openmoji-g1-geometric-gate-v16-85ec724-aaa03592-9b9b1699

completed passed

Hypothesis

v15 localises 27% better than v14 in view units and recovers nearly three times as much render error, yet gates itself more tightly - 0.8604 against 0.8183 - and edits fewer fields. The reason is that the break-even rule p > 1/(1+q) counts a field as recovered only when the predicted token is exact, so a model that is closer but not exacter is punished for it. Choosing the threshold by absolute view-unit error saved on the withheld calibration icons instead should lower it, let v15 act on more of what it already localises well, and raise its paired render recovery further.

Why run it

It is the last step in the chain this session established, and it tests the correction v15 forced: that the project's decode rule, like its criteria, was written in exact-token terms that point the wrong way. Every quantity it is judged on is geometric and was measured before the run.

Scope limits

Fixed-topology and geometry-only, single seed, marginal-respecting corruption at 0.35. The threshold is chosen to maximise error saved on 256 withheld icons, so it is fitted - but never on held-out data, and on icons the model did not train on. Twelve icons for renders. No run in this project has produced a recognizable icon.

Measured behaviour

Held-out loss

Table view

optimizer step	held-out loss
0	1.7438
60	1.3061
120	1.2800
180	1.2626
240	1.2428
300	1.2174
360	1.1937
420	1.1777
480	1.1648
540	1.1473
600	1.1304
660	1.1130
720	1.1032
780	1.0956
840	1.0848
900	1.0804
960	1.0731
1020	1.0662
1080	1.0590
1140	1.0548
1200	1.0500
1260	1.0486
1320	1.0462
1380	1.0446
1440	1.0405
1500	1.0373
1560	1.0348
1620	1.0357
1680	1.0308
1740	1.0322
1800	1.0262
1860	1.0288
1920	1.0267
1980	1.0251
2040	1.0238
2100	1.0235
2160	1.0203
2220	1.0233
2280	1.0207
2340	1.0189
2400	1.0191
2460	1.0219
2520	1.0177
2580	1.0183
2640	1.0172
2700	1.0209
2760	1.0216
2820	1.0172
2880	1.0154
2940	1.0130
3000	1.0110
3060	1.0118
3120	1.0102
3180	1.0076
3240	1.0164
3300	1.0126
3360	1.0089
3420	1.0053
3480	1.0032
3540	0.9980
3600	0.9966
3660	0.9950
3720	0.9882
3780	0.9889
3840	0.9880
3900	0.9958
3960	0.9875
4020	0.9938
4080	0.9838
4140	0.9782
4200	0.9890
4260	0.9804
4320	0.9817
4380	0.9717
4440	0.9734
4500	0.9848
4560	0.9686
4620	0.9827
4680	0.9712
4740	0.9791
4800	0.9686
4860	0.9630
4920	0.9706
4980	0.9684
5040	0.9686
5100	0.9650
5160	0.9719
5220	0.9635
5280	0.9696
5340	0.9731

Held-out token accuracy

aggregatechanged fieldsretained fields

Table view

optimizer step	aggregate	changed fields	retained fields
0	0.3339	0.0011	0.5118
60	0.6247	0.0000	0.9588
120	0.6434	0.0009	0.9870
180	0.6477	0.0007	0.9938
240	0.6456	0.0014	0.9901
300	0.6274	0.0029	0.9614
360	0.6046	0.0062	0.9247
420	0.5858	0.0098	0.8938
480	0.5598	0.0152	0.8511
540	0.5949	0.0158	0.9046
600	0.5921	0.0188	0.8987
660	0.5975	0.0206	0.9061
720	0.6002	0.0222	0.9092
780	0.5981	0.0242	0.9050
840	0.6011	0.0265	0.9084
900	0.6008	0.0275	0.9074
960	0.5924	0.0296	0.8934
1020	0.5967	0.0315	0.8990
1080	0.6001	0.0344	0.9026
1140	0.6035	0.0366	0.9067
1200	0.6008	0.0365	0.9026
1260	0.5987	0.0378	0.8986
1320	0.5951	0.0402	0.8919
1380	0.5943	0.0426	0.8893
1440	0.5937	0.0445	0.8874
1500	0.5942	0.0433	0.8888
1560	0.5938	0.0425	0.8886
1620	0.5918	0.0445	0.8845
1680	0.5969	0.0466	0.8913
1740	0.5855	0.0498	0.8721
1800	0.5988	0.0459	0.8945
1860	0.5941	0.0460	0.8873
1920	0.5884	0.0492	0.8768
1980	0.5967	0.0504	0.8889
2040	0.5943	0.0521	0.8842
2100	0.5966	0.0499	0.8891
2160	0.5933	0.0483	0.8848
2220	0.5934	0.0554	0.8811
2280	0.5927	0.0530	0.8813
2340	0.5947	0.0563	0.8826
2400	0.5860	0.0577	0.8685
2460	0.5887	0.0554	0.8739
2520	0.5950	0.0581	0.8821
2580	0.5978	0.0590	0.8860
2640	0.5937	0.0583	0.8800
2700	0.5913	0.0595	0.8756
2760	0.5848	0.0586	0.8663
2820	0.5893	0.0600	0.8724
2880	0.5881	0.0623	0.8693
2940	0.5905	0.0620	0.8731
3000	0.5926	0.0582	0.8783
3060	0.5910	0.0595	0.8752
3120	0.5949	0.0638	0.8790
3180	0.5934	0.0626	0.8772
3240	0.5848	0.0641	0.8633
3300	0.5852	0.0653	0.8632
3360	0.5902	0.0620	0.8726
3420	0.5853	0.0672	0.8624
3480	0.5841	0.0660	0.8611
3540	0.5926	0.0679	0.8732
3600	0.5959	0.0660	0.8792
3660	0.5945	0.0690	0.8756
3720	0.5980	0.0695	0.8806
3780	0.5939	0.0701	0.8740
3840	0.5970	0.0672	0.8803
3900	0.5884	0.0694	0.8660
3960	0.5965	0.0707	0.8777
4020	0.5907	0.0706	0.8689
4080	0.5935	0.0709	0.8730
4140	0.5974	0.0681	0.8805
4200	0.5958	0.0701	0.8770
4260	0.5934	0.0714	0.8726
4320	0.5956	0.0755	0.8738
4380	0.6012	0.0695	0.8856
4440	0.5959	0.0706	0.8769
4500	0.5957	0.0702	0.8768
4560	0.6021	0.0733	0.8849
4620	0.5961	0.0737	0.8754
4680	0.6011	0.0692	0.8856
4740	0.5968	0.0742	0.8763
4800	0.5992	0.0756	0.8793
4860	0.6011	0.0754	0.8823
4920	0.5972	0.0775	0.8751
4980	0.6036	0.0725	0.8877
5040	0.6009	0.0763	0.8815
5100	0.6035	0.0757	0.8858
5160	0.6004	0.0760	0.8809
5220	0.6005	0.0762	0.8808
5280	0.5974	0.0795	0.8744
5340	0.5953	0.0764	0.8728

