🤖 AI Summary
本文提出了一种新的表示论框架——蜂巢层次结构，用于提供R_2(δ)的渐近上界，通过保留每两个行不可约表示和每个坐标框转移通道，并结合移动投影定理，得到一个显式的四参数指数κ_HC。
📝 Abstract
We introduce the honeycomb hierarchy, a representation-theoretic framework that gives new asymptotic upper bounds on $R_2(δ)$. Its first level is the two-row hyperoctahedral representation graph associated with type $S^{(n-k,k)}$. Retaining every two-row irreducible and every coordinate box-transfer channel, together with a moving-projection theorem, yields an explicit four-parameter exponent $κ_{\mathrm{HC}}$. The earlier whole-cube exponent $κ_H$ is a boundary restriction of this optimization, whereas the fully optimized second MRRW exponent $M_2$ is an exact symmetric slice.
The prior best curve is the combined $κ_{\mathrm{bin}}=\min\{κ_{\mathrm{CW}},κ_H\}$, which uses a constant-weight branch $κ_{\mathrm{CW}}$. Replacing only the whole-cube branch by the honeycomb bound gives $κ_{\mathrm{best}}=\min\{κ_{\mathrm{CW}}, κ_{\mathrm{HC}}\}$. We prove, on $0<δ<1/2$, \[
R_2(δ)\le κ_{\mathrm{best}}(δ)
\le κ_{\mathrm{bin}}(δ)
\le R_{\mathrm{2MQC}}(δ)<M_2(δ),\\[-1mm]
κ_{\mathrm{best}}(δ)
\le \min\{κ_{\mathrm{CW}}(δ),
κ_{\mathrm{bal}}(δ)\}
<R_{\mathrm{2MQC}}(δ),
\qquad
κ_H(δ)=R_{\mathrm{MQC}}(δ). \]
The hierarchy has two further directions. Increasing the representation depth replaces scalar by matrix-valued transfers on the hive. Increasing the anchor depth localizes it in a stable-set hierarchy. The resulting bounds are monotone in both directions and eventually recover $A_2(n,d)$. A complementary Horn--channel hierarchy gives matrix optimizations whose $2\times2$ level is $κ_{\mathrm{HC}}$ and whose $3\times3$ level is a stronger bound. Already at low levels, they can be used to improve the strongest previous general bounds, while the honeycomb framework provides a route towards tighter bounds.