aiqbench analysis
This research proposes a quantum-inspired "Tug-of-War" model that demonstrates how contextual decision-making can be compactly represented within a single internal state using qutrits, challenging classical non-contextual probability limits. The study highlights a fundamental architectural trade-off for AI systems: whether to encode context intrinsically through state transformations or extrinsically via expanded memory resources. By providing a mathematical witness for contextuality in decision processes, the work offers new theoretical frameworks for designing more efficient neural architectures that handle complex contextual dependencies.
Minimal Decision Dynamics and Contextual Probability: A Quantum Tug-of-War Model
arXiv:2601.10034v3 Announce Type: replace-cross Abstract: Decision making often exhibits context dependence that is difficult to accommodate within a single non-invasive classical probability model. This paper develops a quantum-like extension of the Tug-of-War (QTOW) decision-making model to ask when such context dependence can be represented by a single constrained internal state. The QTOW construction uses a qutrit state, a state- disturbing generalized decision instrument, decision- and reward-conditioned norm-preserving feedback, and optional probing operations within one state space. The qutrit representation admits KCBS-type probe families and states that violate a non-contextuality bound, providing a witness that the specified operation family cannot be embedded in a single non-contextual classical prob- ability space. The claim is not that quantum theory is uniquely derived from decision making. Classical reconstructions can retain descriptive adequacy by introducing explicit context labels, stored history, or enlarged state descriptions; alternatively, the non-contextuality witness can be avoided by restricting the admissible probe set. Quantum probability instead supplies a compact single-state realization of the contextual operation family considered here. From the perspective of natural and artificial intelligence, the result identifies an architecture-level representational trade-off: contextual information may be carried intrinsically by transformations of a shared internal state or externalized into additional state and memory resources.
Abstract:Decision making often exhibits context dependence that is difficult to accommodate within a single non-invasive classical probability model. This paper develops a quantum-like extension of the Tug-of-War (QTOW) decision-making model to ask when such context dependence can be represented by a single constrained internal state. The QTOW construction uses a qutrit state, a state- disturbing generalized decision instrument, decision- and reward-conditioned norm-preserving feedback, and optional probing operations within one state space. The qutrit representation admits KCBS-type probe families and states that violate a non-contextuality bound, providing a witness that the specified operation family cannot be embedded in a single non-contextual classical prob- ability space. The claim is not that quantum theory is uniquely derived from decision making. Classical reconstructions can retain descriptive adequacy by introducing explicit context labels, stored history, or enlarged state descriptions; alternatively, the non-contextuality witness can be avoided by restricting the admissible probe set. Quantum probability instead supplies a compact single-state realization of the contextual operation family considered here. From the perspective of natural and artificial intelligence, the result identifies an architecture-level representational trade-off: contextual information may be carried intrinsically by transformations of a shared internal state or externalized into additional state and memory resources.
Substantially revised. The QTOW learning dynamics is formulated using a generalized decision instrument and decision- and reward-conditioned feedback. The contextuality analysis and the relation to natural and artificial intelligence have also been clarified
Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC)
arXiv:2601.10034 [quant-ph]
(or arXiv:2601.10034v3 [quant-ph] for this version)
From: Song-Ju Kim Dr. [view email] [v1] Thu, 15 Jan 2026 03:27:28 UTC (63 KB) [v2] Thu, 9 Jul 2026 12:17:43 UTC (35 KB) [v3] Tue, 25 Aug 2026 14:29:54 UTC (44 KB)
Full text under the publisher's open licence · 282 words
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