Training token accuracy (per batch)

Table view

optimizer step	train accuracy
1	0.3187
2	0.3991
3	0.5024
4	0.5082
5	0.5134
6	0.5537
7	0.5227
8	0.5408
9	0.5465
10	0.5159
11	0.5009
12	0.4840
13	0.4698
14	0.4691
15	0.4758
16	0.5203
17	0.5316
18	0.5613
19	0.5454
20	0.5635
21	0.5433
22	0.5689
23	0.5786
24	0.5895
25	0.5669
26	0.5889
27	0.5455
28	0.5855
29	0.5657
30	0.5644
31	0.5751
32	0.5642
33	0.5568
34	0.5753
35	0.5825
36	0.5959
37	0.5787
38	0.5895
39	0.5980
40	0.6176
41	0.6109
42	0.6219
43	0.5844
44	0.6237
45	0.6309
46	0.6145
47	0.6142
48	0.6178
49	0.6035
50	0.6240
51	0.6156
52	0.6223
53	0.6153
54	0.6152
55	0.6106
56	0.6036
57	0.6228
58	0.6229
59	0.6278
60	0.6148
61	0.6207
62	0.6101
63	0.6247
64	0.6374
65	0.6438
66	0.6436
67	0.6210
68	0.6449
69	0.6212
70	0.6367
71	0.6442
72	0.6361
73	0.6470
74	0.6243
75	0.6405
76	0.6299
77	0.6308
78	0.6298
79	0.5999
80	0.6400
81	0.6292
82	0.6337
83	0.6183
84	0.6358
85	0.6380
86	0.6288
87	0.6284
88	0.6236
89	0.6434
90	0.6419
91	0.6398
92	0.6353
93	0.6062
94	0.6502
95	0.6456
96	0.6356
97	0.6366
98	0.6513
99	0.6495
100	0.6407
101	0.6483
102	0.6403
103	0.6474
104	0.6399
105	0.6588
106	0.6434
107	0.6402
108	0.6333
109	0.6611
110	0.6474
111	0.6477
112	0.6433
113	0.6415
114	0.6479
115	0.6430
116	0.6475
117	0.6385
118	0.6430
119	0.6414
120	0.6602
121	0.6436
122	0.6591
123	0.6383
124	0.6468
125	0.6559
126	0.6458
127	0.6410
128	0.6488
129	0.6414
130	0.6464
131	0.6339
132	0.6375
133	0.6355
134	0.6416
135	0.6189
136	0.6341
137	0.6363
138	0.6493
139	0.6434
140	0.6359
141	0.6440
142	0.6468
143	0.6432
144	0.6362
145	0.6431
146	0.6454
147	0.6470
148	0.6373
149	0.6418
150	0.6356
151	0.6360
152	0.6365
153	0.6420
154	0.6469
155	0.6493
156	0.6443
157	0.6370
158	0.6463
159	0.6467
160	0.6503
161	0.6523
162	0.6392
163	0.6463
164	0.6507
165	0.6480
166	0.6501
167	0.6459
168	0.6358
169	0.6390
170	0.6567
171	0.6576
172	0.6474
173	0.6606
174	0.6428
175	0.6458
176	0.6436
177	0.6554
178	0.6346
179	0.6390
180	0.6497
181	0.6618
182	0.6544
183	0.6450
184	0.6461
185	0.6427
186	0.6416
187	0.6362
188	0.6395
189	0.6537
190	0.6500
191	0.6462
192	0.6476
193	0.6540
194	0.6478
195	0.6522
196	0.6425
197	0.6522
198	0.6462
199	0.6508
200	0.6435
201	0.6522
202	0.6410
203	0.6474
204	0.6403
205	0.6461
206	0.6413
207	0.6326
208	0.6385
209	0.6343
210	0.6458
211	0.6363
212	0.6464
213	0.6513
214	0.6490
215	0.6425
216	0.6491
217	0.6527
218	0.6441
219	0.6469
220	0.6443
221	0.6456
222	0.6537
223	0.6582
224	0.6577
225	0.6402
226	0.6543
227	0.6445
228	0.6395
229	0.6550
230	0.6498
231	0.6492
232	0.6396
233	0.6546
234	0.6442
235	0.6449
236	0.6536
237	0.6431
238	0.6416
239	0.6394
240	0.6471
241	0.6491
242	0.6305
243	0.6294
244	0.6449
245	0.6397
246	0.6255
247	0.6459
248	0.6516
249	0.6385
250	0.6561
251	0.6614
252	0.6475
253	0.6431
254	0.6466
255	0.6398
256	0.6460
257	0.6549
258	0.6396
259	0.6413
260	0.6395
261	0.6409
262	0.6459
263	0.6554
264	0.6439
265	0.6308
266	0.6379
267	0.6386
268	0.6415
269	0.6404
270	0.6359
271	0.6433
272	0.6382
273	0.6560
274	0.6465
275	0.6646
276	0.6513
277	0.6383
278	0.6459
279	0.6394
280	0.6469
281	0.6446
282	0.6529
283	0.6251
284	0.6359
285	0.6378
286	0.6454
287	0.6412
288	0.6538
289	0.6429
290	0.6380
291	0.6586
292	0.6298
293	0.6136
294	0.6194
295	0.6071
296	0.6126
297	0.6140
298	0.6226
299	0.6256
300	0.6379
301	0.6273
302	0.6324
303	0.6265
304	0.6121
305	0.6210
306	0.6213
307	0.6187
308	0.6168
309	0.6154
310	0.6267
311	0.6349
312	0.6256
313	0.6297
314	0.6153
315	0.5969
316	0.5945
317	0.5949
318	0.6107
319	0.5933
320	0.6099
321	0.6138
322	0.6126
323	0.6089
324	0.6115
325	0.6027
326	0.6078
327	0.5972
328	0.6205
329	0.6056
330	0.6185
331	0.6092
332	0.5952
333	0.6026
334	0.6049
335	0.6057
336	0.6063
337	0.6022
338	0.6063
339	0.5797
340	0.6045
341	0.6007
342	0.6061
343	0.6092
344	0.6135
345	0.5990
346	0.6000
347	0.5973
348	0.5905
349	0.6000
350	0.6137
351	0.6056
352	0.6171
353	0.6032
354	0.6134
355	0.5973
356	0.6018
357	0.5836
358	0.5999
359	0.5921
360	0.5980
361	0.6079
362	0.6037
363	0.6015
364	0.5925
365	0.5820
366	0.5899
