OUTPUT of run 6 of at most 100:
```
Common L1 key-selector direction: each head's cosine and bias/QK norm ratio
0 [[-0.118  0.25 ]
 [-0.403  0.113]
 [ 0.149  0.035]
 [-0.039  0.136]
 [-0.141  0.106]
 [ 0.387  0.21 ]]
1 [[0.98  1.531]
 [0.987 1.738]
 [0.989 1.671]
 [0.985 1.711]
 [0.984 1.58 ]
 [0.986 1.312]]
2 [[0.806 0.697]
 [0.781 0.36 ]
 [0.786 0.468]
 [0.814 0.701]
 [0.804 0.673]
 [0.814 0.67 ]]
3 [[ 0.318  0.116]
 [-0.129  0.132]
 [ 0.621  0.192]
 [ 0.652  0.225]
 [ 0.602  0.166]
 [ 0.08   0.197]]
Common selector fraction in top 2/4/8 broad-output dimensions [np.float64(0.824), np.float64(0.9207), np.float64(0.9221)]
Common selector composed with L0 OV heads: norms [np.float64(4.2877), np.float64(4.0564), np.float64(0.0362), np.float64(0.1418), np.float64(2.0539), np.float64(7.59)]

Projected key-composition scores: strongest earlier heads
tuple = (earlier head, trace/F, symmetry, norm)

SPACE V0H2-24
L2H0 [('L0H4', np.float64(0.257), np.float64(0.09), np.float64(0.17)), ('L0H0', np.float64(0.149), np.float64(0.05), np.float64(0.443))]
L2H1 [('L1H3', np.float64(0.409), np.float64(-0.011), np.float64(0.096)), ('L0H4', np.float64(0.228), np.float64(0.071), np.float64(0.116))]
L2H2 [('L1H2', np.float64(0.369), np.float64(0.016), np.float64(0.056)), ('L1H3', np.float64(0.366), np.float64(0.044), np.float64(0.065))]
L2H3 [('L0H0', np.float64(0.335), np.float64(0.133), np.float64(0.451)), ('L0H4', np.float64(0.317), np.float64(0.129), np.float64(0.167))]
L2H4 [('L0H4', np.float64(0.227), np.float64(0.09), np.float64(0.191)), ('L0H0', np.float64(0.214), np.float64(0.064), np.float64(0.511))]
L2H5 [('L1H2', np.float64(0.243), np.float64(-0.066), np.float64(0.1)), ('L0H0', np.float64(0.231), np.float64(0.082), np.float64(0.485))]
L3H0 [('L1H0', np.float64(0.089), np.float64(-0.036), np.float64(0.099)), ('L1H2', np.float64(0.066), np.float64(-0.026), np.float64(0.106))]
L3H1 [('L0H2', np.float64(2.907), np.float64(0.649), np.float64(0.135)), ('L1H2', np.float64(1.933), np.float64(0.216), np.float64(0.118))]
L3H2 [('L0H2', np.float64(0.263), np.float64(0.069), np.float64(0.041)), ('L2H0', np.float64(0.212), np.float64(0.09), np.float64(0.08))]
L3H3 [('L1H3', np.float64(0.212), np.float64(0.049), np.float64(0.064)), ('L1H4', np.float64(0.186), np.float64(0.068), np.float64(0.076))]
L3H4 [('L1H5', np.float64(0.316), np.float64(0.1), np.float64(0.111)), ('L0H4', np.float64(0.077), np.float64(-0.002), np.float64(0.101))]
L3H5 [('L2H3', np.float64(0.705), np.float64(0.173), np.float64(0.045)), ('L1H0', np.float64(0.416), np.float64(0.118), np.float64(0.04))]

SPACE MLP0-32
L2H0 [('L0H4', np.float64(0.143), np.float64(0.025), np.float64(0.225)), ('L0H1', np.float64(0.09), np.float64(0.04), np.float64(0.464))]
L2H1 [('L1H3', np.float64(0.304), np.float64(0.049), np.float64(0.168)), ('L0H4', np.float64(0.185), np.float64(0.059), np.float64(0.124))]
L2H2 [('L1H2', np.float64(0.237), np.float64(0.0), np.float64(0.104)), ('L1H4', np.float64(0.134), np.float64(0.041), np.float64(0.119))]
L2H3 [('L0H1', np.float64(0.14), np.float64(0.103), np.float64(0.382)), ('L0H4', np.float64(0.132), np.float64(0.066), np.float64(0.195))]
