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April 12, 20260 citationsOpen Access

L-OS and Physical Token: A Closed-Loop Paradigm from Photonic Physical Computing to Computation-Backed Value

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XCXiangning Chen

Key Points

  • The aim is to address the challenges faced by modern AI systems regarding compute demand and energy consumption through a novel architecture.
  • Introduced a three-layer paradigm: physical, system, and economic.
  • Developed B³D-HPA for low-power, high-parallelism computation using electromagnetic fields.
  • Utilized L-OS for direct mapping of logical intent to optical configurations, eliminating inefficient processes.
  • Established Physical Tokens to anchor digital value to computational resources.
  • Demonstrated reduced energy consumption through the B³D-HPA architecture.
  • Confirmed increased computational efficiency via L-OS compared to traditional systems.
  • Physical Tokens created a stable value system tied to physical computing resources.

Abstract

Modern large-scale AI models and AGI-oriented systems face intensifying existential bottlenecks: exponential growth in compute demand, unsustainable energy consumption, thermal instability, and severe efficiency limits imposed by legacy von Neumann architectures. This paper presents a unified, physically consistent three-layer paradigm designed as a natural evolutionary solution to the global AGI compute crisis. At the physical layer, B³D-HPA (Block-Based Heterogeneous 3D Photonic Architecture) enables low-power, high-parallelism computation through continuous electromagnetic field evolution within dielectric media, indexed via a Physical Hash Address Table (PHAT). At the system layer, L-OS (Logic Operating System) serves as a lightweight hardware-native neural hub that maps high-level logical intent directly to optical phase configurations, bypassing traditional compilers and inefficient instruction pipelines. At the economic layer, Physical Tokens represent bounded, non-forgeable rights to invoke stable PHAT computational channels, anchoring digital value to physically constrained computing resources rather than abstract consensus or credit. This closed-loop ecosystem offers a thermally resilient, energy-efficient pathway to scale AGI infrastructure while establishing a naturally disciplined value layer for global computational resources.

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Cite This Study

Xiangning Chen (2026) studied this question.

synapsesocial.com/papers/69db388e4fe01fead37c6ae7https://doi.org/10.5281/zenodo.19495312
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