367	0.5686
368	0.5853
369	0.5972
370	0.6023
371	0.6139
372	0.5999
373	0.5877
374	0.5814
375	0.5969
376	0.5716
377	0.5953
378	0.6091
379	0.6063
380	0.6248
381	0.6113
382	0.6093
383	0.5838
384	0.5731
385	0.5733
386	0.5848
387	0.5893
388	0.5886
389	0.5946
390	0.6182
391	0.5998
392	0.5962
393	0.5830
394	0.5843
395	0.5694
396	0.5948
397	0.6023
398	0.5910
399	0.5841
400	0.5934
401	0.5672
402	0.5781
403	0.5792
404	0.5892
405	0.5860
406	0.5958
407	0.6014
408	0.5826
409	0.5925
410	0.5886
411	0.5692
412	0.5803
413	0.5891
414	0.6057
415	0.5941
416	0.5961
417	0.5770
418	0.5795
419	0.5708
420	0.5862
421	0.5821
422	0.5897
423	0.5960
424	0.6053
425	0.5707
426	0.5807
427	0.5605
428	0.5749
429	0.5952
430	0.6039
431	0.6101
432	0.5936
433	0.6021
434	0.5746
435	0.5861
436	0.5652
437	0.5685
438	0.5811
439	0.5760
440	0.6031
441	0.6017
442	0.6088
443	0.6140
444	0.5923
445	0.5944
446	0.5791
447	0.5901
448	0.5956
449	0.5790
450	0.5871
451	0.6040
452	0.5841
453	0.6036
454	0.5997
455	0.5848
456	0.5834
457	0.6043
458	0.5831
459	0.5931
460	0.5838
461	0.5778
462	0.5780
463	0.5748
464	0.5742
465	0.5836
466	0.5770
467	0.5907
468	0.5959
469	0.5816
470	0.5829
471	0.5814
472	0.5698
473	0.5698
474	0.5752
475	0.5873
476	0.6006
477	0.6076
478	0.5960
479	0.5796
480	0.5809
481	0.5615
482	0.5636
483	0.5701
484	0.5886
485	0.6059
486	0.6220
487	0.6096
488	0.5958
489	0.5881
490	0.5663
491	0.5778
492	0.5874
493	0.5916
494	0.5991
495	0.5956
496	0.6067
497	0.6026
498	0.5854
499	0.5849
500	0.5768
501	0.5742
502	0.5909
503	0.6030
504	0.6087
505	0.6090
506	0.5973
507	0.5847
508	0.5749
509	0.5716
510	0.5977
511	0.5789
512	0.5975
513	0.6112
514	0.6125
515	0.6217
516	0.6122
517	0.6072
518	0.6035
519	0.5788
520	0.5780
521	0.5953
522	0.5991
523	0.6067
524	0.6024
525	0.6031
526	0.5911
527	0.5771
528	0.5731
529	0.5795
530	0.5880
531	0.6014
532	0.6012
533	0.6146
534	0.5992
535	0.5981
536	0.5891
537	0.6171
538	0.5999
539	0.6014
540	0.6000
541	0.5856
542	0.6012
543	0.5944
544	0.5807
545	0.5900
546	0.5958
547	0.5831
548	0.5820
549	0.5923
550	0.5937
551	0.5801
552	0.5976
553	0.5989
554	0.6046
555	0.5985
556	0.6020
557	0.6098
558	0.6033
559	0.6140
560	0.5914
561	0.5900
562	0.5875
563	0.6050
564	0.6174
565	0.6063
566	0.6072
567	0.6165
568	0.5948
569	0.6113
570	0.6062
571	0.6007
572	0.5914
573	0.5980
574	0.6202
575	0.6113
576	0.6033
577	0.6087
578	0.5951
579	0.6044
580	0.5980
581	0.6021
582	0.6046
583	0.6093
584	0.6017
585	0.6104
586	0.5841
587	0.6224
588	0.5866
589	0.5897
590	0.5854
591	0.5961
592	0.5876
593	0.6018
594	0.5965
595	0.6040
596	0.6168
597	0.6141
598	0.6142
599	0.5968
600	0.5829
601	0.5813
602	0.5970
603	0.6030
604	0.5916
605	0.6252
606	0.6190
607	0.5949
608	0.6029
609	0.6013
610	0.6061
611	0.5996
612	0.5846
613	0.5914
614	0.6210
615	0.6197
616	0.6183
617	0.5985
618	0.6009
619	0.6102
620	0.6060
621	0.6086
622	0.6037
623	0.6028
624	0.6089
625	0.6093
626	0.6312
627	0.6131
628	0.6174
629	0.6172
630	0.6064
631	0.6137
632	0.6079
633	0.6166
634	0.5910
635	0.6052
636	0.6018
637	0.5924
638	0.5985
639	0.6054
640	0.6175
641	0.6006
642	0.6145
643	0.6176
644	0.6107
645	0.6164
646	0.6071
647	0.6016
648	0.6140
649	0.6032
650	0.6150
651	0.6001
652	0.6078
653	0.5887
654	0.6102
655	0.6071
656	0.6098
657	0.5935
658	0.5986
659	0.5975
660	0.5863
661	0.6066
662	0.5845
663	0.6190
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1600	0.6261
1601	0.6391
1602	0.6038
1603	0.6101
1604	0.6151
1605	0.6125
1606	0.6459
1607	0.6422
1608	0.6128
1609	0.5921
1610	0.5978
1611	0.6408
1612	0.6152
1613	0.6287
1614	0.6161
1615	0.6137
1616	0.6379
1617	0.6227
1618	0.6463
1619	0.6078
1620	0.6371
1621	0.6306
1622	0.6027
1623	0.6123
1624	0.6486
1625	0.6409
1626	0.6204
1627	0.6502
1628	0.6117
1629	0.6077
1630	0.6158
1631	0.6231
1632	0.6154
1633	0.6129
1634	0.6148
1635	0.6262
1636	0.5768
1637	0.6273
1638	0.6277
1639	0.6221
1640	0.6221
1641	0.6086
1642	0.6041
1643	0.6005
1644	0.6455
1645	0.6510
1646	0.6302
1647	0.6092
1648	0.6466
1649	0.6213
1650	0.6080
1651	0.6182
1652	0.6499
1653	0.6237
1654	0.6401
1655	0.6197
