dynamics or Hilbert space structure.
Article Summaries:
- Summary
A February 2026 arXiv submission argues that contextuality-traditionally linked to quantum mechanics-naturally emerges whenever an adaptive system reuses a single internal state across multiple contexts. By modeling contexts as interventions on a shared state, the authors prove that any classical probabilistic model reproducing contextual outcome statistics must incur an irreducible information‑theoretic cost: the internal state alone cannot mediate context dependence. They present a minimal constructive example illustrating this cost and explain how nonclassical probabilistic frameworks avoid the obstacle by relaxing the assumption of a single global joint probability space, without invoking quantum dynamics. The work positions contextuality as a fundamental representational constraint on adaptive intelligence, independent of physical implementation.
- Computer Science > Artificial Intelligence [Submitted on 3 Feb 2026] Title:Contextuality from Single-State Representations: An Information-Theoretic Principle for Adaptive Intelligence View PDF HTML (experimental)Abstract:Adaptive systems often operate across multiple contexts while reusing a fixed internal state space due to constraints on memory, representation, or physical resources. Such single-state reuse is ubiquitous in natural and artificial intelligence, yet its fundamental representational consequences remain poorly understood. We show that contextuality is not a peculiarity of quant
Sources: