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This paper presents a stochastic theoretical framework based on the first principles of physical chemistry to address the core challenges of absolute quantification in digital immunoassays. Conventional methods rely on empirical standard curves, whose parameters lack clear physical meaning and fail to account for competitive binding and saturation effects arising from finite binding sites. This limitation compromises accuracy, dynamic range, and standardization potential. Our framework rigorously integrates three stochastic processes—molecular sampling, immunoreaction with finite binding sites, and bead detection—to establish, for the first time, an absolute quantification model driven by intrinsic system parameters. These include β, representing the maximum binding capacity per bead; α, reflecting the competitive strength in the solution phase; and the pivotal efficiency ratio κ=α/β, which governs overall system behavior. The derived intrinsic capture efficiency, γ=1/(κ+1), thermodynamically explains the incompleteness of the immune reaction, thereby entirely replacing the empirical fitting constants used in traditional models. Based on this model, we developed a principle-driven, calibration-free quantification method. To evaluate its performance, we first validated the reliability of the model using both standard samples and real clinical specimens. Furthermore, by analyzing measurement results from different dilution gradients of the same unknown sample, we demonstrated that the model achieves true calibration-free absolute quantification. In terms of statistical performance, the model provides precise point estimates and reliable confidence intervals across the entire concentration range-from the trace linear region to the high-concentration saturation regime. Theoretically, quantification precision is shown to be largely insensitive to the parameter β under common operating conditions, ensuring robustness against practical parameter fluctuations. Experimentally, compared to traditional standard curve-dependent methods, this framework significantly extends the dynamic range for accurate quantification while maintaining ultra-high sensitivity and improving measurement repeatability. This work transforms digital immunoassay from an empirically calibrated technique into an absolute measurement method grounded in intrinsic physical parameters. It lays a theoretical foundation for precise protein biomarker detection without routine calibration and with cross-platform traceability, offering substantial implications for standardizing ultra-sensitive detection technologies and advancing clinical applications.