1656	0.6331
1657	0.6061
1658	0.6397
1659	0.6192
1660	0.6021
1661	0.6208
1662	0.6155
1663	0.5939
1664	0.6295
1665	0.6252
1666	0.6518
1667	0.6339
1668	0.6045
1669	0.6163
1670	0.6087
1671	0.6589
1672	0.6172
1673	0.5990
1674	0.6068
1675	0.6217
1676	0.6447
1677	0.6398
1678	0.6016
1679	0.6170
1680	0.6254
1681	0.6490
1682	0.6496
1683	0.6345
1684	0.6233
1685	0.6283
1686	0.6584
1687	0.6675
1688	0.6140
1689	0.6018
1690	0.6319
1691	0.6244
1692	0.6297
1693	0.6393
1694	0.6402
1695	0.6223
1696	0.6226
1697	0.6350
1698	0.6166
1699	0.6232
1700	0.6269
1701	0.6635
1702	0.6294
1703	0.6502
1704	0.6186
1705	0.6196
1706	0.6333
1707	0.6103
1708	0.6055
1709	0.6551
1710	0.6087
1711	0.6210
1712	0.6211
1713	0.6012
1714	0.6020
1715	0.6123
1716	0.6257
1717	0.6238
1718	0.6501
1719	0.6208
1720	0.6039
1721	0.6239
1722	0.6258
1723	0.6217
1724	0.6260
1725	0.6188
1726	0.6394
1727	0.6210
1728	0.6253
1729	0.6219
1730	0.6440
1731	0.6103
1732	0.6436
1733	0.6388
1734	0.6025
1735	0.6141
1736	0.6525
1737	0.6320
1738	0.6335
1739	0.6502
1740	0.6131
1741	0.6357
1742	0.5765
1743	0.6223
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1745	0.6130
1746	0.6341
1747	0.6069
1748	0.6143
1749	0.6302
1750	0.6469
1751	0.6257
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1753	0.6196
1754	0.6044
1755	0.6197
1756	0.6122
1757	0.6352
1758	0.6368
1759	0.6483
1760	0.5945
1761	0.5848
1762	0.6251
1763	0.6169
1764	0.6098
1765	0.6293
1766	0.6118
1767	0.6426
1768	0.6230
1769	0.6259
1770	0.6242
1771	0.6474
1772	0.6253
1773	0.6239
1774	0.6112
1775	0.6399
1776	0.6376
1777	0.6385
1778	0.6206
1779	0.6212
1780	0.6056
1781	0.6237
1782	0.6037
1783	0.6208
1784	0.6144
1785	0.6361
1786	0.6266
1787	0.6228
1788	0.6067
1789	0.6390
1790	0.6211
1791	0.6077
1792	0.6187
1793	0.5974
1794	0.6126
1795	0.6280
1796	0.6214
1797	0.6724
1798	0.5889
1799	0.6171
1800	0.6322
1801	0.6087
1802	0.6300
1803	0.6604
1804	0.6203
1805	0.6429
1806	0.6317
1807	0.6283
1808	0.6214
1809	0.6310
1810	0.6488
1811	0.6020
1812	0.6188
1813	0.6208
1814	0.6300
1815	0.6206
1816	0.6247
1817	0.6410
1818	0.6378
1819	0.6479
1820	0.6015
1821	0.6461
1822	0.6287
1823	0.6442
1824	0.6022
1825	0.6103
1826	0.6217
1827	0.6243
1828	0.6340
1829	0.6147
1830	0.5826
1831	0.6629
1832	0.6298
1833	0.6289
1834	0.6547
1835	0.6136
1836	0.6244
1837	0.6109
1838	0.6616
1839	0.6354
1840	0.6133
1841	0.5934
1842	0.6404
1843	0.6351
1844	0.6346
1845	0.6334
1846	0.6331
1847	0.6454
1848	0.6161
1849	0.6471
1850	0.6193
1851	0.6456
1852	0.6064
1853	0.6435
1854	0.6381
1855	0.6437
1856	0.6310
1857	0.6314
1858	0.6345
1859	0.5927
1860	0.6512
1861	0.6379
1862	0.6091
1863	0.6562
1864	0.6229
1865	0.6094
1866	0.5942
1867	0.6271
1868	0.6401
1869	0.5854
1870	0.6413
1871	0.6236
1872	0.6138
1873	0.6106
1874	0.6391
1875	0.5828
1876	0.6260
1877	0.6120
1878	0.6483
1879	0.6048
1880	0.6298
1881	0.6351
1882	0.6520
1883	0.6319
1884	0.6416
1885	0.6458
1886	0.6082
1887	0.6732
1888	0.6054
1889	0.6179
1890	0.6524
1891	0.6538
1892	0.6189
1893	0.6391
1894	0.5962
1895	0.6315
1896	0.5996
1897	0.6516
1898	0.6110
1899	0.6189
1900	0.6090
1901	0.6450
1902	0.6240
1903	0.6342
1904	0.6466
1905	0.6153
1906	0.6300
1907	0.6252
1908	0.6231
1909	0.6336
1910	0.6396
1911	0.6408
1912	0.6040
1913	0.6140
1914	0.6349
1915	0.6319
1916	0.6362
1917	0.6216
1918	0.5938
1919	0.6408
1920	0.6396
1921	0.6324
1922	0.6186
1923	0.6321
1924	0.6370
1925	0.6265
1926	0.6097
1927	0.6462
1928	0.6366
1929	0.6080
1930	0.6517
1931	0.6025
1932	0.6148
1933	0.6102
1934	0.6311
1935	0.6134
1936	0.6146
1937	0.6145
1938	0.6562
1939	0.6069
1940	0.6175
1941	0.6247
1942	0.6188
1943	0.6065
1944	0.6309
1945	0.6259
1946	0.6249
1947	0.6582
1948	0.6420
1949	0.6520
1950	0.5891
1951	0.6490
1952	0.6241
1953	0.6194
1954	0.6120
1955	0.6621
1956	0.6014
1957	0.6532
1958	0.6084
1959	0.6314
1960	0.6356
1961	0.6504
1962	0.6279
1963	0.6148
1964	0.6280
1965	0.6266
1966	0.6238
1967	0.6312
1968	0.6145
1969	0.6513
1970	0.6224
1971	0.6127
1972	0.6208
1973	0.6213
1974	0.6469
1975	0.6374
1976	0.6125
1977	0.6137
1978	0.6193
1979	0.6273
1980	0.6229