L2H4 [('L0H4', np.float64(0.194), np.float64(0.031), np.float64(0.209)), ('L0H1', np.float64(0.186), np.float64(0.048), np.float64(0.419))]
L2H5 [('L1H2', np.float64(0.203), np.float64(-0.012), np.float64(0.185)), ('L0H4', np.float64(0.187), np.float64(0.063), np.float64(0.195))]
L3H0 [('L0H4', np.float64(0.182), np.float64(0.023), np.float64(0.083)), ('L0H0', np.float64(0.176), np.float64(0.03), np.float64(0.188))]
L3H1 [('L2H1', np.float64(2.027), np.float64(0.274), np.float64(0.241)), ('L0H2', np.float64(1.772), np.float64(0.452), np.float64(0.062))]
L3H2 [('L2H2', np.float64(0.216), np.float64(0.003), np.float64(0.142)), ('L2H4', np.float64(0.207), np.float64(0.021), np.float64(0.115))]
L3H3 [('L0H2', np.float64(0.394), np.float64(-0.004), np.float64(0.021)), ('L1H3', np.float64(0.386), np.float64(-0.03), np.float64(0.107))]
L3H4 [('L1H2', np.float64(0.18), np.float64(-0.005), np.float64(0.13)), ('L2H0', np.float64(0.169), np.float64(0.029), np.float64(0.132))]
L3H5 [('L2H2', np.float64(0.638), np.float64(0.137), np.float64(0.091)), ('L2H3', np.float64(0.594), np.float64(0.158), np.float64(0.082))]

SPACE MLP0-64
L2H0 [('L0H4', np.float64(0.183), np.float64(0.022), np.float64(0.393)), ('L0H1', np.float64(0.106), np.float64(0.015), np.float64(0.761))]
L2H1 [('L0H4', np.float64(0.193), np.float64(0.021), np.float64(0.225)), ('L1H3', np.float64(0.173), np.float64(0.012), np.float64(0.323))]
L2H2 [('L0H4', np.float64(0.223), np.float64(0.028), np.float64(0.414)), ('L0H2', np.float64(0.173), np.float64(-0.009), np.float64(0.051))]
L2H3 [('L0H4', np.float64(0.147), np.float64(0.026), np.float64(0.327)), ('L0H1', np.float64(0.125), np.float64(0.036), np.float64(0.602))]
L2H4 [('L0H4', np.float64(0.266), np.float64(0.015), np.float64(0.376)), ('L0H0', np.float64(0.19), np.float64(-0.014), np.float64(0.86))]
L2H5 [('L0H4', np.float64(0.188), np.float64(0.02), np.float64(0.359)), ('L0H0', np.float64(0.137), np.float64(-0.007), np.float64(0.818))]
L3H0 [('L0H4', np.float64(0.113), np.float64(0.005), np.float64(0.149)), ('L0H0', np.float64(0.085), np.float64(0.009), np.float64(0.337))]
L3H1 [('L0H2', np.float64(2.527), np.float64(0.42), np.float64(0.138)), ('L2H1', np.float64(2.124), np.float64(0.193), np.float64(0.452))]
L3H2 [('L0H2', np.float64(0.221), np.float64(0.027), np.float64(0.042)), ('L1H0', np.float64(0.135), np.float64(-0.003), np.float64(0.26))]
L3H3 [('L0H2', np.float64(0.308), np.float64(0.006), np.float64(0.044)), ('L1H3', np.float64(0.103), np.float64(0.002), np.float64(0.2))]
L3H4 [('L2H5', np.float64(0.144), np.float64(0.009), np.float64(0.271)), ('L1H2', np.float64(0.117), np.float64(-0.008), np.float64(0.256))]
L3H5 [('L2H3', np.float64(0.568), np.float64(0.094), np.float64(0.153)), ('L1H0', np.float64(0.364), np.float64(0.068), np.float64(0.168))]

SPACE MLP1-64
L2H0 [('L1H1', np.float64(0.019), np.float64(0.033), np.float64(0.191)), ('L1H0', np.float64(-0.044), np.float64(0.053), np.float64(0.263))]
L2H1 [('L0H3', np.float64(0.134), np.float64(0.019), np.float64(0.044)), ('L0H4', np.float64(0.038), np.float64(0.02), np.float64(0.127))]
L2H2 [('L1H4', np.float64(0.405), np.float64(0.086), np.float64(0.116)), ('L1H0', np.float64(0.266), np.float64(0.053), np.float64(0.212))]
L2H3 [('L0H1', np.float64(0.129), np.float64(0.015), np.float64(0.224)), ('L1H0', np.float64(0.127), np.float64(-0.015), np.float64(0.163))]
L2H4 [('L0H1', np.float64(0.221), np.float64(0.011), np.float64(0.262)), ('L0H0', np.float64(0.189), np.float64(0.012), np.float64(0.322))]
L2H5 [('L1H4', np.float64(0.118), np.float64(0.049), np.float64(0.187)), ('L0H4', np.float64(0.06), np.float64(-0.0), np.float64(0.133))]
L3H0 [('L0H4', np.float64(0.105), np.float64(0.002), np.float64(0.094)), ('L0H5', np.float64(0.052), np.float64(0.008), np.float64(0.246))]
L3H1 [('L2H1', np.float64(2.796), np.float64(0.367), np.float64(0.452)), ('L2H3', np.float64(1.692), np.float64(0.103), np.float64(0.506))]
L3H2 [('L2H0', np.float64(0.203), np.float64(-0.01), np.float64(0.218)), ('L1H0', np.float64(0.183), np.float64(0.023), np.float64(0.112))]
L3H3 [('L1H0', np.float64(0.306), np.float64(-0.014), np.float64(0.103)), ('L1H1', np.float64(0.144), np.float64(0.003), np.float64(0.097))]
L3H4 [('L0H4', np.float64(0.326), np.float64(0.081), np.float64(0.122)), ('L0H1', np.float64(0.306), np.float64(0.07), np.float64(0.216))]
L3H5 [('L2H3', np.float64(0.697), np.float64(0.044), np.float64(0.217)), ('L2H4', np.float64(0.642), np.float64(0.047), np.float64(0.143))]

Direct-match versus predecessor-match on V0H2's 24-dimensional read space
L1H0 [[-1.058 -0.145  0.125]
 [-0.266  0.022  0.033]
 [-1.401  0.322  0.063]]
L1H1 [[-1.489  0.037  0.215]
 [-0.025  0.224  0.074]
 [-0.682  0.264  0.096]]
L1H2 [[-2.134  0.16   0.226]
 [ 0.636  0.078  0.08 ]
 [ 0.585  0.025  0.111]]
L1H3 [[-1.368 -0.039  0.186]
 [ 0.674  0.128  0.065]
 [ 0.574  0.115  0.119]]
L1H4 [[-1.568  0.097  0.221]
 [ 0.688  0.089  0.065]
 [-0.278  0.076  0.142]]
L1H5 [[-1.199 -0.049  0.201]
 [ 0.201  0.022  0.086]
 [-0.681  0.047  0.12 ]]
L2H0 [[-0.425 -0.178  0.207]
 [-0.451 -0.014  0.097]
 [ 0.019  0.11   0.089]]
L2H1 [[-0.374  0.088  0.157]
 [-0.5    0.118  0.071]
 [ 1.192  0.104  0.095]]
L2H2 [[-0.456 -0.18   0.149]
 [ 0.121  0.031  0.049]
 [-0.371  0.072  0.057]]
L2H3 [[-0.789 -0.266  0.175]
 [ 0.266 -0.009  0.068]
 [ 0.417  0.13   0.073]]
L2H4 [[-0.459 -0.172  0.209]
 [-0.578 -0.019  0.08 ]
 [-0.156  0.032  0.086]]
L2H5 [[-0.497 -0.288  0.202]
 [-0.019  0.013  0.081]
 [ 0.287 -0.021  0.084]]
L3H0 [[ 1.23   0.246  0.157]
 [-0.128  0.014  0.079]
 [ 1.039  0.06   0.081]]
L3H1 [[ 1.236  0.271  0.139]
 [ 2.907  0.649  0.135]
 [-1.302  0.1    0.072]]
L3H2 [[ 0.162 -0.208  0.116]
 [ 0.263  0.069  0.041]
 [ 0.104  0.099  0.043]]
L3H3 [[ 0.015 -0.233  0.115]
 [-0.016 -0.083  0.043]
 [ 0.355  0.042  0.049]]
L3H4 [[ 0.341 -0.057  0.12 ]
 [-0.296  0.166  0.056]
 [ 0.142 -0.052  0.052]]
L3H5 [[0.32  0.055 0.047]
 [0.278 0.061 0.026]
 [0.123 0.06  0.02 ]]

MLP neuron weight-norm quantiles
0 read [0.37  0.389 0.423 0.967 2.255] write [0.172 0.194 0.237 1.272 1.99 ] small-write fraction 0.693
1 read [1.829 1.906 2.004 2.105 2.213] write [1.78  1.847 1.922 2.007 2.085] small-write fraction 0.004
2 read [1.928 1.971 2.01  2.055 2.095] write [1.82  1.888 1.957 2.025 2.091] small-write fraction 0.0
3 read [1.907 1.938 1.976 2.014 2.053] write [1.809 1.921 2.062 2.222 2.396] small-write fraction 0.0

```