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Published November 21, 2025 | Version v1
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Observer as a Substrate-Independent Realization Condition: A Cross-Disciplinary Framework for Observable State Formation

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Across machine learning, physics, quantum theory, and the biological sciences, many systems exhibit the same fundamental transition: a probabilistic representation of possible states gives rise to a single, externally accessible outcome. Although these processes differ in substrate, scale, and mechanism, they share a minimal structural form involving a description of alternative possibilities, constraints that determine when one becomes admissible, and the resulting realized state.

This article develops a substrate-independent framework that formalizes this shared structure. We define a realization condition as the minimal set of constraints under which a probabilistic state description yields a definite, externally accessible outcome, and argue that the role traditionally attributed to an “observer” in quantum theory can be understood in these functional terms.

We examine four distinct domains — large language models, information-based physical theories, quantum measurement, and biological decision making — and show that each instantiates this transition in a structurally homologous way without implying mechanistic or ontological equivalence. The framework clarifies the functional role of observation in quantum theory, provides a common vocabulary for cross-domain comparison, and identifies structural expectations that may guide empirical work on uncertainty resolution and outcome formation.

The framework is deliberately limited to describing the form of this transition rather than its underlying mechanisms, but it offers a basis for interdisciplinary analysis and a foundation for further theoretical development.