1981	0.6120
1982	0.6095
1983	0.6533
1984	0.6394
1985	0.6529
1986	0.6401
1987	0.6303
1988	0.6445
1989	0.6478
1990	0.6646
1991	0.6053
1992	0.6202
1993	0.6451
1994	0.6080
1995	0.6440
1996	0.6386
1997	0.6546
1998	0.6286
1999	0.6361
2000	0.6140
2001	0.6326
2002	0.6421
2003	0.6293
2004	0.6260
2005	0.6372
2006	0.6331
2007	0.6375
2008	0.6333
2009	0.6282
2010	0.6144
2011	0.6168
2012	0.6637
2013	0.6199
2014	0.6352
2015	0.6377
2016	0.5999
2017	0.6410
2018	0.6176
2019	0.6401
2020	0.6363
2021	0.6184
2022	0.6299
2023	0.6039
2024	0.6253
2025	0.6306
2026	0.6473
2027	0.6230
2028	0.6288
2029	0.6298
2030	0.6373
2031	0.6196
2032	0.6308
2033	0.6554
2034	0.5923
2035	0.6309
2036	0.6292
2037	0.6180
2038	0.6166
2039	0.6525
2040	0.6286
2041	0.6442
2042	0.6692
2043	0.6233
2044	0.6677
2045	0.6135
2046	0.6145
2047	0.6350
2048	0.6214
2049	0.6363
2050	0.6111
2051	0.6171
2052	0.6250
2053	0.6624
2054	0.6197
2055	0.6299
2056	0.6413
2057	0.6295
2058	0.6243
2059	0.6206
2060	0.6294
2061	0.6472
2062	0.6640
2063	0.6158
2064	0.5880
2065	0.6286
2066	0.6333
2067	0.6192
2068	0.6257
2069	0.6138
2070	0.6349
2071	0.6212
2072	0.6153
2073	0.6217
2074	0.6348
2075	0.6402
2076	0.6104
2077	0.6128
2078	0.6302
2079	0.6246
2080	0.6505
2081	0.6253
2082	0.6331
2083	0.6173
2084	0.6298
2085	0.6169
2086	0.6350
2087	0.6075
2088	0.6357
2089	0.6141
2090	0.6073
2091	0.6151
2092	0.6145
2093	0.6291
2094	0.6128
2095	0.6417
2096	0.6250
2097	0.6292
2098	0.6372
2099	0.6527
2100	0.6509
2101	0.6381
2102	0.6279
2103	0.6145
2104	0.6122
2105	0.6152
2106	0.6563
2107	0.6324
2108	0.6465
2109	0.6365
2110	0.6078
2111	0.6634
2112	0.6423
2113	0.6537
2114	0.6133
2115	0.6518
2116	0.6139
2117	0.6553
2118	0.6145
2119	0.6381
2120	0.6271
2121	0.6336
2122	0.6542
2123	0.5936
2124	0.6351
2125	0.6410
2126	0.6515
2127	0.6131
2128	0.5989
2129	0.6258
2130	0.6463
2131	0.6323
2132	0.6253
2133	0.5916
2134	0.6523
2135	0.6235
2136	0.6580
2137	0.6607
2138	0.6428
2139	0.6416
2140	0.6353
2141	0.6793
2142	0.6423
2143	0.6108
2144	0.6092
2145	0.6452
2146	0.6353
2147	0.6554
2148	0.6263
2149	0.6321
2150	0.6592
2151	0.6212
2152	0.6351
2153	0.6245
2154	0.6524
2155	0.6110
2156	0.6675
2157	0.6330
2158	0.6512
2159	0.6191
2160	0.6418
2161	0.6188
2162	0.6231
2163	0.6668
2164	0.6410
2165	0.6103
2166	0.6545
2167	0.6405
2168	0.6340
2169	0.6171
2170	0.6402
2171	0.6326
2172	0.6035
2173	0.6446
2174	0.6173
2175	0.6028
2176	0.6292
2177	0.6534
2178	0.5853
2179	0.6285
2180	0.6304
2181	0.6396
2182	0.6321
2183	0.6440
2184	0.6074
2185	0.6463
2186	0.6334
2187	0.6331
2188	0.6168
2189	0.5840
2190	0.6395
2191	0.6197
2192	0.6264
2193	0.6537
2194	0.6415
2195	0.6411
2196	0.6343
2197	0.6361
2198	0.6264
2199	0.6150
2200	0.6295
2201	0.6297
2202	0.6032
2203	0.6355
2204	0.6477
2205	0.6355
2206	0.6156
2207	0.6183
2208	0.6423
2209	0.5991
2210	0.6151
2211	0.6405
2212	0.6398
2213	0.6375
2214	0.6464
2215	0.5775
2216	0.5961
2217	0.6171
2218	0.6281
2219	0.6213
2220	0.6204
2221	0.5958
2222	0.6436
2223	0.6697
2224	0.6427
2225	0.6089
2226	0.6455
2227	0.6295
2228	0.6166
2229	0.6158
2230	0.6407
2231	0.6501
2232	0.5949
2233	0.6703
2234	0.6159
2235	0.6104
2236	0.6162
2237	0.6385
2238	0.6129
2239	0.6211
2240	0.6217
2241	0.6470
2242	0.6102
2243	0.6333
2244	0.6341
2245	0.6083
2246	0.6032
2247	0.6308
2248	0.6060
2249	0.6265
2250	0.6620
2251	0.6382
2252	0.6867
2253	0.6134
2254	0.6324
2255	0.6074
2256	0.6417
2257	0.6299
2258	0.6800
2259	0.6034
2260	0.6571
2261	0.6273
2262	0.6384
2263	0.6393
2264	0.6560
2265	0.6443
2266	0.6185
2267	0.6175
2268	0.6337
2269	0.6468
2270	0.6343
2271	0.6296
2272	0.6452
2273	0.6640
2274	0.6177
2275	0.6251
2276	0.6427
2277	0.6388
2278	0.6519
2279	0.6282
2280	0.6217
2281	0.6315
2282	0.6295
2283	0.6554
2284	0.6126
2285	0.6008
2286	0.6638
2287	0.6345
2288	0.6621
2289	0.6554
2290	0.6416
2291	0.6449
2292	0.6555
2293	0.6787
2294	0.6094
2295	0.6024
2296	0.6379
2297	0.6403
2298	0.6460
2299	0.6406
2300	0.6180
2301	0.6501
2302	0.6681
2303	0.6277
2304	0.6393
2305	0.6334
2306	0.6399
2307	0.6019
2308	0.6782
2309	0.6298
2310	0.6537
2311	0.6291
2312	0.6302
2313	0.6393
