[
  "In the high-dimensional limit, the microscopic dynamics of straight-through estimator (STE) training converge to a stochastic differential equation governed by Eq. (15) (Section IV, Theorem IV.3).",
  "The macroscopic summary statistics of STE training concentrate onto a deterministic ODE trajectory (Eqs. (24)-(25)) with concentration rate O_d(d^{-1/2}) as dimension d grows (Section V-B, Theorem V.3).",
  "STE training exhibits an extended plateau followed by a sharp drop in generalization error across bit-widths b in {2,3,4,5}, with the plateau length depending on the quantization range ω (Section VI-A1/VI-A2, Figures 2-3).",
  "For input-only quantization, a closed-form stable fixed point exists under the learning-rate condition 0 < η < 2(σ_ψ^2 + λ)/σ_ψ^4 (Section VI-C1, Proposition VI.1).",
  "The asymptotic deviation of the STE-trained quantized model from the unquantized linear model is characterized across three regimes, with a leading-order correction term of size O_Δ(Δ^2) depending on the fractional quantization level position (Section VI-C2, Theorem VI.3)."
]