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2025-11-21

References

  • Arieli, A., Sterkin, A., Grinvald, A., & Aertsen, A. (1996). Dynamics of ongoing activity: Explanation of the large variability in evoked cortical responses. Science, 273(5283), 1868–1871.
  • Bekenstein, J. D. (1973). Black holes and entropy. Physical Review D, 7(8), 2333–2346.
  • Bohr, N. (1935). Can quantum-mechanical description of physical reality be considered complete? Physical Review, 48(8), 696–702.
  • Cao, S. (2023). On the Entropy Calibration of Language Models. OpenReview preprint.
  • Chang, H.-S., Peng, N., Bansal, M., Ramakrishna, A., & Chung, T. (2024). REAL Sampling: Boosting Factuality and Diversity of Open-Ended Generation via Asymptotic Entropy. arXiv preprint arXiv:2406.07735.
  • Churchland, A. K., Kiani, R., Chaudhuri, R., Wang, X.-J., Pouget, A., & Shadlen, M. N. (2011). Variance as a signature of neural computations during decision making. Neuron, 69(4), 818–831.
  • Davies, P. C. W. (2019). The demon in the machine: How hidden webs of information are solving the mystery of life. Penguin.
  • Elhage, N., Nanda, N., Olsson, C., et al. (2021). A mathematical framework for transformer circuits. Anthropic technical report / arXiv preprint.
  • Everett, H. (1957). "Relative state" formulation of quantum mechanics. Reviews of Modern Physics, 29(3), 454–462.
  • Fiser, J., Berkes, P., Orbán, G., & Lengyel, M. (2010). Statistically optimal perception and learning: from behavior to neural representations. Trends in Cognitive Sciences, 14(3), 119–130.
  • Friston, K. (2005). A theory of cortical responses. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1456), 815–836.
  • Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138.
  • Fuchs, C. A., & Schack, R. (2013). Quantum-Bayesian coherence. Reviews of Modern Physics, 85(4), 1693–1715.
  • Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30, 535–574.
  • Hawking, S. W. (1975). Particle creation by black holes. Communications in Mathematical Physics, 43(3), 199–220.
  • Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2020). The curious case of neural text degeneration. In Proceedings of the International Conference on Learning Representations (ICLR 2020).
  • Hu, J., Zhang, B., Sun, Q., Hu, Z., & Dong, Y. (2023). A survey on uncertainty in natural language processing. arXiv preprint arXiv:2306.04459.
  • Jaynes, E. T. (1957). Information theory and statistical mechanics. Physical Review, 106(4), 620–630; 108(2), 171–190.
  • Knill, D. C., & Pouget, A. (2004). The Bayesian brain: The role of uncertainty in neural coding and computation. Trends in Neurosciences, 27(12), 712–719.
  • Landau, L. D., & Lifshitz, E. M. (1980). Statistical physics. Part 1 (3rd ed.). Pergamon Press.
  • Landauer, R. (1961). Irreversibility and heat generation in the computing process. IBM Journal of Research and Development, 5(3), 183–191.
  • Lloyd, S. (2006). Programming the universe: A quantum computer scientist takes on the cosmos. Alfred A. Knopf.
  • Lu, X., Yu, M., Zhou, J., Ren, X., Zhang, M., & Dong, Y. (2021). Neurologic decoding: (Un) constrained text generation with neurosymbolic methods. Advances in Neural Information Processing Systems, 34, 8964–8977.
  • Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006). Bayesian inference with probabilistic population codes. Nature Neuroscience, 9(11), 1432–1438.
  • McFadden, D. (2001). Economic choices. American Economic Review, 91(3), 351–378.
  • Moore, K., Roberts, J., Watson, D., & Wisniewski, P. (2025). Human-Alignment and Calibration of Inference-Time Uncertainty in Large Language Models. arXiv preprint arXiv:2508.08204.
  • Nielsen, M. A., & Chuang, I. L. (2000). Quantum computation and quantum information. Cambridge University Press.
  • Olsson, C., Elhage, N., Nanda, N., et al. (2022). In-context learning and induction heads. Anthropic technical report / arXiv preprint.
  • Pouget, A., Beck, J. M., Ma, W. J., & Latham, P. E. (2013). Probabilistic brains: Knowns and unknowns. Nature Neuroscience, 16(9), 1170–1178.
  • Raj, A., & van Oudenaarden, A. (2008). Nature, nurture, or chance: Stochastic gene expression and its consequences. Cell, 135(2), 216–226.
  • Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20(4), 873–922.
  • Rovelli, C. (1996). Relational quantum mechanics. International Journal of Theoretical Physics, 35(8), 1637–1678.
  • Rovelli, C. (2021). Helgoland: Making sense of the quantum revolution. Penguin.
  • Schlosshauer, M. (2007). Decoherence and the quantum-to-classical transition. Springer.
  • Shadlen, M. N., & Newsome, W. T. (2001). Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. Journal of Neurophysiology, 86(4), 1916–1936.
  • Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423; 27(4), 623–656.
  • Susskind, L. (1995). The world as a hologram. Journal of Mathematical Physics, 36(11), 6377–6396.
  • Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
  • 't Hooft, G. (1993). Dimensional reduction in quantum gravity. In A. Ali, J. Ellis, & S. Randjbar-Daemi (Eds.), Salamfestschrift: A collection of talks (pp. 284–296). World Scientific.
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (NeurIPS 30) (pp. 5998–6008).
  • Vedral, V. (2012). Information and physics. Information, 3(2), 219–223.
  • Verlinde, E. (2011). On the origin of gravity and the laws of Newton. Journal of High Energy Physics, 2011(4), Article 29.
  • Wallace, D. (2012). The emergent multiverse: Quantum theory according to the Everett interpretation. Oxford University Press.
  • Wang, B., Wang, Z., Liu, Y., Zhu, C., Zeng, M., & Ji, H. (2020). Contextual temperature for language modeling. arXiv preprint arXiv:2012.13575.
  • Wang, X.-J. (2002). Probabilistic decision making by slow reverberation in cortical circuits. Neuron, 36(5), 955–968.
  • Wang, X.-J. (2008). Decision making in recurrent neuronal circuits. Neuron, 60(2), 215–234.
  • Welleck, S., Kulikov, I., Kim, J., Pang, R. Y., Cho, K., & Choi, Y. (2020). Neural text generation with unlikelihood training. International Conference on Learning Representations (ICLR 2020).
  • Wheeler, J. A. (1990). Information, physics, quantum: The search for links. In W. H. Zurek (Ed.), Complexity, entropy, and the physics of information (pp. 3–28). Addison-Wesley.
  • Wu, Y., Xu, C., Zhang, F., Wang, Y., Xu, Y., Zhang, Y., … Liu, Z. (2023). Visual ChatGPT: Talking, drawing and editing with visual foundation models. arXiv preprint arXiv:2303.04671.
  • Zurek, W. H. (2003). Decoherence, einselection, and the quantum origins of the classical. Reviews of Modern Physics, 75(3), 715–775.