2314	0.6045
2315	0.6736
2316	0.6206
2317	0.6439
2318	0.6472
2319	0.6095
2320	0.6272
2321	0.6102
2322	0.6296
2323	0.6325
2324	0.6227
2325	0.6356
2326	0.5882
2327	0.6292
2328	0.6475
2329	0.6411
2330	0.6285
2331	0.6445
2332	0.6330
2333	0.6354
2334	0.6478
2335	0.6299
2336	0.6427
2337	0.6441
2338	0.6275
2339	0.6486
2340	0.6392
2341	0.5861
2342	0.6536
2343	0.6197
2344	0.6477
2345	0.6628
2346	0.6268
2347	0.6863
2348	0.6105
2349	0.6343
2350	0.6323
2351	0.6103
2352	0.6335
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2354	0.6217
2355	0.6118
2356	0.6584
2357	0.6207
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2359	0.6587
2360	0.6460
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2363	0.6491
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2365	0.6743
2366	0.6173
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2374	0.6264
2375	0.6259
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2378	0.6565
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2380	0.6078
2381	0.6240
2382	0.6466
2383	0.6425
2384	0.6068
2385	0.6645
2386	0.6065
2387	0.6199
2388	0.6161
2389	0.6401
2390	0.6268
2391	0.6389
2392	0.6260
2393	0.6409
2394	0.6264
2395	0.6302
2396	0.6434
2397	0.5964
2398	0.6227
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2400	0.5960
2401	0.6274
2402	0.6475
2403	0.6566
2404	0.6423
2405	0.6285
2406	0.6244
2407	0.6365
2408	0.6301
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2410	0.6416
2411	0.6511
2412	0.6412
2413	0.6171
2414	0.6466
2415	0.6345
2416	0.6629
2417	0.5969
2418	0.6407
2419	0.6194
2420	0.6274
2421	0.6230
2422	0.6357
2423	0.6194
2424	0.6681
2425	0.6427
2426	0.6304
2427	0.6490
2428	0.6255
2429	0.6459
2430	0.6043
2431	0.5849
2432	0.6373
2433	0.6393
2434	0.6380
2435	0.6261
2436	0.6152
2437	0.6137
2438	0.6505
2439	0.6561
2440	0.6674
2441	0.6514
2442	0.6315
2443	0.6560
2444	0.6598
2445	0.6774
2446	0.6072
2447	0.6211
2448	0.6633
2449	0.6336
2450	0.6571
2451	0.6412
2452	0.6311
2453	0.6607
2454	0.6403
2455	0.6416
2456	0.6239
2457	0.6364
2458	0.6228
2459	0.6827
2460	0.6332
2461	0.6456
2462	0.6005
2463	0.6525
2464	0.6169
2465	0.6229
2466	0.6672
2467	0.6523
2468	0.5888
2469	0.6505
2470	0.6278
2471	0.6162
2472	0.6264
2473	0.6305
2474	0.6514
2475	0.6050
2476	0.6502
2477	0.6305
2478	0.6097
2479	0.6392
2480	0.6634
2481	0.5982
2482	0.6363
2483	0.6190
2484	0.6533
2485	0.6223
2486	0.6320
2487	0.6211
2488	0.6552
2489	0.6392
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2492	0.6062
2493	0.6508
2494	0.6365
2495	0.6499
2496	0.6403
2497	0.6528
2498	0.6441
2499	0.6463
2500	0.6211
2501	0.6351
2502	0.6007
2503	0.6452
2504	0.6226
2505	0.6213
2506	0.6327
2507	0.6284
2508	0.6493
2509	0.6139
2510	0.6480
2511	0.6429
2512	0.6346
2513	0.6273
2514	0.6366
2515	0.6515
2516	0.6644
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5241	0.6549
5242	0.6239
5243	0.6866
5244	0.6945
5245	0.6794
5246	0.6218
5247	0.6070
5248	0.6351
5249	0.6757
5250	0.6860
5251	0.6328
5252	0.6392
5253	0.6869
5254	0.6706
5255	0.6774
5256	0.6564
5257	0.6919
5258	0.6840
5259	0.6561
5260	0.6451
5261	0.6806
5262	0.6854
5263	0.6608
5264	0.6863
5265	0.6566
5266	0.6403
5267	0.6570
5268	0.6414
5269	0.6500
5270	0.6474
5271	0.6562
5272	0.6415
5273	0.6550
5274	0.6517
5275	0.6787
5276	0.6422
5277	0.6412
5278	0.6608
5279	0.6351
5280	0.6390
5281	0.6731
5282	0.6830
5283	0.6943
5284	0.6249
5285	0.6677
5286	0.6651
5287	0.6598
5288	0.6578
5289	0.7066
5290	0.6595
5291	0.6996
5292	0.6796
5293	0.6686
5294	0.6674
5295	0.6950
5296	0.6957
5297	0.6284
5298	0.6635
5299	0.6594
5300	0.6747
5301	0.6575
5302	0.6431
5303	0.6745
5304	0.6823
5305	0.6803
5306	0.6399
5307	0.6621
5308	0.6718
5309	0.6847
5310	0.6383
5311	0.6314
5312	0.6746
5313	0.6443
5314	0.6902
5315	0.6399
5316	0.6319
5317	0.7179
5318	0.6664
5319	0.6984
5320	0.6839
5321	0.6664
5322	0.6756
5323	0.6744
5324	0.7417
5325	0.6673
5326	0.6520
5327	0.6503
5328	0.6803
5329	0.6854
5330	0.6901
5331	0.6663
5332	0.6534
5333	0.6933
5334	0.6581
5335	0.6758
5336	0.6840
5337	0.6685
5338	0.6325
5339	0.7019
5340	0.6715

Measured result

Verbatim from the run's summary.json.

break_even_threshold_for_comparison

0.8603620019436345

bucket

bucket-p32-t128

bucket_icons

3359

calibration_samples

256

calibration_view_units_saved_per_field

2.9842581213934603

calibration_withheld_from_training

true

checkpoint_bytes

6463494

checkpoint_round_trip

true

checkpoint_sha256

31fa3e14e7f1b7e2…

config_sha256

aaa03592a404740b…

corruption

factorized_marginal_respecting_geometry

cuda_version

13.4

decision_threshold

0.68

deterministic_algorithms

true

device

cuda

eval_every

final_train

loss

0.8464025259017944

step

5340

train_token_accuracy

0.6715017064846417

geometric_gate

true

group_vocabulary_size

locked_path_exact

true

metrics_sha256

df740a71027f0add…

model_parameters

525152

schema_version

scope

dominant-bucket fixed-topology geometry pilot; not unconditional generation

selected_train_rows

color/svg/1F3F4-E0064-E0065-E0062-E0065-E007F.svg, color/svg/1F468-1F3FC-200D-1F9BC.svg, color/svg/1F561.svg, color/svg/1F469-1F3FE-200D-1F9BC.svg, color/svg/1F469-1F3FD-200D-2764-FE0F-200D-1F48B-200D-1F469-1F3FE.svg, color/svg/1F6B5-1F3FB-200D-2642-FE0F.svg … and 2419 more

selected_validation_rows

color/svg/1F994.svg, color/svg/E30A.svg, color/svg/1F6BE.svg, color/svg/1F3CA-1F3FB-200D-2642-FE0F.svg, color/svg/1F3CB-1F3FC-200D-2642-FE0F.svg, color/svg/1F93D-1F3FC.svg … and 122 more

selection

completed_steps

5340

evals_without_improvement

min_delta

0.0

objective

held_out_loss

patience_evals

selected_held_out_loss

0.9630383253097534

selected_step

4860

stopped_early

true

steps

6300

study_version

openmoji-g1-geometric-gate-v16

subgroup_vocabulary_size

118

torch_version

2.14.0a0+4fdf77b940.nv26.08

train_value_accuracy

0.16230144722908577

validation

accuracy

0.635372925414917

changed_accuracy

0.06721182124668244

changed_total

13941

loss

0.9630383253097534

retained_accuracy

0.9392335136379331

retained_total

26067

validation_final_step

accuracy

0.5953059388122376

changed_accuracy

0.07639337206800086

changed_total

13941

loss

0.9730569124221802

retained_accuracy

0.8728277132005985

retained_total

26067

validation_trace_sha256

159709731df021bc…

validation_untrained

accuracy

0.33385822835432916

changed_accuracy

0.0011476938526648016

changed_total

13941

loss

1.7438199520111084

retained_accuracy

0.511796524341121

retained_total

26067

value_distance_tau

1.0

Written result

v15 localised 27% better than v14 yet gated itself more tightly — 0.8604 against 0.8183 — and edited fewer fields. The break-even rule p > 1/(1+q) counts a field as recovered only when the predicted token is exact, so a model that is closer but not exacter is punished for it. This run picks the threshold by absolute view-unit error saved on the withheld calibration icons instead.

The trained weights are identical to v15's — the checkpoint hash matches — so everything below is attributable to the gate alone.

Result

criterion	threshold	observed	outcome
lowers_the_threshold	< 0.8604	0.6800	pass
improves_paired_render_recovery	> +0.1246, CI excludes zero	+0.2511	pass
saves_error_on_calibration	> 0	2.9843 view units/field	pass
structural_safety	locked-path exact, round-trips	both true	pass
reproducibility	identical rerun	identical	pass

Paired per-icon render recovery, 12 held-out icons, each model at its own threshold:

	72 px	95% CI	helped	18 px	helped
identity	0.0000	—	—	0.0000	—
v14, exact-token target, break-even gate	+0.0447	[+0.008, +0.081]	8/12	+0.0729	10/12
v15, distance kernel, break-even gate	+0.1246	[+0.061, +0.188]	10/12	+0.1306	11/12
v16, distance kernel, geometric gate	+0.2511	[+0.141, +0.361]	11/12	+0.2745	11/12

5.6x v14 and 2x v15, from changing nothing but the decode rule.

The two corrections compound, and they are the same correction applied twice: train for closeness, then gate on closeness. Either alone gets part way; together they recover a quarter of the render gap between the corrupted input and the clean program.

The tension, which was predeclared

Held-out aggregate token accuracy falls to 0.6354 against identity's 0.6515. The model now recovers 25% of the render gap while scoring worse than doing nothing on exact tokens.

That was written into this run's record before it ran, as expected_tension: editing more fields lowers exact-token accuracy while improving geometry, because most edits land near rather than on the true bin. It is the v15 finding repeating one level down, and between the two runs it is now conclusive rather than suggestive:

Exact-token accuracy is the wrong metric for this project. A model can be simultaneously worse than the identity policy on it and the best geometric denoiser the project has produced. Both statements are true of v16 and neither is a contradiction — they are a statement about the metric.

Where the whole sequence lands

run	change	render recovery, 72 px
identity	do nothing	0.0000
v2	the model before any of this	strongly negative, destroyed its input
v14	five defects fixed, exact-token target, break-even gate	+0.0447
v15	distance-kernel target	+0.1246
v16	geometric gate	+0.2511

Seven verified defects and corrections, none of them tuning knobs, and the model shrank from 579,872 parameters to 525,152 along the way.

Scope, unchanged and still binding

Fixed-topology and geometry-only, single seed, marginal-respecting corruption at 0.35, 12 icons for renders. x_hat_0 is visibly tidier than x_t on most rows — the warning triangle, the tumbler, the badge — but no render in this project is a recognizable icon, and this one is not either. At 35% corruption the input is already scribble, and recovering a quarter of that gap leaves scribble. What has been established is a measurable, compounding direction, not a working generator.

State transitions

planned2026-09-21T04:40:00Z
completed2026-09-21T05:00:00Zgating_on_closeness_doubles_render_recovery_again_to_0.2511_while_exact_token_accuracy_falls_below_identity

Run record

Verbatim from runs/openmoji-g1-geometric-gate-v16-85ec724-aaa03592-9b9b1699/run.yaml, the record committed before launch.

schema_version

run_id

openmoji-g1-geometric-gate-v16-85ec724-aaa03592-9b9b1699

state

completed

parent_run

openmoji-g1-distance-kernel-v15-6a52c12-744bad0a-9b9b1699

hypothesis

v15 localises 27% better than v14 in view units and recovers nearly three times as much render error, yet gates itself more tightly - 0.8604 against 0.8183 - and edits fewer fields. The reason is that the break-even rule p > 1/(1+q) counts a field as recovered only when the predicted token is exact, so a model that is closer but not exacter is punished for it. Choosing the threshold by absolute view-unit error saved on the withheld calibration icons instead should lower it, let v15 act on more of what it already localises well, and raise its paired render recovery further.

expected_information_gain

scope_limits

changed_factors

primary

training.geometric_gate false -> true. The decode threshold is swept on the withheld calibration icons and set to the value that saves the most absolute view-unit error, replacing the exact-token break-even rule.

parameters

unchanged at 525,152

weights_unchanged

This alters decoding and reporting only. Training is identical to v15, so the checkpoint should match and any difference is attributable entirely to the gate.

held_fixed

v15 exactly otherwise.

code

git_commit

85ec724

execution_mode

native-local

config

path

configs/learning/openmoji-g1-geometric-gate-v16.yaml

sha256

aaa03592a404740b…

dataset

hybrid_sha256

9b9b1699677a6f97…

bucket

bucket-p32-t128

trained_on

2425

calibration_samples

256

validation_samples

128

baselines

note

Primary metrics are geometric, per the correction v15 forced. Exact-token accuracy is reported as secondary and is known to point the wrong way.

v15

threshold

0.8604

paired_render_recovery_72px

0.1246

paired_render_recovery_18px

0.1306

mean_abs_view_units

6.0282

aggregate

0.6529

identity

0.6515

v14

threshold

0.8183

paired_render_recovery_72px

0.0447

mean_abs_view_units

8.2871

aggregate

0.6543

predeclared_criteria

evaluated_at

the checkpoint selected by held-out loss, decoded at the geometric threshold

primary

lowers_the_threshold

statement

The geometric threshold is below v15's break-even 0.8604. A rule that credits closeness should permit more editing than one that demands exactness.

improves_paired_render_recovery

statement

Mean paired per-icon render recovery at 72 px exceeds v15's +0.1246, with the 95% interval excluding zero. This is the primary metric.

saves_error_on_calibration

statement

The reported view-unit error saved per field on the calibration icons is positive, which is a consistency check on the estimator rather than a result.

structural_safety

statement

Locked-path exactness holds and the canonical checkpoint round-trips.

reproducibility

statement

A second complete invocation returns an identical JSON result.

reported_not_gated

held-out aggregate against identity, which may fall since the gate no longer optimises it, exact-token changed and retained accuracy, secondary by the v15 correction, the break-even threshold alongside, for direct comparison

expected_tension

Editing more fields should lower exact-token aggregate accuracy while improving geometry, because most edits land near rather than on the true bin. If aggregate falls below identity's 0.6515 while render recovery rises, that is the v15 finding repeating itself one level down, and it should be reported as such rather than as a regression.

falsification_meaning

If a geometric gate does not improve render recovery, then the threshold was not what limited v15, and the remaining constraint is the value head's absolute accuracy rather than how its output is gated. That would point back at the model - a single encoder serving both heads, or a categorical parameterisation of a metric quantity that might be better regressed - and both are larger changes to predeclare separately.

outputs

local_metadata

runs/openmoji-g1-geometric-gate-v16-85ec724-aaa03592-9b9b1699

report_root

reports/learning/openmoji-g1-geometric-gate-v16

durable_artifacts

/home/dev/.cache/openmoji-g1-geometric-gate-v16-85ec724-aaa03592-9b9b1699

planned_at

2026-09-21 04:40:00+00:00

completed_at

2026-09-21 05:00:00+00:00

result

device

cuda

identical_rerun

true

checkpoint_identical_to_v15

true

decision_threshold

0.68

break_even_threshold_for_comparison

0.8604

calibration_view_units_saved_per_field

2.9843

held_out

aggregate

0.6354

changed

0.0672

retained

0.9392

identity_baseline

0.6515

paired_render_recovery

px72

mean

0.2511

ci95

0.1408, 0.3614

helped

px18

mean

0.2745

ci95

0.1661, 0.3828

helped

v15_px72

0.1246

v14_px72

0.0447

identity

0.0

predeclared_outcome

overall

passed

lowers_the_threshold

passed

true

observed

0.68

threshold

0.8604

improves_paired_render_recovery

passed

true

observed

0.2511

threshold

0.1246

ci_excludes_zero

true

saves_error_on_calibration

passed

true

observed

2.9843

structural_safety

passed

true

reproducibility

passed

true

expected_tension_observed

statement

Held-out aggregate token accuracy falls to 0.6354 against identity's 0.6515, which this run's record anticipated before it ran. The model recovers 25% of the render gap while scoring worse than doing nothing on exact tokens.

conclusion

Between v15 and v16 this is now conclusive rather than suggestive: exact-token accuracy is the wrong metric for this project. A model can be simultaneously worse than the identity policy on it and the best geometric denoiser the project has produced, and both are true of v16.

conclusion

Changing nothing but the decode rule - the trained weights are byte-identical to v15's - takes paired render recovery from +0.1246 to +0.2511 at 72 px and +0.1306 to +0.2745 at 18 px, helping 11 of 12 icons with intervals excluding zero. That is 5.6x v14 and 2x v15. The two corrections compound because they are the same correction applied twice: train for closeness, then gate on closeness.

scope

No render in this project is a recognizable icon and this one is not either. At 35% corruption the input is already scribble and recovering a quarter of that gap leaves scribble. What is established is a measurable, compounding direction.